system

Abstract

The system according to this embodiment aims to rapidly optimize the user experience. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, and a prediction unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The prediction unit predicts future user behavior 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 Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot 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 an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time to optimize the user experience and it is difficult to respond quickly.

[0005] The system according to the embodiment aims to quickly optimize the user experience.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a prediction unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts future user behavior based on the analysis result obtained by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can quickly optimize the user experience. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) An AI agent-driven A / B testing platform according to an embodiment of the present invention is an innovative solution for rapidly optimizing the user experience of websites and applications. This platform leverages an AI agent to automate the A / B testing process, supporting rapid and accurate design decision-making. The AI ​​agent-driven A / B testing platform can continuously run the planning, execution, verification, and improvement cycle at many times the speed of manual execution, enabling ultra-optimization of UI / UX by responding more precisely and timely to user needs. For example, the AI ​​agent optimizes the A / B test design. Specifically, the AI ​​agent analyzes user behavior data and generates optimal test patterns. For instance, it creates multiple versions with different designs and functions and tests how each version affects users. Next, the AI ​​agent automates data analysis. It collects test result data and analyzes it. For example, it determines which version is most effective based on metrics such as click-through rate and time spent on the site. Furthermore, the AI ​​agent performs predictive analytics. Based on past data, it predicts future user behavior. For example, it predicts the impact of a specific design change in the future and proposes the optimal design based on the results. In this way, an AI agent-driven A / B testing platform becomes a powerful tool for companies to respond quickly to user needs and maintain their competitiveness. For example, when adding new features, the AI ​​agent can suggest the optimal timing and method, improving user satisfaction. Furthermore, because the AI ​​agent-driven A / B testing platform can continuously run the planning, execution, verification, and improvement cycle, it can always respond to the latest user needs. For example, it is possible to quickly adjust designs and functions in response to seasonal or trend changes.This enables AI-powered agent-driven A / B testing platforms to hyper-optimize the user experience and maintain a competitive edge. For example, they can respond to market changes faster than competitors and meet user needs, thereby increasing customer satisfaction. This makes AI-powered agent-driven A / B testing platforms a powerful tool for businesses to quickly respond to user needs and maintain a competitive edge.

[0029] The AI ​​agent-driven A / B testing platform according to this embodiment comprises a data collection unit, an analysis unit, and a prediction unit. The data collection unit collects data. The data collection unit can collect user behavior data, such as website click data and app usage data. The data collection unit may include AI processing and can automate data collection using AI. For example, the data collection unit can use AI to collect user behavior data in real time and store it in a database. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis unit includes AI processing and can automate data analysis using AI. For example, the analysis unit can use AI to analyze the collected data and identify user behavior patterns. The prediction unit predicts future user behavior based on the analysis results obtained by the analysis unit. The prediction unit can use, for example, an algorithm that predicts future behavior based on past behavior data. The prediction unit includes AI processing and can predict future user behavior using AI. For example, the prediction unit can use AI to analyze past data and predict future user behavior. As a result, the AI ​​agent-driven A / B testing platform according to this embodiment can automate data collection, analysis, and prediction, supporting rapid and accurate decision-making.

[0030] The data collection unit collects data. For example, the data collection unit can collect user behavior data such as website click data and app usage data. Specifically, website click data includes detailed information such as which links users clicked, which pages they viewed, and how long they spent on each page. App usage data includes information such as which features users used within the app, which screens they viewed, and how long they spent within the app. The data collection unit may also include AI processing, and can use AI to automate data collection. For example, the data collection unit can use AI to collect user behavior data in real time and store it in a database. The AI ​​analyzes website and app log data to track user behavior in real time. This allows the data collection unit to collect user behavior data quickly and accurately. Furthermore, the data collection unit can flexibly set the frequency and scope of data collection. For example, data collection can be concentrated on specific time periods or specific user groups. This allows the data collection unit to gain a detailed understanding of user behavior under specific conditions. The data collection unit can also centrally manage the collected data and collaborate with other systems or departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the analysis and prediction units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, statistical analysis allows for the understanding of data distribution and trends, and the identification of user behavior patterns. Machine learning algorithms build models based on the collected data to predict user behavior. The analysis unit includes AI processing and can automate data analysis using AI. For example, the analysis unit can use AI to analyze collected data and identify user behavior patterns. AI extracts data features and groups user behavior through clustering and classification. This allows the analysis unit to quickly and accurately identify user behavior patterns. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and fluctuations. For example, it can analyze changes in behavior during specific periods or events based on past user behavior data to predict future trends. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The prediction unit predicts future user behavior based on the analysis results obtained by the analysis unit. The prediction unit can use algorithms that predict future behavior based on past behavioral data, for example. Specifically, it performs time series analysis and regression analysis based on past data to predict future behavior. The prediction unit includes AI processing and can use AI to predict future user behavior. For example, the prediction unit can use AI to analyze past data and predict future user behavior. The AI ​​learns patterns and trends from past data and predicts future behavior with high accuracy. This allows the prediction unit to predict future user behavior quickly and accurately. Furthermore, the prediction unit can continuously revise its prediction results based on data that is updated in real time, and respond to the latest situation. For example, if user behavior changes rapidly, the prediction unit immediately incorporates new data and updates the prediction results. In addition, the prediction unit can make more accurate predictions by considering regional characteristics and past behavioral history. This allows the prediction unit to always provide highly accurate predictions based on the latest information and support quick and appropriate responses.

[0033] The data collection unit can collect user behavior data. For example, the data collection unit can collect website click data. For example, the data collection unit can also collect app usage data. For example, the data collection unit can also collect user scrolling data. By collecting user behavior data, more accurate analysis and prediction become possible. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to collect user behavior data in real time and store it in a database.

[0034] The analysis unit can generate optimal test patterns based on the collected data. For example, the analysis unit can generate multiple versions with different designs and functions based on the collected data. For example, the analysis unit can analyze user behavior patterns based on the collected data and generate optimal test patterns. For example, the analysis unit can analyze user attribute information based on the collected data and generate optimal test patterns. By generating optimal test patterns, the effectiveness of the tests can be maximized. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze the collected data and identify user behavior patterns.

[0035] The prediction unit can predict future user behavior based on past data. For example, the prediction unit uses an algorithm that predicts future behavior based on past behavioral data. For example, the prediction unit can also predict future purchasing behavior based on past purchase history. For example, the prediction unit can also predict future browsing behavior based on past browsing history. This enables forward-looking decision-making by predicting future user behavior. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to analyze past data and predict future user behavior.

