System

Abstract

An object of a system according to an embodiment is to analyze big data of an existing user and predict acceptance of a new service.SOLUTION: A system according to an embodiment includes a data collector, a preprocessor, a behavior analyzer, a prediction model constructor, and a prediction executor. The data collection unit collects big data of an existing user. The preprocessing part preprocesses the data collected by the data collection part. The behavior analysis unit analyzes a behavior pattern of the user based on the data preprocessed by the preprocessing unit. The prediction model construction unit constructs an acceptance prediction model of the new service based on the behavior pattern analyzed by the behavior analysis unit. The prediction execution unit performs the acceptance prediction of the new service using the prediction model constructed by the prediction model construction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have not adequately utilized big data of existing users to predict the acceptance of new services, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze big data of existing users and predict the acceptance of new services. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a preprocessing unit, a behavior analysis unit, a prediction model construction unit, and a prediction execution unit. The data collection unit collects big data on existing users. The preprocessing unit preprocesses the data collected by the data collection unit. The behavior analysis unit analyzes user behavior patterns based on the data preprocessed by the preprocessing unit. The prediction model construction unit constructs a new service acceptance prediction model based on the behavior patterns analyzed by the behavior analysis unit. The prediction execution unit predicts acceptance of the new service using the prediction model constructed by the prediction model construction unit. [Effects of the Invention]

[0007] The system according to the embodiment can analyze big data of existing users and predict the acceptance of new services. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The acceptance prediction system according to the embodiment of the present invention is a system in which a generation AI analyzes big data of existing users and predicts the acceptance of a new service. As a result, the acceptance prediction system can predict the acceptance of a new service with high accuracy.

[0029] The acceptance prediction system according to the embodiment includes a data collection unit, a preprocessing unit, a behavior analysis unit, a prediction model construction unit, and a prediction execution unit. The data collection unit collects big data on existing users, such as website access logs, purchase histories, and social media data. The data collection unit can also collect survey results and user behavior histories. The preprocessing unit preprocesses the collected data, such as by cleaning, normalizing, and filtering the data. The preprocessing unit can also remove duplicate data and impute missing values. The preprocessing unit can also scale the data. The behavior analysis unit analyzes user behavior patterns based on the preprocessed data. For example, the generation AI analyzes user behavior patterns using techniques such as clustering and classification. The behavior analysis unit can identify a group of users who use a specific service during a specific time period or a group of users who frequently purchase a specific product. The prediction model construction unit constructs an acceptance prediction model for a new service based on the behavior patterns analyzed by the behavior analysis unit. For example, the generation AI constructs a model to predict the degree to which users who have previously used a specific service will use a new service. The predictive model construction unit can also construct a model that associates user preferences and behavioral patterns with the characteristics of the new service. The prediction execution unit uses the predictive model constructed by the predictive model construction unit to predict the acceptance of the new service. For example, the generation AI inputs data from existing users to predict the acceptance rate of the new service. The prediction execution unit can also output a prediction result such as "New service A is likely to be used by 30% of existing users." This allows the acceptance prediction system according to the embodiment to accurately predict the acceptance of new services. For example, by making acceptance predictions at the planning stage of a new service, companies can reduce risks and increase the success rate. This also contributes to optimizing marketing strategies and enabling efficient promotional activities.

[0030] The preprocessing section can automatically detect specific events and trends from the data and emphasize that information during preprocessing. The preprocessing section can automatically detect specific events and trends from the collected data, for example, using generative AI. For example, it can extract search keywords or purchase history that have increased sharply during a specific period and emphasize that information during preprocessing. The preprocessing section can also detect specific events and trends using time series analysis and topic modeling. This improves the accuracy of data preprocessing by emphasizing specific events and trends.

[0031] The preprocessing unit can detect abnormal values ​​and outliers in real time during data preprocessing and make appropriate corrections. The preprocessing unit can, for example, use generative AI to detect abnormal values ​​and outliers in real time during data preprocessing and make appropriate corrections. For example, it detects and corrects abnormal purchase histories and access logs. The preprocessing unit can also detect abnormal values ​​and outliers using statistical methods and machine learning algorithms. This allows for real-time detection of abnormal values ​​and outliers and appropriate corrections, improving the accuracy of data preprocessing.

