System

Abstract

An object of the system according to the embodiment is to quickly collect, analyze, and provide necessary data and insights.SOLUTION: A system includes a data collection part, a data analysis part, and an insight provision part. The data collection unit collects necessary data from a public database on the Internet or a database in a company. The data analysis unit analyzes the data collected by the data collection unit. An insight providing part provides a user with a result analyzed by the data analysis part.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 faced the challenge of making it difficult to quickly collect, analyze, and provide the necessary data and insights.

[0005] The system according to the embodiment aims to quickly collect, analyze, and provide necessary data and insights. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, a data analysis unit, and an insight provision unit. The data collection unit collects necessary data from public databases on the Internet or from corporate databases. The data analysis unit analyzes the data collected by the data collection unit. The insight provision unit provides the results of the analysis by the data analysis unit to a user. [Effects of the Invention]

[0007] The system according to the embodiment can quickly collect, analyze, and provide necessary data and insights. [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 AI ​​system according to the embodiment of the present invention is a system that allows users to instantly obtain the data and insights they need when they need them. This system utilizes generative AI to collect, analyze, and provide data. This allows the AI ​​system to quickly and accurately provide the information users are looking for.

[0029] The AI ​​system according to the embodiment includes a data collection unit, a data analysis unit, and an insight provision unit. The data collection unit collects necessary data from public databases on the Internet or internal corporate databases. For example, when a user inputs a prompt such as "I want to know the latest market trends," the data collection unit causes the generation AI to collect related data from the Internet. The data collection unit can also collect necessary data from internal corporate databases. The data analysis unit analyzes the collected data. For example, the generation AI analyzes the collected data using statistical methods or machine learning algorithms to extract the insights desired by the user. The data analysis unit can also analyze the collected market trend data and provide insights indicating future market forecasts and trends. The insight provision unit provides the analysis results to the user. For example, the generation AI presents the analysis results to the user in an easy-to-understand format. The generation AI displays the results visually using graphs and charts for easy understanding. The generation AI can also respond to user questions in natural language. For example, in response to a question such as "What is the future market growth rate?", the generation AI may respond with, "The future market growth rate is predicted to be 5% per year." As a result, the AI ​​system according to the embodiment allows users to quickly and accurately obtain the data and insights they need.

[0030] The data collection unit can implement an algorithm that evaluates the reliability of the data to be collected and prioritizes the collection of only highly reliable data. For example, the data collection unit calculates a reliability score for the data source to evaluate the reliability of the data collected by the generation AI. For example, the data collection unit assigns a score based on the data source's past reliability history and the provider's evaluation, and prioritizes the collection of data from highly reliable data sources. The data collection unit can also use Bayesian inference or machine learning models to evaluate the reliability of the data to be collected. This prioritizes the collection of highly reliable data, thereby improving the accuracy of the analysis results.

[0031] The data collection unit can develop methods to collect data from different sources in a balanced manner to ensure the diversity of the collected data. For example, to ensure the diversity of the data collected by the generative AI, the data collection unit can introduce an algorithm to collect data evenly from different sources. For example, data can be collected in a balanced manner from news sites, academic papers, social media, etc. The data collection unit can also use random sampling or stratified sampling to collect data from different industries or in different formats. This allows for the collection of data from a variety of sources, thereby providing more comprehensive insights.

[0032] The data collection unit can expand the types of data it collects, collecting not only text data but also image or audio data. For example, to expand the types of data collected by the generative AI, the data collection unit can introduce image recognition technology and build a system to collect image data. For example, it can collect images of news articles and photos from social media. The data collection unit can also introduce voice recognition technology and build a system to collect audio data. For example, it can collect podcasts and audio notes. This allows for the collection of a variety of data formats, providing richer insights.

[0033] The data collection unit can collect data from different industries and fields to provide cross-domain insights. For example, the data collection unit develops industry-specific data collection algorithms to collect data from different industries and fields. For example, data is collected from different industries such as healthcare, finance, and entertainment. The data collection unit can also introduce cross-domain data collection methods to collect data from different fields. This allows for the collection of data from different industries and fields to provide broader insights.