[0036] The AI ​​agent-driven A / B testing platform according to this embodiment includes a test design unit. The test design unit can design optimal test patterns. For example, the test design unit designs different test patterns depending on the purpose of the test. For example, the test design unit can also design test patterns based on the characteristics of the target user. For example, the test design unit can also design test patterns depending on the duration and frequency of the test. By designing the optimal test pattern, the effectiveness of the test can be maximized. Some or all of the above-described processes in the test design unit may be performed using AI or not. For example, the test design unit can use AI to analyze user behavior data and design the optimal test pattern.

[0037] The AI ​​agent-driven A / B testing platform according to this embodiment includes a design proposal unit. The design proposal unit can propose an optimal design. For example, the design proposal unit can propose a design aimed at improving the user experience. For example, the design proposal unit can also propose a design that enhances visual appeal. For example, the design proposal unit can also propose a design that improves user operability. In this way, the user experience can be improved by proposing an optimal design. Some or all of the above-described processes in the design proposal unit may be performed using AI or not. For example, the design proposal unit can use AI to analyze user behavior data and propose an optimal design.

[0038] The AI ​​agent-driven A / B testing platform according to this embodiment includes a results provision unit. The results provision unit can provide analysis results. The results provision unit can provide analysis results in the form of graphs or charts, for example. The results provision unit can also provide analysis results in the form of reports, for example. The results provision unit can also provide analysis results in the form of dashboards, for example. This allows for rapid decision-making by providing analysis results. Some or all of the above-described processing in the results provision unit may be performed using AI or not. For example, the results provision unit can automatically generate analysis results using AI and provide them to the user.

[0039] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can concentrate data collection during times when the user frequently accessed the system in the past. The data collection unit can also select the optimal data collection method based on the devices the user has used in the past. For example, the data collection unit can analyze the user's past behavioral patterns and collect data in accordance with specific events. This allows the optimal data collection method to be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's past behavioral data and select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current context and areas of interest during data collection. For example, the data collection unit can collect only relevant data based on the content of the page the user is currently viewing. The data collection unit can also limit the scope of data collection based on topics the user has shown interest in. For example, the data collection unit can prioritize the collection of location-related data based on the user's current location. This allows for the collection of highly relevant data by filtering data based on the user's current context and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's current context and areas of interest and filter the relevant data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's geographical location information and prioritize the collection of relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on content shared by the user on social media. The data collection unit can also collect relevant data based on the activity of accounts followed by the user. The data collection unit can also collect relevant data based on groups and events in which the user participates. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze a user's social media activity and collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a behavioral analysis algorithm to user behavior data. For example, the analysis unit can also apply an emotion analysis algorithm to user feedback data. For example, the analysis unit can also apply a purchase pattern analysis algorithm to user purchase data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to classify data categories and apply an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the most recent data while referring to past data. The analysis unit may also prioritize the analysis of data collected during a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the data collection timing and determine the priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may also analyze data with low relevance last. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to evaluate the relevance of the data and adjust the order of analysis.

[0047] The prediction unit can optimize its prediction algorithm by referring to historical data during the prediction process. For example, the prediction unit can optimize its prediction algorithm based on historical user behavior data. The prediction unit can also optimize its prediction algorithm based on historical test result data. The prediction unit can also optimize its prediction algorithm based on historical market data. By optimizing the prediction algorithm by referring to historical data, more accurate predictions become possible. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to analyze historical data and optimize its prediction algorithm.

[0048] The prediction unit can apply different prediction methods to each data category during prediction. For example, the prediction unit can apply a behavior prediction method to user behavior data. For example, the prediction unit can also apply an emotion prediction method to user feedback data. For example, the prediction unit can also apply a purchase prediction method to user purchase data. By applying different prediction methods to each data category, more accurate predictions become possible. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to classify data categories and apply an appropriate prediction method.

[0049] The prediction unit can analyze changes in predictions based on the data collection timing during the prediction process. For example, the prediction unit can analyze changes in predictions based on the latest data. For example, the prediction unit can analyze changes in predictions by prioritizing the latest data while also referring to past data. For example, the prediction unit can analyze changes in predictions based on data collected during a specific period. This allows the unit to provide up-to-date prediction results by analyzing changes in predictions based on the data collection timing. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to analyze the data collection timing and analyze changes in predictions.

[0050] The forecasting unit can analyze its forecasts by referring to relevant market data during the forecasting process. For example, the forecasting unit can analyze its forecasts based on trend data of the relevant market. For example, the forecasting unit can also analyze its forecasts based on competitive data of the relevant market. For example, the forecasting unit can also analyze its forecasts based on consumer data of the relevant market. By analyzing forecasts by referring to relevant market data, more accurate forecasts become possible. Some or all of the above processing in the forecasting unit may be performed using AI or not. For example, the forecasting unit can use AI to analyze relevant market data and then analyze its forecasts.

[0051] The test design department can generate optimal test patterns by referring to past test results during the test design process. For example, the test design department can generate new test patterns based on past successful test patterns. For example, the test design department can also generate optimal test patterns to avoid past failed test patterns. For example, the test design department can analyze past test results and generate the most effective test patterns. This maximizes the effectiveness of testing by generating optimal test patterns by referring to past test results. Some or all of the above processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze past test results and generate optimal test patterns.

[0052] The test design department can customize test patterns based on the user's current behavior data during the test design process. For example, the test design department can customize test patterns based on the page the user is currently viewing. The test design department can also generate optimal test patterns based on the user's current behavior patterns. The test design department can also customize test patterns based on the user's current interests and preferences. By customizing test patterns based on the user's current behavior data, more effective testing becomes possible. Some or all of the above processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze the user's current behavior data and generate optimal test patterns.

[0053] The test design department can generate optimal test patterns by considering the user's geographical location information during the test design process. For example, if the user is in a specific region, the test design department can generate test patterns related to that region. For example, if the user is traveling, the test design department can also generate test patterns related to the travel destination. For example, if the user is at home, the test design department can generate test patterns based on data around the user's home. This allows for the generation of highly relevant test patterns by considering the user's geographical location information. Some or all of the above-described processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze the user's geographical location information and generate optimal test patterns.