[0032] The data collection unit can simultaneously collect the user's biometric information and analyze it in combination with behavioral data. For example, when collecting big data, the data collection unit simultaneously collects the user's biometric information (heart rate, galvanic skin response, etc.) and analyzes it in combination with behavioral data. For example, the data collection unit collects data from a wearable device. The data collection unit can also analyze the user's behavioral data based on the heart rate and galvanic skin response data. This enables more accurate behavioral pattern analysis by combining and analyzing the biometric information with behavioral data.

[0033] The data collection unit can integrate different data sources to collect data from more diverse angles. The data collection unit can, for example, integrate different data sources (e.g., SNS data and IoT device data) to collect data from more diverse angles. For example, it can integrate SNS post data with smart home device data. The data collection unit can also integrate sensor data and location information data. This allows for more diverse data collection by integrating different data sources.

[0034] The behavior analysis unit can simultaneously perform past behavior data and future behavior prediction based on the behavior data. For example, when analyzing a user's behavior patterns using generative AI, the behavior analysis unit simultaneously performs past behavior data and future behavior prediction. For example, it predicts future purchases based on past purchase history. The behavior analysis unit can also predict future behavior using time series analysis and predictive models. This allows for more accurate behavior pattern analysis by simultaneously performing past behavior data and future behavior prediction.

[0035] When analyzing a user's behavioral patterns, the behavioral analysis unit can perform a detailed analysis of changes in behavioral patterns over different time periods and seasons. For example, when analyzing a user's behavioral patterns, the behavioral analysis unit uses a generative AI to perform a detailed analysis of changes in behavioral patterns over different time periods and seasons. For example, it analyzes the tendency for specific products to sell well in specific seasons. The behavioral analysis unit can also analyze behavioral patterns by time period and changes in behavior by season. This enables more accurate behavioral pattern analysis by performing a detailed analysis of changes in behavioral patterns over different time periods and seasons.

[0036] The behavior analysis unit can combine geographical location information with an analysis of a user's behavior pattern to identify behavior patterns for each region. The behavior analysis unit, for example, combines geographical location information with an analysis of a user's behavior pattern to identify behavior patterns for each region. For example, it analyzes purchasing behavior in a specific region. The behavior analysis unit can also identify behavior patterns for each region using GPS data and location information services. This makes it possible to identify behavior patterns for each region by combining geographical location information.

[0037] The behavior analysis unit can analyze behavior patterns for different user groups and clarify the characteristics of each group. For example, the behavior analysis unit can analyze behavior patterns for different user groups (age, gender, occupation, etc.) and clarify the characteristics of each group. For example, it can analyze purchasing behavior for each age group. The behavior analysis unit can also clarify the characteristics of each group using classification criteria and analysis methods for user groups. This makes it possible to clarify the characteristics of each group by analyzing the behavior patterns of different user groups.

[0038] The predictive model construction unit can build a more accurate predictive model by combining past service acceptance data with the characteristics of a new service. The predictive model construction unit can, for example, use generative AI to combine past service acceptance data with the characteristics of a new service to build a more accurate predictive model. For example, it predicts the acceptance of a new service based on past usage data. The predictive model construction unit can also build a predictive model based on past sales data and user feedback. This makes it possible to build a more accurate predictive model by combining past service acceptance data with the characteristics of a new service.

[0039] The prediction model construction unit can use the generation AI to construct multiple acceptance prediction models based on different scenarios. The prediction model construction unit, for example, uses the generation AI to construct multiple acceptance prediction models based on different scenarios (e.g., price changes or promotion strategies). For example, it constructs a prediction model based on a price change scenario and a promotion strategy scenario. The prediction model construction unit can also construct a prediction model based on a scenario that takes into account the trends of competitors. In this way, by constructing multiple acceptance prediction models based on different scenarios, more multifaceted predictions become possible.

[0040] The prediction model construction unit can incorporate competitors' service data into a new service acceptance prediction model and make predictions that take the competitive environment into consideration. The prediction model construction unit, for example, incorporates competitors' service data into a new service acceptance prediction model and makes predictions that take the competitive environment into consideration. For example, the prediction model is constructed based on competitors' service usage data. The prediction model construction unit can also make predictions that take the competitive environment into consideration based on publicly available sales data and market research data. In this way, by incorporating competitors' service data, predictions that take the competitive environment into consideration become possible.