[0034] The data analysis unit can introduce algorithms to remove noise in the preprocessing stage to improve the accuracy of the data to be analyzed. For example, the data analysis unit develops algorithms to remove noise in the data preprocessing stage to improve the accuracy of the data to be analyzed by the generative AI. For example, this automatically corrects spelling errors and redundant information in text data. The data analysis unit can also remove outliers and fill in missing values ​​to remove noise. By removing noise, the accuracy of data analysis is improved.

[0035] The data analysis unit can develop methods for automatically detecting correlations between data in order to increase the relevance of the data to be analyzed. For example, the data analysis unit develops algorithms for automatically detecting correlations between data in order to increase the relevance of the data to be analyzed by the generative AI. For example, the data analysis unit calculates correlation coefficients and identifies highly relevant data. The data analysis unit can also detect correlations between data using the Pearson correlation coefficient or the Spearman rank correlation coefficient. This makes it possible to provide more relevant insights by automatically detecting correlations between data.

[0036] The data analysis unit can expand the range of data to be analyzed and integrate and analyze different data sets. For example, the data analysis unit builds a system that integrates and analyzes different data sets in order to expand the range of data analyzed by the generative AI. For example, it integrates and analyzes in-house data and public data. The data analysis unit can also integrate different data sets using data merging and data fusion. This allows for the integration and analysis of different data sets to provide broader insights.

[0037] The data analysis unit can analyze time series data, taking into account temporal fluctuations in the data to be analyzed. For example, the data analysis unit introduces a time series data analysis algorithm to take into account temporal fluctuations in the data to be analyzed by the generation AI. For example, it predicts future trends based on past data. The data analysis unit can also take temporal fluctuations into account using moving averages and seasonal adjustments. This makes it possible to provide insights that take temporal fluctuations into account by analyzing time series data.

[0038] The insight providing unit can introduce an algorithm that takes into account a user's past search history and behavioral patterns in order to improve the accuracy of the insights it provides. For example, the insight providing unit develops an algorithm that analyzes a user's past search history in order to improve the accuracy of the insights provided by the generation AI. For example, the insight providing unit customizes insights based on past search keywords and browsing history. The insight providing unit can also introduce a machine learning algorithm to analyze a user's behavioral patterns and improve the accuracy of the insights. This allows the insights to be provided with greater accuracy by taking into account a user's past search history and behavioral patterns.

[0039] The insight providing unit can diversify the visualization methods of the insights it provides, allowing users to intuitively understand them. For example, the insight providing unit develops visualization tools using graphs and charts to diversify the visualization methods of the insights provided by the generation AI. For example, the insight providing unit visually displays data using bar graphs, pie charts, line graphs, etc. The insight providing unit can also use infographics and dashboards to allow users to intuitively understand the insights. In this way, by diversifying the visualization methods, users can intuitively understand the insights.

[0040] The insight providing unit can expand the format of the insights it provides, providing them not only in text but also in audio or video format. For example, in order to expand the format of the insights provided by the generation AI, the insight providing unit may introduce speech synthesis technology and build a system that provides insights in audio format. For example, the insights may be read aloud using a voice assistant. The insight providing unit may also create video tutorials or presentations to provide insights in video format. This expands the format of the insights, thereby improving user convenience.

[0041] The insight providing unit can make it possible to receive the insights it provides on different devices. For example, the insight providing unit builds an insight providing system that is compatible with multiple devices so that insights provided by the generation AI can be received on different devices. For example, the insight providing unit develops an app that is compatible with smart watches and smart speakers. In addition, the insight providing unit can introduce notification functions and voice notifications to receive insights on different devices. This improves user convenience by allowing insights to be received on different devices.