[0054] The test design department can analyze users' social media activity and propose test patterns during the test design process. For example, the test design department can propose relevant test patterns based on content shared by users on social media. For example, the test design department can also propose relevant test patterns based on the activity of accounts that users follow. For example, the test design department can propose relevant test patterns based on groups and events that users participate in. In this way, relevant test patterns can be proposed by analyzing users' social media activity. Some or all of the above processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze users' social media activity and propose relevant test patterns.

[0055] The design proposal department can propose the optimal design by referring to past design data when making design proposals. For example, the design proposal department can propose a new design based on past successful designs. For example, the design proposal department can propose the optimal design to avoid past unsuccessful designs. For example, the design proposal department can analyze past design data and propose the most effective design. In this way, the effectiveness of the design can be maximized by proposing the optimal design by referring to past design data. Some or all of the above processes in the design proposal department may be performed using AI or not. For example, the design proposal department can use AI to analyze past design data and propose the optimal design.

[0056] The design proposal department can customize designs based on the user's current behavioral data when proposing designs. For example, the design proposal department can customize designs based on the page the user is currently viewing. For example, the design proposal department can also propose the optimal design based on the user's current behavioral patterns. For example, the design proposal department can customize designs based on the user's current interests and preferences. By customizing designs based on the user's current behavioral data, more effective design proposals become possible. Some or all of the above processes in the design proposal department may be performed using AI or not. For example, the design proposal department can use AI to analyze the user's current behavioral data and propose the optimal design.

[0057] The design proposal department can propose the most suitable design by considering the user's geographical location information. For example, if the user is in a specific region, the design proposal department can propose a design related to that region. For example, if the user is traveling, the design proposal department can propose a design related to the travel destination. For example, if the user is at home, the design proposal department can propose a design based on data about the area around the user's home. In this way, by considering the user's geographical location information, it is possible to propose a highly relevant design. Some or all of the above processing in the design proposal department may be performed using AI or not. For example, the design proposal department can use AI to analyze the user's geographical location information and propose the most suitable design.

[0058] The design proposal department can analyze a user's social media activity and propose designs when creating a design proposal. For example, the design proposal department can propose relevant designs based on content shared by the user on social media. For example, the design proposal department can also propose relevant designs based on the activity of accounts followed by the user. For example, the design proposal department can propose relevant designs based on groups and events the user participates in. In this way, relevant designs can be proposed by analyzing the user's social media activity. Some or all of the above processes in the design proposal department may be performed using AI or not. For example, the design proposal department can use AI to analyze a user's social media activity and propose relevant designs.

[0059] The results delivery unit can select the optimal results delivery method by referring to past results data when delivering results. For example, the results delivery unit can select a new results delivery method based on past successful results delivery methods. For example, the results delivery unit can also select the optimal results delivery method to avoid past unsuccessful results delivery methods. For example, the results delivery unit can analyze past results data and select the most effective results delivery method. This maximizes the effectiveness of results delivery by selecting the optimal results delivery method by referring to past results data. Some or all of the above processes in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze past results data and select the optimal results delivery method.

[0060] The results delivery unit can customize the results based on the user's current behavioral data when providing the results. For example, the results delivery unit can customize the results based on the page the user is currently viewing. For example, the results delivery unit can also provide the optimal results based on the user's current behavioral patterns. For example, the results delivery unit can also customize the results based on the user's current interests and preferences. By customizing the results based on the user's current behavioral data, it becomes possible to provide more effective results. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze the user's current behavioral data and provide the optimal results.

[0061] The results delivery unit can select the optimal results delivery method by considering the user's geographical location information when providing results. For example, if the user is in a specific region, the results delivery unit can provide results relevant to that region. For example, if the user is traveling, the results delivery unit can also provide results relevant to the travel destination. For example, if the user is at home, the results delivery unit can provide results based on data around the user's home. This allows for the provision of highly relevant results by considering the user's geographical location information. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze the user's geographical location information and select the optimal results delivery method.

[0062] The results delivery unit can analyze the user's social media activity and propose means of providing results when delivering results. For example, the results delivery unit can provide relevant results based on the content the user has shared on social media. The results delivery unit can also provide relevant results based on the activity of accounts the user follows. The results delivery unit can also provide relevant results based on groups and events the user participates in. In this way, relevant results can be provided by analyzing the user's social media activity. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze the user's social media activity and provide relevant results.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, it can concentrate data collection during times when the user frequently accessed the system in the past. It can also select the optimal data collection method based on the devices the user has used in the past. It can also analyze the user's past behavioral patterns and collect data in accordance with specific events. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's past behavioral data and select the optimal data collection method.

[0065] The data collection unit can filter data based on the user's current context and areas of interest during data collection. For example, it can collect only relevant data based on the content of the page the user is currently viewing. It can also limit the scope of data collection based on topics the user has shown interest in. It can also prioritize the collection of region-related data based on the user's current location. This allows for the collection of highly relevant data by filtering data based on the user's current context and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's current context and areas of interest and filter the relevant data.

[0066] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0067] The prediction unit can optimize its prediction algorithm by referring to historical data during the prediction process. For example, it can optimize the prediction algorithm based on past user behavior data. It can also optimize the prediction algorithm based on past test result data. It can also optimize the prediction algorithm based on past market data. By optimizing the prediction algorithm by referring to historical data, more accurate predictions become possible. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to analyze historical data and optimize the prediction algorithm.

[0068] The test design department can customize test patterns based on the user's current behavior data during the test design process. For example, it can customize test patterns based on the page the user is currently viewing. It can also generate optimal test patterns based on the user's current behavior patterns. Furthermore, it can customize test patterns based on the user's current interests and concerns. This allows for more effective testing by customizing test patterns based on the user's current behavior data. Some or all of the above processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze the user's current behavior data and generate optimal test patterns.

[0069] The results delivery unit can customize the results based on the user's current behavioral data when providing the results. For example, it can customize the results based on the page the user is currently viewing. It can also provide the optimal results based on the user's current behavioral patterns. It can also customize the results based on the user's current interests and preferences. By customizing the results based on the user's current behavioral data, it becomes possible to provide more effective results. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze the user's current behavioral data and provide the optimal results.

[0070] The following briefly describes the processing flow for example form 1.