[0041] The prediction model construction unit can construct an acceptance prediction model for each different market, taking into account the characteristics of each region. For example, the prediction model construction unit constructs an acceptance prediction model for each different market (domestic market, international market), taking into account the characteristics of each region. For example, the prediction model is constructed based on data from the domestic market and the international market. The prediction model construction unit can also construct a prediction model that takes into account the characteristics of each region, based on data from a specific regional market. In this way, by constructing an acceptance prediction model for each different market, predictions that take into account the characteristics of each region become possible.

[0042] The prediction execution unit uses the generation AI to update the prediction results in real time and perform acceptance predictions based on the latest data. The prediction execution unit, for example, uses the generation AI to update the prediction results in real time and perform acceptance predictions based on the latest data. For example, the prediction results are updated based on the latest user data. The prediction execution unit can also use the generation AI to re-learn the prediction model and improve prediction accuracy. As a result, by updating the prediction results in real time, acceptance predictions based on the latest data become possible.

[0043] The prediction execution unit can use the generation AI to make acceptance predictions based on different scenarios in real time. The prediction execution unit, for example, uses the generation AI to make acceptance predictions based on different scenarios (such as price changes and promotion strategies) in real time. For example, it makes predictions based on a price change scenario and a promotion strategy scenario. The prediction execution unit can also make predictions based on scenarios that take into account the trends of competitors. This makes it possible to make more multifaceted predictions by making acceptance predictions based on different scenarios in real time.

[0044] The prediction execution unit can display the acceptance prediction results of a new service on different devices, improving user convenience. The prediction execution unit can display, for example, the acceptance prediction results of a new service on different devices (smartphones, tablets, PCs), improving user convenience. For example, the prediction results can be displayed on a smartphone app. The prediction execution unit can also display the prediction results on tablets and PCs. This improves user convenience by displaying the prediction results on different devices.

[0045] The prediction execution unit can share the prediction results with different departments and use them in formulating strategies for each department. The prediction execution unit, for example, shares the prediction results with different departments (marketing, sales, development) and uses them in formulating strategies for each department. For example, the prediction results are provided to the marketing department to formulate a promotion strategy. The prediction execution unit can also share the prediction results with the sales department and development department and use them in formulating strategies for each department. In this way, by sharing the prediction results with different departments, they can be used in formulating strategies for each department.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data collection unit can simultaneously collect the user's biometric information and analyze it in combination with behavioral data. For example, when collecting big data, the user's biometric information (heart rate, electrodermal response, etc.) can be simultaneously collected and analyzed in combination with behavioral data. For example, data can be collected from a wearable device. The data collection unit can also analyze the user's behavioral data based on the heart rate and electrodermal response data. This allows for more accurate analysis of behavioral patterns by combining and analyzing biometric information with behavioral data.

[0048] The data collection unit can integrate different data sources to collect more diversified data. For example, it can integrate different data sources (e.g., social media data and IoT device data) to collect more diversified data. For example, it can integrate social media posting data with smart home device data. The data collection unit can also integrate sensor data and location information data. This allows for more diversified data collection by integrating different data sources.

[0049] The behavior analysis unit can simultaneously perform both past behavior data and future behavior predictions based on behavior data. For example, when analyzing a user's behavior patterns using generative AI, it simultaneously performs both past behavior data and future behavior predictions. For example, it predicts future purchases based on past purchase history. The behavior analysis unit can also predict future behavior using time series analysis and predictive models. This allows for more accurate behavior pattern analysis by simultaneously performing both past behavior data and future behavior predictions.

[0050] When analyzing a user's behavioral patterns, the behavioral analysis unit can perform a detailed analysis of changes in behavioral patterns over different time periods and seasons. For example, when analyzing a user's behavioral patterns using generative AI, the unit can perform a detailed analysis of changes in behavioral patterns over different time periods and seasons. For example, the unit can analyze the tendency for certain products to sell well in certain seasons. The behavioral analysis unit can also analyze behavioral patterns by time period and changes in behavior by season. This enables more accurate behavioral pattern analysis by performing a detailed analysis of changes in behavioral patterns over different time periods and seasons.

[0051] The behavior analysis unit can combine geographical location information with an analysis of a user's behavior pattern to identify behavior patterns for each region. For example, the behavior analysis unit can combine geographical location information with an analysis of a user's behavior pattern to identify behavior patterns for each region. For example, purchasing behavior in a specific region can be analyzed. The behavior analysis unit can also identify behavior patterns for each region using GPS data and location information services. This makes it possible to identify behavior patterns for each region by combining geographical location information.