[0042] The user interface providing unit can learn user behavior patterns and automatically optimize the interface in order to improve the usability of the user interface. The user interface providing unit, for example, builds a system that learns user behavior patterns and automatically optimizes the interface. For example, it analyzes the user's click history and operation time and proposes the optimal interface layout. The user interface providing unit can also use a machine learning algorithm to learn user behavior patterns and improve the interface design. In this way, usability is improved by learning user behavior patterns and automatically optimizing the interface.

[0043] The user interface providing unit can add a voice recognition function to the user interface, enabling operation by voice input. The user interface providing unit, for example, adds a voice recognition function to the user interface, building a system that enables operation by voice input. For example, it makes it possible to instruct data collection and analysis by voice commands. The user interface providing unit can also use a voice recognition API to set voice commands. This allows operation by voice input, improving user convenience.

[0044] The user interface providing unit can make the user interface multilingual and accommodate users who speak different languages. For example, the user interface providing unit builds a system that automatically translates interface text to make the user interface multilingual. For example, the user interface providing unit can accommodate multiple languages, such as English, Japanese, and Chinese. The user interface providing unit can also use a translation API to provide a language selection option. This makes the interface multilingual and accommodates users who speak different languages.

[0045] The user interface providing unit can make the user interface available on different platforms. For example, the user interface providing unit develops a 3D model of the interface to make the user interface available on VR and AR platforms. For example, the user interface providing unit provides an interface that can be used with a VR headset or AR glasses. The user interface providing unit can also support different platforms using cross-platform development tools. This allows the user interface to be used on different platforms, improving user convenience.

[0046] The security protection unit can provide stronger security by improving the data encryption method. For example, the security protection unit introduces the latest encryption algorithm to improve the data encryption method. For example, quantum cryptography is used to strengthen data security. The security protection unit can also improve the encryption key management method to provide stronger security. This makes it possible to provide stronger security by improving the data encryption method.

[0047] The security protection unit can introduce data anonymization technology to protect user privacy. The security protection unit, for example, builds a system that introduces data anonymization technology to protect user privacy. For example, personal information is anonymized so that a specific individual cannot be identified. The security protection unit can also formulate a privacy policy and provide specific methods for protecting user privacy. In this way, user privacy can be protected by introducing data anonymization technology.

[0048] The security protection unit can implement multiple layers of security measures and combine different security technologies. For example, the security protection unit builds a system that combines different security technologies to implement multiple layers of security measures. For example, a firewall, antivirus software, and an intrusion detection system are used together. The security protection unit can also combine data encryption, access control, and monitoring systems. This allows for the provision of stronger security by implementing multiple layers of security measures.

[0049] The security protection unit can provide a dashboard that allows users to check their own data usage status in real time for privacy protection. The security protection unit, for example, builds a system that provides a dashboard that allows users to check their own data usage status in real time for privacy protection. For example, the security protection unit displays the status of data collection, analysis, and provision. The security protection unit can also design the dashboard and display real-time data. This allows users to check their own data usage status in real time, thereby strengthening privacy protection.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The data collection unit can collect region-specific data by utilizing the user's location information. For example, if the user is in a specific region, news and market trend data related to that region can be collected preferentially. The data collection unit can also collect local event information and data on local businesses based on the location information. This makes it possible to provide region-specific insights based on the user's location information.

[0052] The data analysis unit can perform analysis by combining multiple analysis methods to improve the reliability of the analysis results. For example, it can analyze data using a combination of statistical methods and machine learning algorithms. The data analysis unit can also compare the results of different analysis methods and select the most reliable result. In this way, combining multiple analysis methods improves the reliability of the analysis results.

[0053] The data analysis unit can diversify the visualization methods of analysis results to enable users to intuitively understand them. For example, data can be visualized using 3D graphs or interactive dashboards. The data analysis unit can also provide analysis results in the form of animations or visual storytelling. This diversification of visualization methods allows users to intuitively understand the analysis results.