[0071] Step 1: The collection unit collects data. The collection unit can collect user behavior data, such as website click data or app usage data. The collection unit may also include AI processing, and can use AI to automate data collection. For example, the collection unit can use AI to collect user behavior data in real time and store it in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can include AI processing and automate data analysis using AI. For example, the analysis unit can use AI to analyze the collected data and identify user behavior patterns. Step 3: The prediction unit predicts future user behavior based on the analysis results obtained by the analysis unit. The prediction unit can use, for example, an algorithm that predicts future behavior based on past behavioral data. The prediction unit includes AI processing and can use AI to predict future user behavior. For example, the prediction unit can use AI to analyze past data and predict future user behavior.

[0072] (Example of form 2) An AI agent-driven A / B testing platform according to an embodiment of the present invention is an innovative solution for rapidly optimizing the user experience of websites and applications. This platform leverages an AI agent to automate the A / B testing process, supporting rapid and accurate design decision-making. The AI ​​agent-driven A / B testing platform can continuously run the planning, execution, verification, and improvement cycle at many times the speed of manual execution, enabling ultra-optimization of UI / UX by responding more precisely and timely to user needs. For example, the AI ​​agent optimizes the A / B test design. Specifically, the AI ​​agent analyzes user behavior data and generates optimal test patterns. For instance, it creates multiple versions with different designs and functions and tests how each version affects users. Next, the AI ​​agent automates data analysis. It collects test result data and analyzes it. For example, it determines which version is most effective based on metrics such as click-through rate and time spent on the site. Furthermore, the AI ​​agent performs predictive analytics. Based on past data, it predicts future user behavior. For example, it predicts the impact of a specific design change in the future and proposes the optimal design based on the results. In this way, an AI agent-driven A / B testing platform becomes a powerful tool for companies to respond quickly to user needs and maintain their competitiveness. For example, when adding new features, the AI ​​agent can suggest the optimal timing and method, improving user satisfaction. Furthermore, because the AI ​​agent-driven A / B testing platform can continuously run the planning, execution, verification, and improvement cycle, it can always respond to the latest user needs. For example, it is possible to quickly adjust designs and functions in response to seasonal or trend changes.This enables AI-powered agent-driven A / B testing platforms to hyper-optimize the user experience and maintain a competitive edge. For example, they can respond to market changes faster than competitors and meet user needs, thereby increasing customer satisfaction. This makes AI-powered agent-driven A / B testing platforms a powerful tool for businesses to quickly respond to user needs and maintain a competitive edge.

[0073] The AI ​​agent-driven A / B testing platform according to this embodiment comprises a data collection unit, an analysis unit, and a prediction unit. The data collection unit collects data. The data collection unit can collect user behavior data, such as website click data and app usage data. The data collection unit may include AI processing and can automate data collection using AI. For example, the data collection unit can use AI to collect user behavior data in real time and store it in a database. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis unit includes AI processing and can automate data analysis using AI. For example, the analysis unit can use AI to analyze the collected data and identify user behavior patterns. The prediction unit predicts future user behavior based on the analysis results obtained by the analysis unit. The prediction unit can use, for example, an algorithm that predicts future behavior based on past behavior data. The prediction unit includes AI processing and can predict future user behavior using AI. For example, the prediction unit can use AI to analyze past data and predict future user behavior. As a result, the AI ​​agent-driven A / B testing platform according to this embodiment can automate data collection, analysis, and prediction, supporting rapid and accurate decision-making.

[0074] The data collection unit collects data. For example, the data collection unit can collect user behavior data such as website click data and app usage data. Specifically, website click data includes detailed information such as which links users clicked, which pages they viewed, and how long they spent on each page. App usage data includes information such as which features users used within the app, which screens they viewed, and how long they spent within the app. The data collection unit may also include AI processing, and can use AI to automate data collection. For example, the data collection unit can use AI to collect user behavior data in real time and store it in a database. The AI ​​analyzes website and app log data to track user behavior in real time. This allows the data collection unit to collect user behavior data quickly and accurately. Furthermore, the data collection unit can flexibly set the frequency and scope of data collection. For example, data collection can be concentrated on specific time periods or specific user groups. This allows the data collection unit to gain a detailed understanding of user behavior under specific conditions. The data collection unit can also centrally manage the collected data and collaborate with other systems or departments as needed. For example, the collected data is stored on a cloud server, making it accessible to the analysis and prediction units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0075] The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. Specifically, statistical analysis allows for the understanding of data distribution and trends, and the identification of user behavior patterns. Machine learning algorithms build models based on the collected data to predict user behavior. The analysis unit includes AI processing and can automate data analysis using AI. For example, the analysis unit can use AI to analyze collected data and identify user behavior patterns. AI extracts data features and groups user behavior through clustering and classification. This allows the analysis unit to quickly and accurately identify user behavior patterns. Furthermore, the analysis unit can utilize historical data and statistical information to analyze long-term trends and fluctuations. For example, it can analyze changes in behavior during specific periods or events based on past user behavior data to predict future trends. Additionally, the analysis unit can use anomaly detection algorithms to detect unusual patterns or abnormal data and issue early warnings. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0076] The prediction unit predicts future user behavior based on the analysis results obtained by the analysis unit. The prediction unit can use algorithms that predict future behavior based on past behavioral data, for example. Specifically, it performs time series analysis and regression analysis based on past data to predict future behavior. The prediction unit includes AI processing and can use AI to predict future user behavior. For example, the prediction unit can use AI to analyze past data and predict future user behavior. The AI ​​learns patterns and trends from past data and predicts future behavior with high accuracy. This allows the prediction unit to predict future user behavior quickly and accurately. Furthermore, the prediction unit can continuously revise its prediction results based on data that is updated in real time, and respond to the latest situation. For example, if user behavior changes rapidly, the prediction unit immediately incorporates new data and updates the prediction results. In addition, the prediction unit can make more accurate predictions by considering regional characteristics and past behavioral history. This allows the prediction unit to always provide highly accurate predictions based on the latest information and support quick and appropriate responses.

[0077] The data collection unit can collect user behavior data. For example, the data collection unit can collect website click data. For example, the data collection unit can also collect app usage data. For example, the data collection unit can also collect user scrolling data. By collecting user behavior data, more accurate analysis and prediction become possible. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to collect user behavior data in real time and store it in a database.