[0052] The predictive model construction unit can build a more accurate predictive model by combining past service acceptance data with the characteristics of a new service. For example, a generative AI can be used to combine past service acceptance data with the characteristics of a new service to build a more accurate predictive model. For example, it can predict the acceptance of a new service based on past usage data. The predictive model construction unit can also build a predictive model based on past sales data and user feedback. This makes it possible to build a more accurate predictive model by combining past service acceptance data with the characteristics of a new service.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The data collection unit collects big data on existing users, such as website access logs, purchase history, social media data, survey results, and user behavior history. Step 2: The preprocessing section preprocesses the collected data, such as cleaning, normalizing, filtering, removing duplicate data, imputing missing values, and scaling the data. Step 3: The behavior analysis unit analyzes user behavior patterns based on the preprocessed data. For example, it uses techniques such as clustering and classification to analyze user behavior patterns and identify groups of users who use specific services during specific times or who frequently purchase specific products. Step 4: The prediction model construction unit constructs a new service acceptance prediction model based on the behavioral patterns analyzed by the behavior analysis unit. For example, it constructs a model that predicts to what extent users who have used a particular service in the past will use a new service, or a model that associates user preferences and behavioral patterns with the characteristics of the new service. Step 5: The prediction execution unit predicts the acceptance of the new service using the prediction model constructed by the prediction model construction unit. For example, it predicts the acceptance of the new service using data of existing users as input, and outputs a prediction result such as "New service A is likely to be used by 30% of existing users."

[0055] (Example 2) The acceptance prediction system according to the embodiment of the present invention is a system in which a generation AI analyzes big data of existing users and predicts the acceptance of a new service. As a result, the acceptance prediction system can predict the acceptance of a new service with high accuracy.

[0056] The acceptance prediction system according to the embodiment includes a data collection unit, a preprocessing unit, a behavior analysis unit, a prediction model construction unit, and a prediction execution unit. The data collection unit collects big data on existing users, such as website access logs, purchase histories, and social media data. The data collection unit can also collect survey results and user behavior histories. The preprocessing unit preprocesses the collected data, such as by cleaning, normalizing, and filtering the data. The preprocessing unit can also remove duplicate data and impute missing values. The preprocessing unit can also scale the data. The behavior analysis unit analyzes user behavior patterns based on the preprocessed data. For example, the generation AI analyzes user behavior patterns using techniques such as clustering and classification. The behavior analysis unit can identify a group of users who use a specific service during a specific time period or a group of users who frequently purchase a specific product. The prediction model construction unit constructs an acceptance prediction model for a new service based on the behavior patterns analyzed by the behavior analysis unit. For example, the generation AI constructs a model to predict the degree to which users who have previously used a specific service will use a new service. The predictive model construction unit can also construct a model that associates user preferences and behavioral patterns with the characteristics of the new service. The prediction execution unit uses the predictive model constructed by the predictive model construction unit to predict the acceptance of the new service. For example, the generation AI inputs data from existing users to predict the acceptance rate of the new service. The prediction execution unit can also output a prediction result such as "New service A is likely to be used by 30% of existing users." This allows the acceptance prediction system according to the embodiment to accurately predict the acceptance of new services. For example, by making acceptance predictions at the planning stage of a new service, companies can reduce risks and increase the success rate. This also contributes to optimizing marketing strategies and enabling efficient promotional activities.

[0057] The preprocessing section can automatically detect specific events and trends from the data and emphasize that information during preprocessing. The preprocessing section can automatically detect specific events and trends from the collected data, for example, using generative AI. For example, it can extract search keywords or purchase history that have increased sharply during a specific period and emphasize that information during preprocessing. The preprocessing section can also detect specific events and trends using time series analysis and topic modeling. This improves the accuracy of data preprocessing by emphasizing specific events and trends.

[0058] The preprocessing unit can analyze user emotional data during the data preprocessing stage and prioritize data with positive emotions. The preprocessing unit can, for example, use generative AI to analyze user emotional data from collected data and prioritize data with positive emotions. For example, it can filter data based on the emotional scores of reviews and comments. The preprocessing unit can also analyze user emotional data using text analysis or voice analysis. This improves the accuracy of data preprocessing by prioritizing data with positive emotions.