[0054] The data analysis unit can introduce an anomaly detection algorithm in the data preprocessing stage to improve the accuracy of the analysis results. For example, it can automatically detect and correct outliers and inconsistencies in the data. The data analysis unit can also use an anomaly detection algorithm to improve the quality of the data. As a result, the introduction of an anomaly detection algorithm improves the accuracy of the analysis results.

[0055] The data analysis unit can use crowdsourcing to verify the data in order to improve the reliability of the analysis results. For example, the analysis results can be reviewed by multiple experts to collect feedback. The data analysis unit can also use crowdsourcing to incorporate the opinions of experts in order to improve the accuracy of the analysis results. In this way, the reliability of the analysis results can be improved by utilizing crowdsourcing.

[0056] The data analysis department can integrate and analyze data from different data sources to ensure diversity in the analysis results. For example, data from public databases and in-house databases can be integrated and analyzed. The data analysis department can also use data merging and data fusion techniques to integrate data from different data sources. This allows the integration of data from different data sources to provide more diverse insights.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The data collection unit collects the necessary data from public databases on the Internet or from internal company databases. For example, when a user enters a prompt such as "I want to know the latest market trends," the generation AI collects relevant data from the Internet. It can also collect necessary data from internal company databases. Step 2: The data analysis unit analyzes the collected data. For example, the generative AI can analyze the collected data using statistical methods and machine learning algorithms to extract the insights the user is looking for. It can also analyze the collected market trend data to provide insights that indicate future market forecasts and trends. Step 3: The insight provider provides the analysis results to the user. For example, the generative AI presents the analysis results to the user in an easy-to-understand format. It displays them visually in an easy-to-understand manner using graphs and charts. It can also respond to user questions in natural language. For example, in response to a question such as "What is the future market growth rate?", it can respond with "The future market growth rate is predicted to be 5% per year."

[0059] (Example 2) The AI ​​system according to the embodiment of the present invention is a system that allows users to instantly obtain the data and insights they need when they need them. This system utilizes generative AI to collect, analyze, and provide data. This allows the AI ​​system to quickly and accurately provide the information users are looking for.

[0060] The AI ​​system according to the embodiment includes a data collection unit, a data analysis unit, and an insight provision unit. The data collection unit collects necessary data from public databases on the Internet or internal corporate databases. For example, when a user inputs a prompt such as "I want to know the latest market trends," the data collection unit causes the generation AI to collect related data from the Internet. The data collection unit can also collect necessary data from internal corporate databases. The data analysis unit analyzes the collected data. For example, the generation AI analyzes the collected data using statistical methods or machine learning algorithms to extract the insights desired by the user. The data analysis unit can also analyze the collected market trend data and provide insights indicating future market forecasts and trends. The insight provision unit provides the analysis results to the user. For example, the generation AI presents the analysis results to the user in an easy-to-understand format. The generation AI displays the results visually using graphs and charts for easy understanding. The generation AI can also respond to user questions in natural language. For example, in response to a question such as "What is the future market growth rate?", the generation AI may respond with, "The future market growth rate is predicted to be 5% per year." As a result, the AI ​​system according to the embodiment allows users to quickly and accurately obtain the data and insights they need.

[0061] The data collection unit can implement an algorithm that evaluates the reliability of the data to be collected and prioritizes the collection of only highly reliable data. For example, the data collection unit calculates a reliability score for the data source to evaluate the reliability of the data collected by the generation AI. For example, the data collection unit assigns a score based on the data source's past reliability history and the provider's evaluation, and prioritizes the collection of data from highly reliable data sources. The data collection unit can also use Bayesian inference or machine learning models to evaluate the reliability of the data to be collected. This prioritizes the collection of highly reliable data, thereby improving the accuracy of the analysis results.

[0062] The data collection unit can develop methods to collect data from different sources in a balanced manner to ensure the diversity of the collected data. For example, to ensure the diversity of the data collected by the generative AI, the data collection unit can introduce an algorithm to collect data evenly from different sources. For example, data can be collected in a balanced manner from news sites, academic papers, social media, etc. The data collection unit can also use random sampling or stratified sampling to collect data from different industries or in different formats. This allows for the collection of data from a variety of sources, thereby providing more comprehensive insights.