[0078] The analysis unit can generate optimal test patterns based on the collected data. For example, the analysis unit can generate multiple versions with different designs and functions based on the collected data. For example, the analysis unit can analyze user behavior patterns based on the collected data and generate optimal test patterns. For example, the analysis unit can analyze user attribute information based on the collected data and generate optimal test patterns. By generating optimal test patterns, the effectiveness of the tests can be maximized. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to analyze the collected data and identify user behavior patterns.

[0079] The prediction unit can predict future user behavior based on past data. For example, the prediction unit uses an algorithm that predicts future behavior based on past behavioral data. For example, the prediction unit can also predict future purchasing behavior based on past purchase history. For example, the prediction unit can also predict future browsing behavior based on past browsing history. This enables forward-looking decision-making by predicting future user behavior. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to analyze past data and predict future user behavior.

[0080] The AI ​​agent-driven A / B testing platform according to this embodiment includes a test design unit. The test design unit can design optimal test patterns. For example, the test design unit designs different test patterns depending on the purpose of the test. For example, the test design unit can also design test patterns based on the characteristics of the target user. For example, the test design unit can also design test patterns depending on the duration and frequency of the test. By designing the optimal test pattern, the effectiveness of the test can be maximized. Some or all of the above-described processes in the test design unit may be performed using AI or not. For example, the test design unit can use AI to analyze user behavior data and design the optimal test pattern.

[0081] The AI ​​agent-driven A / B testing platform according to this embodiment includes a design proposal unit. The design proposal unit can propose an optimal design. For example, the design proposal unit can propose a design aimed at improving the user experience. For example, the design proposal unit can also propose a design that enhances visual appeal. For example, the design proposal unit can also propose a design that improves user operability. In this way, the user experience can be improved by proposing an optimal design. Some or all of the above-described processes in the design proposal unit may be performed using AI or not. For example, the design proposal unit can use AI to analyze user behavior data and propose an optimal design.

[0082] The AI ​​agent-driven A / B testing platform according to this embodiment includes a results provision unit. The results provision unit can provide analysis results. The results provision unit can provide analysis results in the form of graphs or charts, for example. The results provision unit can also provide analysis results in the form of reports, for example. The results provision unit can also provide analysis results in the form of dashboards, for example. This allows for rapid decision-making by providing analysis results. Some or all of the above-described processing in the results provision unit may be performed using AI or not. For example, the results provision unit can automatically generate analysis results using AI and provide them to the user.

[0083] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can temporarily stop data collection and resume it after the user has calmed down. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, the data collection unit can concentrate data collection during times when the user frequently accessed the system in the past. The data collection unit can also select the optimal data collection method based on the devices the user has used in the past. For example, the data collection unit can analyze the user's past behavioral patterns and collect data in accordance with specific events. This allows the optimal data collection method to be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's past behavioral data and select the optimal data collection method.

[0085] The data collection unit can filter data based on the user's current context and areas of interest during data collection. For example, the data collection unit can collect only relevant data based on the content of the page the user is currently viewing. The data collection unit can also limit the scope of data collection based on topics the user has shown interest in. For example, the data collection unit can prioritize the collection of location-related data based on the user's current location. This allows for the collection of highly relevant data by filtering data based on the user's current context and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's current context and areas of interest and filter the relevant data.

[0086] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting real-time data. If the user is relaxed, the data collection unit may also prioritize collecting historical data. If the user is stressed, the data collection unit may postpone the collection of less important data. This allows for the priority collection of important data by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0087] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is traveling, the data collection unit can prioritize the collection of data related to the travel destination. For example, if the user is at home, the data collection unit can prioritize the collection of data around the user's home. In this way, by considering the user's geographical location information, highly relevant data can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's geographical location information and prioritize the collection of relevant data.

[0088] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant data based on content shared by the user on social media. The data collection unit can also collect relevant data based on the activity of accounts followed by the user. The data collection unit can also collect relevant data based on groups and events in which the user participates. This allows for the collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze a user's social media activity and collect relevant data.

[0089] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit can also provide detailed analysis results. For example, if the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. By adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0090] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with an appropriate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0091] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a behavioral analysis algorithm to user behavior data. For example, the analysis unit can also apply an emotion analysis algorithm to user feedback data. For example, the analysis unit can also apply a purchase pattern analysis algorithm to user purchase data. By applying different analysis algorithms depending on the data category, more accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to classify data categories and apply an appropriate analysis algorithm.

[0092] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0093] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis. For example, the analysis unit may prioritize the analysis of the most recent data. The analysis unit may also prioritize the most recent data while referring to past data. The analysis unit may also prioritize the analysis of data collected during a specific period. This allows for the prioritization of the analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to analyze the data collection timing and determine the priority.

[0094] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may also analyze data with moderate relevance next. For example, the analysis unit may also analyze data with low relevance last. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit may use AI to evaluate the relevance of the data and adjust the order of analysis.

[0095] The prediction unit can estimate the user's emotions and adjust the display method of the prediction based on the estimated user emotions. For example, if the user is nervous, the prediction unit can provide a simple and easy-to-read prediction result. For example, if the user is relaxed, the prediction unit can also provide a detailed prediction result. For example, if the user is in a hurry, the prediction unit can also provide a concise prediction result that gets straight to the point. In this way, by adjusting the display method of the prediction according to the user's emotions, the prediction result can be provided that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not using AI. For example, the prediction unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0096] The prediction unit can optimize its prediction algorithm by referring to historical data during the prediction process. For example, the prediction unit can optimize its prediction algorithm based on historical user behavior data. The prediction unit can also optimize its prediction algorithm based on historical test result data. The prediction unit can also optimize its prediction algorithm based on historical market data. By optimizing the prediction algorithm by referring to historical data, more accurate predictions become possible. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to analyze historical data and optimize its prediction algorithm.

[0097] The prediction unit can apply different prediction methods to each data category during prediction. For example, the prediction unit can apply a behavior prediction method to user behavior data. For example, the prediction unit can also apply an emotion prediction method to user feedback data. For example, the prediction unit can also apply a purchase prediction method to user purchase data. By applying different prediction methods to each data category, more accurate predictions become possible. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to classify data categories and apply an appropriate prediction method.

[0098] The prediction unit can estimate the user's emotions and adjust the importance of predictions based on the estimated emotions. For example, if the user is excited, the prediction unit will prioritize displaying high-importance prediction results. If the user is relaxed, the prediction unit may also display low-importance prediction results. If the user is stressed, the prediction unit may postpone displaying low-importance prediction results. This allows for prioritizing important prediction results by adjusting their importance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input user image data captured by a camera into the generative AI and have the generative AI perform the user's emotion estimation.