[0059] The preprocessing unit can detect abnormal values ​​and outliers in real time during data preprocessing and make appropriate corrections. The preprocessing unit can, for example, use generative AI to detect abnormal values ​​and outliers in real time during data preprocessing and make appropriate corrections. For example, it detects and corrects abnormal purchase histories and access logs. The preprocessing unit can also detect abnormal values ​​and outliers using statistical methods and machine learning algorithms. This allows for real-time detection of abnormal values ​​and outliers and appropriate corrections, improving the accuracy of data preprocessing.

[0060] The data collection unit can simultaneously collect the user's biometric information and analyze it in combination with behavioral data. For example, when collecting big data, the data collection unit simultaneously collects the user's biometric information (heart rate, galvanic skin response, etc.) and analyzes it in combination with behavioral data. For example, the data collection unit collects data from a wearable device. The data collection unit can also analyze the user's behavioral data based on the heart rate and galvanic skin response data. This enables more accurate behavioral pattern analysis by combining and analyzing the biometric information with behavioral data.

[0061] The data collection unit can integrate different data sources to collect data from more diverse angles. The data collection unit can, for example, integrate different data sources (e.g., SNS data and IoT device data) to collect data from more diverse angles. For example, it can integrate SNS post data with smart home device data. The data collection unit can also integrate sensor data and location information data. This allows for more diverse data collection by integrating different data sources.

[0062] The data collection unit can use the emotion estimation function to monitor the user's emotional state in real time during data collection and develop a data collection strategy based on the emotions. The data collection unit, for example, uses the emotion estimation function to monitor the user's emotional state in real time during data collection. For example, the data collection unit analyzes the user's facial expressions and voice using a camera or microphone. The data collection unit can also use the emotion estimation function to develop a data collection strategy based on the user's emotional state. This enables more effective data collection by developing a data collection strategy based on emotions.

[0063] The behavior analysis unit can simultaneously perform past behavior data and future behavior prediction based on the behavior data. For example, when analyzing a user's behavior patterns using generative AI, the behavior analysis unit simultaneously performs past behavior data and future behavior prediction. For example, it predicts future purchases based on past purchase history. The behavior analysis unit can also predict future behavior using time series analysis and predictive models. This allows for more accurate behavior pattern analysis by simultaneously performing past behavior data and future behavior prediction.

[0064] The behavior analysis unit can combine the user's emotional data when analyzing behavior patterns and identify behavior patterns based on changes in emotions. For example, the behavior analysis unit can combine the user's emotional data when analyzing behavior patterns and identify behavior patterns based on changes in emotions. For example, the behavior analysis unit can analyze the correlation between fluctuations in emotion scores and behavior. The behavior analysis unit can also identify behavior patterns based on changes in emotions over time. This allows for more accurate behavior pattern analysis by identifying behavior patterns based on changes in emotions.

[0065] When analyzing a user's behavioral patterns, the behavioral analysis unit can perform a detailed analysis of changes in behavioral patterns over different time periods and seasons. For example, when analyzing a user's behavioral patterns, the behavioral analysis unit uses a generative AI to perform a detailed analysis of changes in behavioral patterns over different time periods and seasons. For example, it analyzes the tendency for specific products to sell well in specific seasons. The behavioral analysis unit can also analyze behavioral patterns by time period and changes in behavior by season. This enables more accurate behavioral pattern analysis by performing a detailed analysis of changes in behavioral patterns over different time periods and seasons.

[0066] The behavior analysis unit can combine geographical location information with an analysis of a user's behavior pattern to identify behavior patterns for each region. The behavior analysis unit, for example, combines geographical location information with an analysis of a user's behavior pattern to identify behavior patterns for each region. For example, it analyzes purchasing behavior in a specific region. The behavior analysis unit can also identify behavior patterns for each region using GPS data and location information services. This makes it possible to identify behavior patterns for each region by combining geographical location information.

[0067] The behavior analysis unit can analyze behavior patterns for different user groups and clarify the characteristics of each group. For example, the behavior analysis unit can analyze behavior patterns for different user groups (age, gender, occupation, etc.) and clarify the characteristics of each group. For example, it can analyze purchasing behavior for each age group. The behavior analysis unit can also clarify the characteristics of each group using classification criteria and analysis methods for user groups. This makes it possible to clarify the characteristics of each group by analyzing the behavior patterns of different user groups.