[0063] The data collection unit can use the emotion estimation function to analyze the emotional tone of the data to be collected and prioritize collecting data with positive emotions. The data collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotional tone of the data to be collected in real time. For example, the data collection unit calculates an emotion score for text data and prioritizes collecting data with positive emotions. The data collection unit can also introduce an emotion analysis algorithm to identify and prioritize collecting data with positive emotions using the emotion estimation function. This allows for the prioritized collection of data with positive emotions, thereby providing better insights to users.

[0064] The data collection unit can expand the types of data it collects, collecting not only text data but also image or audio data. For example, to expand the types of data collected by the generative AI, the data collection unit can introduce image recognition technology and build a system to collect image data. For example, it can collect images of news articles and photos from social media. The data collection unit can also introduce voice recognition technology and build a system to collect audio data. For example, it can collect podcasts and audio notes. This allows for the collection of a variety of data formats, providing richer insights.

[0065] The data collection unit can collect data from different industries and fields to provide cross-domain insights. For example, the data collection unit develops industry-specific data collection algorithms to collect data from different industries and fields. For example, data is collected from different industries such as healthcare, finance, and entertainment. The data collection unit can also introduce cross-domain data collection methods to collect data from different fields. This allows for the collection of data from different industries and fields to provide broader insights.

[0066] The data collection unit can use the emotion estimation function to analyze the emotion of prompts entered by a user in real time and collect data according to the user's emotion. The data collection unit, for example, uses the emotion estimation function to build a system that analyzes the emotion of prompts entered by a user in real time. For example, the data collection unit calculates an emotion score for the user's input text and collects data according to the emotion. The data collection unit can also introduce an emotion analysis algorithm to use the emotion estimation function to collect data according to the user's emotion. This allows for more appropriate insights to be provided by collecting data according to the user's emotion.

[0067] The data analysis unit can introduce algorithms to remove noise in the preprocessing stage to improve the accuracy of the data to be analyzed. For example, the data analysis unit develops algorithms to remove noise in the data preprocessing stage to improve the accuracy of the data to be analyzed by the generative AI. For example, this automatically corrects spelling errors and redundant information in text data. The data analysis unit can also remove outliers and fill in missing values ​​to remove noise. By removing noise, the accuracy of data analysis is improved.

[0068] The data analysis unit can develop methods for automatically detecting correlations between data in order to increase the relevance of the data to be analyzed. For example, the data analysis unit develops algorithms for automatically detecting correlations between data in order to increase the relevance of the data to be analyzed by the generative AI. For example, the data analysis unit calculates correlation coefficients and identifies highly relevant data. The data analysis unit can also detect correlations between data using the Pearson correlation coefficient or the Spearman rank correlation coefficient. This makes it possible to provide more relevant insights by automatically detecting correlations between data.

[0069] The data analysis unit can use the emotion estimation function to extract emotional elements contained in the analysis results and provide emotional insights to the user. The data analysis unit, for example, uses the emotion estimation function to build a system that extracts emotional elements contained in the analysis results. For example, the data analysis unit calculates an emotion score for text data and provides emotional insights. The data analysis unit can also introduce natural language processing technology or emotion analysis algorithms to extract emotional elements using the emotion estimation function. This allows the extraction of emotional elements to provide more emotional insights to the user.

[0070] The data analysis unit can expand the range of data to be analyzed and integrate and analyze different data sets. For example, the data analysis unit builds a system that integrates and analyzes different data sets in order to expand the range of data analyzed by the generative AI. For example, it integrates and analyzes in-house data and public data. The data analysis unit can also integrate different data sets using data merging and data fusion. This allows for the integration and analysis of different data sets to provide broader insights.