[0099] The prediction unit can analyze changes in predictions based on the data collection timing during the prediction process. For example, the prediction unit can analyze changes in predictions based on the latest data. For example, the prediction unit can analyze changes in predictions by prioritizing the latest data while also referring to past data. For example, the prediction unit can analyze changes in predictions based on data collected during a specific period. This allows the unit to provide up-to-date prediction results by analyzing changes in predictions based on the data collection timing. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to analyze the data collection timing and analyze changes in predictions.

[0100] The forecasting unit can analyze its forecasts by referring to relevant market data during the forecasting process. For example, the forecasting unit can analyze its forecasts based on trend data of the relevant market. For example, the forecasting unit can also analyze its forecasts based on competitive data of the relevant market. For example, the forecasting unit can also analyze its forecasts based on consumer data of the relevant market. By analyzing forecasts by referring to relevant market data, more accurate forecasts become possible. Some or all of the above processing in the forecasting unit may be performed using AI or not. For example, the forecasting unit can use AI to analyze relevant market data and then analyze its forecasts.

[0101] The test design department can estimate the user's emotions and adjust the test design method based on the estimated user emotions. For example, if the user is relaxed, the test design department can create a detailed test design. For example, if the user is in a hurry, the test design department can create a concise test design. For example, if the user is excited, the test design department can create a visually stimulating test design. By adjusting the test design method according to the user's emotions, it becomes possible to create a test design that is optimal for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the test design department may be performed using AI or not. For example, the test design department can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0102] The test design department can generate optimal test patterns by referring to past test results during the test design process. For example, the test design department can generate new test patterns based on past successful test patterns. For example, the test design department can also generate optimal test patterns to avoid past failed test patterns. For example, the test design department can analyze past test results and generate the most effective test patterns. This maximizes the effectiveness of testing by generating optimal test patterns by referring to past test results. Some or all of the above processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze past test results and generate optimal test patterns.

[0103] The test design department can customize test patterns based on the user's current behavior data during the test design process. For example, the test design department can customize test patterns based on the page the user is currently viewing. The test design department can also generate optimal test patterns based on the user's current behavior patterns. The test design department can also customize test patterns based on the user's current interests and preferences. By customizing test patterns based on the user's current behavior data, more effective testing becomes possible. Some or all of the above processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze the user's current behavior data and generate optimal test patterns.

[0104] The test design department can estimate the user's emotions and determine the priority of test design based on the estimated user emotions. For example, if the user is excited, the test design department will prioritize designing high-priority tests. If the user is relaxed, for example, the test design department may also design tests that are of lower importance. If the user is stressed, for example, the test design department may postpone tests that are of lower importance. In this way, by determining the priority of test design according to the user's emotions, important tests can be executed first. Emotion estimation is achieved using emotion estimation functions, such as using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the test design department may be performed using AI or not. For example, the test design department can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0105] The test design department can generate optimal test patterns by considering the user's geographical location information during the test design process. For example, if the user is in a specific region, the test design department can generate test patterns related to that region. For example, if the user is traveling, the test design department can also generate test patterns related to the travel destination. For example, if the user is at home, the test design department can generate test patterns based on data around the user's home. This allows for the generation of highly relevant test patterns by considering the user's geographical location information. Some or all of the above-described processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze the user's geographical location information and generate optimal test patterns.

[0106] The test design department can analyze users' social media activity and propose test patterns during the test design process. For example, the test design department can propose relevant test patterns based on content shared by users on social media. For example, the test design department can also propose relevant test patterns based on the activity of accounts that users follow. For example, the test design department can propose relevant test patterns based on groups and events that users participate in. In this way, relevant test patterns can be proposed by analyzing users' social media activity. Some or all of the above processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze users' social media activity and propose relevant test patterns.

[0107] The design proposal unit can estimate the user's emotions and adjust the design proposal method based on the estimated emotions. For example, if the user is relaxed, the design proposal unit can provide a detailed design proposal. If the user is in a hurry, for example, the design proposal unit can provide a concise design proposal. If the user is excited, for example, the design proposal unit can provide a visually stimulating design proposal. By adjusting the design proposal method according to the user's emotions, it becomes possible to provide the optimal design proposal for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design proposal unit may be performed using AI or not. For example, the design proposal unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0108] The design proposal department can propose the optimal design by referring to past design data when making design proposals. For example, the design proposal department can propose a new design based on past successful designs. For example, the design proposal department can propose the optimal design to avoid past unsuccessful designs. For example, the design proposal department can analyze past design data and propose the most effective design. In this way, the effectiveness of the design can be maximized by proposing the optimal design by referring to past design data. Some or all of the above processes in the design proposal department may be performed using AI or not. For example, the design proposal department can use AI to analyze past design data and propose the optimal design.

[0109] The design proposal department can customize designs based on the user's current behavioral data when proposing designs. For example, the design proposal department can customize designs based on the page the user is currently viewing. For example, the design proposal department can also propose the optimal design based on the user's current behavioral patterns. For example, the design proposal department can customize designs based on the user's current interests and preferences. By customizing designs based on the user's current behavioral data, more effective design proposals become possible. Some or all of the above processes in the design proposal department may be performed using AI or not. For example, the design proposal department can use AI to analyze the user's current behavioral data and propose the optimal design.

[0110] The design proposal department can estimate the user's emotions and prioritize design proposals based on those emotions. For example, if the user is excited, the design proposal department will prioritize proposing high-priority designs. If the user is relaxed, the design proposal department may also propose designs of lower importance. If the user is stressed, the design proposal department may postpone proposing designs of lower importance. This allows for prioritizing important designs by determining the priority of design proposals according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design proposal department may be performed using AI or not. For example, the design proposal department can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0111] The design proposal department can propose the most suitable design by considering the user's geographical location information. For example, if the user is in a specific region, the design proposal department can propose a design related to that region. For example, if the user is traveling, the design proposal department can propose a design related to the travel destination. For example, if the user is at home, the design proposal department can propose a design based on data about the area around the user's home. In this way, by considering the user's geographical location information, it is possible to propose a highly relevant design. Some or all of the above processing in the design proposal department may be performed using AI or not. For example, the design proposal department can use AI to analyze the user's geographical location information and propose the most suitable design.