[0068] The behavior analysis unit uses the emotion estimation function to predict behavior based on changes in emotions when analyzing a user's behavior pattern, and can provide services according to the emotions. The behavior analysis unit, for example, uses the emotion estimation function to predict behavior based on changes in emotions when analyzing a user's behavior pattern. For example, it predicts behavior during periods when positive emotions are strong. The behavior analysis unit can also provide services according to emotions. This makes it possible to provide more effective services by predicting behavior based on changes in emotions and providing services according to emotions.

[0069] The predictive model construction unit can build a more accurate predictive model by combining past service acceptance data with the characteristics of a new service. The predictive model construction unit can, for example, use generative AI to combine past service acceptance data with the characteristics of a new service to build a more accurate predictive model. For example, it predicts the acceptance of a new service based on past usage data. The predictive model construction unit can also build a predictive model based on past sales data and user feedback. This makes it possible to build a more accurate predictive model by combining past service acceptance data with the characteristics of a new service.

[0070] The predictive model construction unit can incorporate user emotional data when constructing a predictive model and perform emotion-based acceptance prediction. The predictive model construction unit, for example, incorporates user emotional data when constructing a predictive model and performs emotion-based acceptance prediction. For example, it performs acceptance prediction for users with strong positive emotions. The predictive model construction unit can also analyze user emotional data using text analysis or voice analysis. In this way, incorporating emotion data makes it possible to perform emotion-based acceptance prediction.

[0071] The prediction model construction unit can use the generation AI to construct multiple acceptance prediction models based on different scenarios. The prediction model construction unit, for example, uses the generation AI to construct multiple acceptance prediction models based on different scenarios (e.g., price changes or promotion strategies). For example, it constructs a prediction model based on a price change scenario and a promotion strategy scenario. The prediction model construction unit can also construct a prediction model based on a scenario that takes into account the trends of competitors. In this way, by constructing multiple acceptance prediction models based on different scenarios, more multifaceted predictions become possible.

[0072] The prediction model construction unit can incorporate competitors' service data into a new service acceptance prediction model and make predictions that take the competitive environment into consideration. The prediction model construction unit, for example, incorporates competitors' service data into a new service acceptance prediction model and makes predictions that take the competitive environment into consideration. For example, the prediction model is constructed based on competitors' service usage data. The prediction model construction unit can also make predictions that take the competitive environment into consideration based on publicly available sales data and market research data. In this way, by incorporating competitors' service data, predictions that take the competitive environment into consideration become possible.

[0073] The prediction model construction unit can construct an acceptance prediction model for each different market, taking into account the characteristics of each region. For example, the prediction model construction unit constructs an acceptance prediction model for each different market (domestic market, international market), taking into account the characteristics of each region. For example, the prediction model is constructed based on data from the domestic market and the international market. The prediction model construction unit can also construct a prediction model that takes into account the characteristics of each region, based on data from a specific regional market. In this way, by constructing an acceptance prediction model for each different market, predictions that take into account the characteristics of each region become possible.

[0074] The predictive model construction unit can use the emotion estimation function to perform scenario analysis based on the user's emotions and predict acceptance according to the emotions. The predictive model construction unit, for example, uses the emotion estimation function to perform scenario analysis based on the user's emotions and predict acceptance according to the emotions. For example, it predicts acceptance for users with strong positive emotions. The predictive model construction unit can also analyze user emotion data using text analysis or voice analysis. This makes it possible to predict acceptance according to emotions by performing scenario analysis based on emotions.

[0075] The prediction execution unit uses the generation AI to update the prediction results in real time and perform acceptance predictions based on the latest data. The prediction execution unit, for example, uses the generation AI to update the prediction results in real time and perform acceptance predictions based on the latest data. For example, the prediction results are updated based on the latest user data. The prediction execution unit can also use the generation AI to re-learn the prediction model and improve prediction accuracy. As a result, by updating the prediction results in real time, acceptance predictions based on the latest data become possible.

[0076] The prediction execution unit can analyze the user's emotional data based on the prediction result and propose an emotion-based marketing strategy. The prediction execution unit, for example, analyzes the user's emotional data based on the prediction result and proposes an emotion-based marketing strategy. For example, it proposes a marketing strategy for users with strong positive emotions. The prediction execution unit can also analyze the user's emotional data using text analysis or voice analysis. This allows for more effective marketing by proposing an emotion-based marketing strategy.