[0071] The data analysis unit can analyze time series data, taking into account temporal fluctuations in the data to be analyzed. For example, the data analysis unit introduces a time series data analysis algorithm to take into account temporal fluctuations in the data to be analyzed by the generation AI. For example, it predicts future trends based on past data. The data analysis unit can also take temporal fluctuations into account using moving averages and seasonal adjustments. This makes it possible to provide insights that take temporal fluctuations into account by analyzing time series data.

[0072] The data analysis unit can use the emotion estimation function to monitor the user's emotional response to the analysis results in real time and improve the analysis method. The data analysis unit, for example, uses the emotion estimation function to build a system that monitors the user's emotional response to the analysis results in real time. For example, the data analysis unit analyzes the user's facial expressions and voice and calculates an emotion score. The data analysis unit can also use the emotion estimation function to monitor the user's emotional response and introduce a feedback system to improve the analysis method. In this way, by monitoring the user's emotional response, the analysis method can be improved and better insights can be provided.

[0073] The insight providing unit can introduce an algorithm that takes into account a user's past search history and behavioral patterns in order to improve the accuracy of the insights it provides. For example, the insight providing unit develops an algorithm that analyzes a user's past search history in order to improve the accuracy of the insights provided by the generation AI. For example, the insight providing unit customizes insights based on past search keywords and browsing history. The insight providing unit can also introduce a machine learning algorithm to analyze a user's behavioral patterns and improve the accuracy of the insights. This allows the insights to be provided with greater accuracy by taking into account a user's past search history and behavioral patterns.

[0074] The insight providing unit can diversify the visualization methods of the insights it provides, allowing users to intuitively understand them. For example, the insight providing unit develops visualization tools using graphs and charts to diversify the visualization methods of the insights provided by the generation AI. For example, the insight providing unit visually displays data using bar graphs, pie charts, line graphs, etc. The insight providing unit can also use infographics and dashboards to allow users to intuitively understand the insights. In this way, by diversifying the visualization methods, users can intuitively understand the insights.

[0075] The insight providing unit can use the emotion estimation function to preferentially provide insights that evoke the most positive emotions in the user. The insight providing unit, for example, uses the emotion estimation function to build a system that identifies insights that evoke the most positive emotions in the user. For example, the insight providing unit analyzes the user's facial expressions and voice to calculate an emotion score. The insight providing unit can also use the emotion estimation function to introduce an emotion analysis algorithm to preferentially provide insights based on the user's emotion score. This improves the user experience by preferentially providing insights that evoke the most positive emotions in the user.

[0076] The insight providing unit can expand the format of the insights it provides, providing them not only in text but also in audio or video format. For example, in order to expand the format of the insights provided by the generation AI, the insight providing unit may introduce speech synthesis technology and build a system that provides insights in audio format. For example, the insights may be read aloud using a voice assistant. The insight providing unit may also create video tutorials or presentations to provide insights in video format. This expands the format of the insights, thereby improving user convenience.

[0077] The insight providing unit can make it possible to receive the insights it provides on different devices. For example, the insight providing unit builds an insight providing system that is compatible with multiple devices so that insights provided by the generation AI can be received on different devices. For example, the insight providing unit develops an app that is compatible with smart watches and smart speakers. In addition, the insight providing unit can introduce notification functions and voice notifications to receive insights on different devices. This improves user convenience by allowing insights to be received on different devices.

[0078] The insight providing unit can use the emotion estimation function to customize the method of providing insights according to the user's emotions. For example, the insight providing unit uses the emotion estimation function to build a system that customizes the method of providing insights according to the user's emotions. For example, the insight providing unit adjusts the format and content of the insights based on the user's emotion score. Furthermore, the insight providing unit can use the emotion estimation function to introduce a feedback system according to the user's emotions. This improves the user experience by customizing the method of providing insights according to the user's emotions.

[0079] The user interface providing unit can learn user behavior patterns and automatically optimize the interface in order to improve the usability of the user interface. The user interface providing unit, for example, builds a system that learns user behavior patterns and automatically optimizes the interface. For example, it analyzes the user's click history and operation time and proposes the optimal interface layout. The user interface providing unit can also use a machine learning algorithm to learn user behavior patterns and improve the interface design. In this way, usability is improved by learning user behavior patterns and automatically optimizing the interface.