[0112] The design proposal department can analyze a user's social media activity and propose designs when creating a design proposal. For example, the design proposal department can propose relevant designs based on content shared by the user on social media. For example, the design proposal department can also propose relevant designs based on the activity of accounts followed by the user. For example, the design proposal department can propose relevant designs based on groups and events the user participates in. In this way, relevant designs can be proposed by analyzing the user's social media activity. Some or all of the above processes in the design proposal department may be performed using AI or not. For example, the design proposal department can use AI to analyze a user's social media activity and propose relevant designs.

[0113] The results delivery unit can estimate the user's emotions and adjust the method of delivering results based on the estimated emotions. For example, if the user is nervous, the results delivery unit can provide simple and easy-to-understand results. For example, if the user is relaxed, the results delivery unit can also provide detailed results. For example, if the user is in a hurry, the results delivery unit can provide concise results that get straight to the point. By adjusting the method of delivering results according to the user's emotions, it becomes possible to deliver the most optimal results for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the results delivery unit may be performed using AI or not using AI. For example, the results delivery unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0114] The results delivery unit can select the optimal results delivery method by referring to past results data when delivering results. For example, the results delivery unit can select a new results delivery method based on past successful results delivery methods. For example, the results delivery unit can also select the optimal results delivery method to avoid past unsuccessful results delivery methods. For example, the results delivery unit can analyze past results data and select the most effective results delivery method. This maximizes the effectiveness of results delivery by selecting the optimal results delivery method by referring to past results data. Some or all of the above processes in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze past results data and select the optimal results delivery method.

[0115] The results delivery unit can customize the results based on the user's current behavioral data when providing the results. For example, the results delivery unit can customize the results based on the page the user is currently viewing. For example, the results delivery unit can also provide the optimal results based on the user's current behavioral patterns. For example, the results delivery unit can also customize the results based on the user's current interests and preferences. By customizing the results based on the user's current behavioral data, it becomes possible to provide more effective results. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze the user's current behavioral data and provide the optimal results.

[0116] The results delivery unit can estimate the user's emotions and determine the priority of results based on the estimated emotions. For example, if the user is excited, the results delivery unit will prioritize providing high-importance results. If the user is relaxed, the results delivery unit may also provide less important results. If the user is stressed, the results delivery unit may postpone providing less important results. This allows for the prioritization of important results by determining the priority of results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0117] The results delivery unit can select the optimal results delivery method by considering the user's geographical location information when providing results. For example, if the user is in a specific region, the results delivery unit can provide results relevant to that region. For example, if the user is traveling, the results delivery unit can also provide results relevant to the travel destination. For example, if the user is at home, the results delivery unit can provide results based on data around the user's home. This allows for the provision of highly relevant results by considering the user's geographical location information. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze the user's geographical location information and select the optimal results delivery method.

[0118] The results delivery unit can analyze the user's social media activity and propose means of providing results when delivering results. For example, the results delivery unit can provide relevant results based on the content the user has shared on social media. The results delivery unit can also provide relevant results based on the activity of accounts the user follows. The results delivery unit can also provide relevant results based on groups and events the user participates in. In this way, relevant results can be provided by analyzing the user's social media activity. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze the user's social media activity and provide relevant results.

[0119] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0120] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and easy-to-understand analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is in a hurry, the analysis unit can also provide a concise analysis result that gets straight to the point. In this way, by adjusting the analysis method according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0121] The data collection unit can analyze the user's past behavioral data and select the optimal data collection method. For example, it can concentrate data collection during times when the user frequently accessed the system in the past. It can also select the optimal data collection method based on the devices the user has used in the past. It can also analyze the user's past behavioral patterns and collect data in accordance with specific events. In this way, the optimal data collection method can be selected by analyzing the user's past behavioral data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's past behavioral data and select the optimal data collection method.

[0122] The prediction unit can estimate the user's emotions and adjust how the prediction is displayed based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and easy-to-read prediction result. If the user is relaxed, the prediction unit can also provide a detailed prediction result. If the user is in a hurry, the prediction unit can also provide a concise prediction result that gets straight to the point. By adjusting how the prediction is displayed according to the user's emotions, the prediction results can be made easier for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using AI or not. For example, the prediction unit can input user image data captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0123] The test design department can estimate the user's emotions and adjust the test design method based on the estimated user emotions. For example, if the user is relaxed, the test design department can create a detailed test design. If the user is in a hurry, the test design department can create a concise test design. If the user is excited, the test design department can create a visually stimulating test design. By adjusting the test design method according to the user's emotions, it becomes possible to create a test design that is optimal for the user. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the test design department may be performed using AI or not. For example, the test design department can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0124] The design proposal unit can estimate the user's emotions and adjust the design proposal method based on the estimated emotions. For example, if the user is relaxed, the design proposal unit can provide a detailed design proposal. If the user is in a hurry, the design proposal unit can provide a concise design proposal. If the user is excited, the design proposal unit can provide a visually stimulating design proposal. By adjusting the design proposal method according to the user's emotions, it becomes possible to provide the optimal design proposal for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the design proposal unit may be performed using AI or not. For example, the design proposal unit can input user image data captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0125] The data collection unit can filter data based on the user's current context and areas of interest during data collection. For example, it can collect only relevant data based on the content of the page the user is currently viewing. It can also limit the scope of data collection based on topics the user has shown interest in. It can also prioritize the collection of region-related data based on the user's current location. This allows for the collection of highly relevant data by filtering data based on the user's current context and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can use AI to analyze the user's current context and areas of interest and filter the relevant data.

[0126] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data, a simplified analysis on less important data, and an analysis with an appropriate level of detail on moderately important data. By adjusting the level of detail of the analysis based on the importance of the data, efficient analysis becomes possible. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can use AI to evaluate the importance of the data and adjust the level of detail of the analysis.

[0127] The prediction unit can optimize its prediction algorithm by referring to historical data during the prediction process. For example, it can optimize the prediction algorithm based on past user behavior data. It can also optimize the prediction algorithm based on past test result data. It can also optimize the prediction algorithm based on past market data. By optimizing the prediction algorithm by referring to historical data, more accurate predictions become possible. Some or all of the above-described processes in the prediction unit may be performed using AI or not. For example, the prediction unit can use AI to analyze historical data and optimize the prediction algorithm.