[0077] The prediction execution unit can use the generation AI to make acceptance predictions based on different scenarios in real time. The prediction execution unit, for example, uses the generation AI to make acceptance predictions based on different scenarios (such as price changes and promotion strategies) in real time. For example, it makes predictions based on a price change scenario and a promotion strategy scenario. The prediction execution unit can also make predictions based on scenarios that take into account the trends of competitors. This makes it possible to make more multifaceted predictions by making acceptance predictions based on different scenarios in real time.

[0078] The prediction execution unit can display the acceptance prediction results of a new service on different devices, improving user convenience. The prediction execution unit can display, for example, the acceptance prediction results of a new service on different devices (smartphones, tablets, PCs), improving user convenience. For example, the prediction results can be displayed on a smartphone app. The prediction execution unit can also display the prediction results on tablets and PCs. This improves user convenience by displaying the prediction results on different devices.

[0079] The prediction execution unit can share the prediction results with different departments and use them in formulating strategies for each department. The prediction execution unit, for example, shares the prediction results with different departments (marketing, sales, development) and uses them in formulating strategies for each department. For example, the prediction results are provided to the marketing department to formulate a promotion strategy. The prediction execution unit can also share the prediction results with the sales department and development department and use them in formulating strategies for each department. In this way, by sharing the prediction results with different departments, they can be used in formulating strategies for each department.

[0080] The prediction execution unit can use the emotion estimation function to monitor the user's emotional response based on the prediction result in real time and provide feedback according to the emotion. The prediction execution unit, for example, uses the emotion estimation function to monitor the user's emotional response based on the prediction result in real time and provide feedback according to the emotion. For example, feedback is provided to a user with strong positive emotions. The prediction execution unit can also analyze the user's emotional data using text analysis or voice analysis. This allows for more effective user support by providing feedback according to the emotion.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] The data collection unit can simultaneously collect the user's biometric information and analyze it in combination with behavioral data. For example, when collecting big data, the user's biometric information (heart rate, electrodermal response, etc.) can be simultaneously collected and analyzed in combination with behavioral data. For example, data can be collected from a wearable device. The data collection unit can also analyze the user's behavioral data based on the heart rate and electrodermal response data. This allows for more accurate analysis of behavioral patterns by combining and analyzing biometric information with behavioral data.

[0083] The data collection unit can integrate different data sources to collect more diversified data. For example, it can integrate different data sources (e.g., social media data and IoT device data) to collect more diversified data. For example, it can integrate social media posting data with smart home device data. The data collection unit can also integrate sensor data and location information data. This allows for more diversified data collection by integrating different data sources.

[0084] The data collection unit can use the emotion estimation function to monitor the user's emotional state in real time during data collection and develop a data collection strategy based on the user's emotions. For example, the data collection unit can analyze the user's facial expressions and voice using a camera or microphone. The data collection unit can also use the emotion estimation function to develop a data collection strategy based on the user's emotional state. This allows for more effective data collection by developing a data collection strategy based on emotions.

[0085] The behavior analysis unit can simultaneously perform both past behavior data and future behavior predictions based on behavior data. For example, when analyzing a user's behavior patterns using generative AI, it simultaneously performs both past behavior data and future behavior predictions. For example, it predicts future purchases based on past purchase history. The behavior analysis unit can also predict future behavior using time series analysis and predictive models. This allows for more accurate behavior pattern analysis by simultaneously performing both past behavior data and future behavior predictions.

[0086] The behavior analysis unit can combine user emotional data when analyzing behavior patterns and identify behavior patterns based on changes in emotions. For example, when analyzing behavior patterns, the behavior analysis unit can combine user emotional data and identify behavior patterns based on changes in emotions. For example, the behavior analysis unit can analyze the correlation between changes in emotion scores and behavior. The behavior analysis unit can also identify behavior patterns based on changes in emotions over time. This allows for more accurate behavior pattern analysis by identifying behavior patterns based on changes in emotions.