[0128] The test design department can customize test patterns based on the user's current behavior data during the test design process. For example, it can customize test patterns based on the page the user is currently viewing. It can also generate optimal test patterns based on the user's current behavior patterns. Furthermore, it can customize test patterns based on the user's current interests and concerns. This allows for more effective testing by customizing test patterns based on the user's current behavior data. Some or all of the above processes in the test design department may be performed using AI or not. For example, the test design department can use AI to analyze the user's current behavior data and generate optimal test patterns.

[0129] The results delivery unit can customize the results based on the user's current behavioral data when providing the results. For example, it can customize the results based on the page the user is currently viewing. It can also provide the optimal results based on the user's current behavioral patterns. It can also customize the results based on the user's current interests and preferences. By customizing the results based on the user's current behavioral data, it becomes possible to provide more effective results. Some or all of the above processing in the results delivery unit may be performed using AI or not. For example, the results delivery unit can use AI to analyze the user's current behavioral data and provide the optimal results.

[0130] The following briefly describes the processing flow for example form 2.

[0131] Step 1: The collection unit collects data. The collection unit can collect user behavior data, such as website click data or app usage data. The collection unit may also include AI processing, and can use AI to automate data collection. For example, the collection unit can use AI to collect user behavior data in real time and store it in a database. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can include AI processing and automate data analysis using AI. For example, the analysis unit can use AI to analyze the collected data and identify user behavior patterns. Step 3: The prediction unit predicts future user behavior based on the analysis results obtained by the analysis unit. The prediction unit can use, for example, an algorithm that predicts future behavior based on past behavioral data. The prediction unit includes AI processing and can use AI to predict future user behavior. For example, the prediction unit can use AI to analyze past data and predict future user behavior.

[0132] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0133] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0134] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0135] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, test design unit, design proposal unit, and results provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects user behavior data using the camera 42 and microphone 38B of the smart device 14 and stores it in a database using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future user behavior. The test design unit is implemented in the specific processing unit 46A of the smart device 14 and designs the optimal test pattern. The design proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal design. The results provision unit is implemented in the specific processing unit 46A of the smart device 14 and provides the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0137] As shown in Figure 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.

[0138] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0139] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0140] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0142] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0143] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0144] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0145] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0146] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0147] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0148] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0149] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0150] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, test design unit, design proposal unit, and results provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and stores it in a database by the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and predicts future user behavior. The test design unit is implemented, for example, in the control unit 46A of the smart glasses 214 and designs the optimal test pattern. The design proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and proposes the optimal design. The results provision unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0152] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0153] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0154] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0155] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0156] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0157] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0158] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0159] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0160] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0161] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0162] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0163] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0164] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0165] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0166] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0167] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, test design unit, design proposal unit, and results provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects user behavior data using the camera 42 and microphone 238 of the headset terminal 314 and stores it in a database using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented in the specific processing unit 290 of the data processing unit 12 and predicts future user behavior. The test design unit is implemented in the specific processing unit 46A of the headset terminal 314 and designs the optimal test pattern. The design proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and proposes the optimal design. The results provision unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0168] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0169] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0170] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0171] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0172] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0173] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0174] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0175] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0176] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0177] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0178] Storage 32 stores the data generation model 58 and the 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 the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0179] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0180] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0181] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0182] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0183] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0184] Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, test design unit, design proposal unit, and results provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects user behavior data using the camera 42 and microphone 238 of the robot 414 and stores it in a database using the control unit 46A. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts future user behavior. The test design unit is implemented, for example, by the control unit 46A of the robot 414 and designs the optimal test pattern. The design proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes the optimal design. The results provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the analysis results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0185] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0186] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0187] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0188] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0189] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0190] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0191] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0192] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0193] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0194] 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.

[0195] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0196] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0197] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0198] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0199] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0200] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0201] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0202] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0203] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a prediction unit that predicts future user behavior based on the analysis results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, generate the optimal test pattern. The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, Predicting future user behavior based on past data. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a test design department that designs the optimal test patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) We have a design proposal department that offers optimal designs. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a results provision unit that provides analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current context and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The prediction unit, It estimates the user's emotions and adjusts how predictions are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The prediction unit, When making predictions, the prediction algorithm is optimized by referring to historical data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The prediction unit, When making predictions, different prediction methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The prediction unit, It estimates the user's emotions and adjusts the importance of the prediction based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The prediction unit, When making predictions, analyze how the predictions change based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The prediction unit, When making predictions, we analyze the forecast by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned test design unit, We estimate user emotions and adjust the test design method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned test design unit, During test design, the optimal test patterns are generated by referring to past test results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned test design unit, During test design, customize test patterns based on current user behavior data. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned test design unit, We estimate user emotions and prioritize test design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned test design unit, During test design, the optimal test patterns are generated by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned test design unit, During test design, we analyze users' social media activity and propose test patterns. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned design proposal department, We estimate the user's emotions and adjust the design proposal method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned design proposal department, When proposing a design, we refer to past design data to suggest the most suitable design. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned design proposal department, When proposing a design, customize the design based on the user's current behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned design proposal department, It estimates user emotions and prioritizes design proposals based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned design proposal department, When proposing designs, we consider the user's geographical location to propose the most suitable design. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned design proposal department, When proposing designs, we analyze users' social media activity and propose designs based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned results provision unit, We estimate the user's emotions and adjust the way results are delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned results provision unit, When providing results, the optimal method of providing results is selected by referring to past result data. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned results provision unit, When providing results, customize them based on the user's current behavioral data. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned results provision unit, It estimates the user's emotions and determines the priority of result delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned results provision unit, When providing results, the optimal method of providing results will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned results provision unit, When providing results, we analyze the user's social media activity and propose methods for delivering those results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0204] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, The system includes a prediction unit that predicts future user behavior based on the analysis results obtained by the analysis unit. A system characterized by the following features.

2. It includes a test design department that designs the optimal test patterns. The system according to feature 1.

3. We have a design proposal department that offers optimal designs. The system according to feature 1.

4. It includes a results provision unit that provides analysis results. The system according to feature 1.

5. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system according to feature 1.

6. The aforementioned collection unit is Analyze users' past behavioral data to select the optimal data collection method. The system according to feature 1.

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