[0087] When analyzing a user's behavioral patterns, the behavioral analysis unit can perform a detailed analysis of changes in behavioral patterns over different time periods and seasons. For example, when analyzing a user's behavioral patterns using generative AI, the unit can perform a detailed analysis of changes in behavioral patterns over different time periods and seasons. For example, the unit can analyze the tendency for certain products to sell well in certain seasons. The behavioral analysis unit can also analyze behavioral patterns by time period and changes in behavior by season. This enables more accurate behavioral pattern analysis by performing a detailed analysis of changes in behavioral patterns over different time periods and seasons.

[0088] The behavior analysis unit can combine geographical location information with an analysis of a user's behavior pattern to identify behavior patterns for each region. For example, the behavior analysis unit can combine geographical location information with an analysis of a user's behavior pattern to identify behavior patterns for each region. For example, purchasing behavior in a specific region can be analyzed. The behavior analysis unit can also identify behavior patterns for each region using GPS data and location information services. This makes it possible to identify behavior patterns for each region by combining geographical location information.

[0089] The behavior analysis unit uses the emotion estimation function to predict behavior based on changes in emotions when analyzing a user's behavior pattern, and can provide services according to the emotions. For example, the emotion estimation function is used to predict behavior based on changes in emotions when analyzing a user's behavior pattern. For example, behavior during periods when positive emotions are strong is predicted. The behavior analysis unit can also provide services according to emotions. This makes it possible to provide more effective services by predicting behavior based on changes in emotions and providing services according to emotions.

[0090] The predictive model construction unit can build a more accurate predictive model by combining past service acceptance data with the characteristics of a new service. For example, a generative AI can be used to combine past service acceptance data with the characteristics of a new service to build a more accurate predictive model. For example, it can predict the acceptance of a new service based on past usage data. The predictive model construction unit can also build a predictive model based on past sales data and user feedback. This makes it possible to build a more accurate predictive model by combining past service acceptance data with the characteristics of a new service.

[0091] The predictive model construction unit can incorporate user emotional data when constructing a predictive model and make emotion-based acceptance predictions. For example, when constructing a predictive model, the predictive model can incorporate user emotional data and make emotion-based acceptance predictions. For example, acceptance predictions can be made for users with strong positive emotions. The predictive model construction unit can also analyze user emotional data using text analysis or voice analysis. In this way, incorporating emotion data makes it possible to make emotion-based acceptance predictions.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The data collection unit collects big data on existing users, such as website access logs, purchase history, social media data, survey results, and user behavior history. Step 2: The preprocessing section preprocesses the collected data, such as cleaning, normalizing, filtering, removing duplicate data, imputing missing values, and scaling the data. Step 3: The behavior analysis unit analyzes user behavior patterns based on the preprocessed data. For example, it uses techniques such as clustering and classification to analyze user behavior patterns and identify groups of users who use specific services during specific times or who frequently purchase specific products. Step 4: The prediction model construction unit constructs a new service acceptance prediction model based on the behavioral patterns analyzed by the behavior analysis unit. For example, it constructs a model that predicts to what extent users who have used a particular service in the past will use a new service, or a model that associates user preferences and behavioral patterns with the characteristics of the new service. Step 5: The prediction execution unit predicts the acceptance of the new service using the prediction model constructed by the prediction model construction unit. For example, it predicts the acceptance of the new service using data of existing users as input, and outputs a prediction result such as "New service A is likely to be used by 30% of existing users."

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0138] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a data collection unit that collects big data from existing users; a preprocessing unit that preprocesses the data collected by the data collection unit; a behavior analysis unit that analyzes a user's behavior pattern based on the data preprocessed by the preprocessing unit; a prediction model construction unit that constructs a new service acceptance prediction model based on the behavioral patterns analyzed by the behavior analysis unit; a prediction execution unit that predicts acceptance of new services using the prediction model constructed by the prediction model construction unit. A system characterized by:

2. The data collection unit The user's biometric information is collected at the same time and analyzed in combination with behavioral data.

2. The system of claim 1.

3. The behavior analysis unit Simultaneously predict past behavioral data and future behavior based on behavioral data 2. The system of claim 1.

4. The prediction model construction unit Combining past service acceptance data with the characteristics of new services to build more accurate prediction models 2. The system of claim 1.

5. The prediction execution unit Generative AI is used to update prediction results in real time, making acceptance predictions based on the latest data.

2. The system of claim 1.

6. The pre-treatment unit In the pre-processing stage of the data, the user's emotional data is analyzed and data with positive emotions is processed preferentially.

2. The system of claim 1.

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