[0080] The user interface providing unit can add a voice recognition function to the user interface, enabling operation by voice input. The user interface providing unit, for example, adds a voice recognition function to the user interface, building a system that enables operation by voice input. For example, it makes it possible to instruct data collection and analysis by voice commands. The user interface providing unit can also use a voice recognition API to set voice commands. This allows operation by voice input, improving user convenience.

[0081] The user interface providing unit can use the emotion estimation function to customize the interface according to the user's emotions. For example, the user interface providing unit uses the emotion estimation function to build a system that customizes the interface according to the user's emotions. For example, the color and layout of the interface can be adjusted based on the user's emotion score. The user interface providing unit can also use an emotion analysis algorithm to provide a personalized UI based on the user's emotions. This improves the user experience by customizing the interface according to the user's emotions.

[0082] The user interface providing unit can make the user interface multilingual and accommodate users who speak different languages. For example, the user interface providing unit builds a system that automatically translates interface text to make the user interface multilingual. For example, the user interface providing unit can accommodate multiple languages, such as English, Japanese, and Chinese. The user interface providing unit can also use a translation API to provide a language selection option. This makes the interface multilingual and accommodates users who speak different languages.

[0083] The user interface providing unit can make the user interface available on different platforms. For example, the user interface providing unit develops a 3D model of the interface to make the user interface available on VR and AR platforms. For example, the user interface providing unit provides an interface that can be used with a VR headset or AR glasses. The user interface providing unit can also support different platforms using cross-platform development tools. This allows the user interface to be used on different platforms, improving user convenience.

[0084] The user interface providing unit can use the emotion estimation function to change the theme or design of the interface according to the user's emotion. For example, the user interface providing unit uses the emotion estimation function to build a system that changes the theme or design of the interface according to the user's emotion. For example, the color or layout of the interface can be adjusted based on the user's emotion score. The user interface providing unit can also dynamically change the theme using an emotion analysis algorithm. This improves the user experience by changing the theme or design of the interface according to the user's emotion.

[0085] The security protection unit can provide stronger security by improving the data encryption method. For example, the security protection unit introduces the latest encryption algorithm to improve the data encryption method. For example, quantum cryptography is used to strengthen data security. The security protection unit can also improve the encryption key management method to provide stronger security. This makes it possible to provide stronger security by improving the data encryption method.

[0086] The security protection unit can introduce data anonymization technology to protect user privacy. The security protection unit, for example, builds a system that introduces data anonymization technology to protect user privacy. For example, personal information is anonymized so that a specific individual cannot be identified. The security protection unit can also formulate a privacy policy and provide specific methods for protecting user privacy. In this way, user privacy can be protected by introducing data anonymization technology.

[0087] The security protection unit can use the emotion estimation function to automatically adjust the security level according to the user's emotion. For example, the security protection unit uses the emotion estimation function to build a system that automatically adjusts the security level according to the user's emotion. For example, the security level can be strengthened when the user feels anxious. The security protection unit can also use an emotion analysis algorithm to dynamically change security settings. This automatically adjusts the security level according to the user's emotion, thereby improving the user's sense of security.

[0088] The security protection unit can implement multiple layers of security measures and combine different security technologies. For example, the security protection unit builds a system that combines different security technologies to implement multiple layers of security measures. For example, a firewall, antivirus software, and an intrusion detection system are used together. The security protection unit can also combine data encryption, access control, and monitoring systems. This allows for the provision of stronger security by implementing multiple layers of security measures.

[0089] The security protection unit can provide a dashboard that allows users to check their own data usage status in real time for privacy protection. The security protection unit, for example, builds a system that provides a dashboard that allows users to check their own data usage status in real time for privacy protection. For example, the security protection unit displays the status of data collection, analysis, and provision. The security protection unit can also design the dashboard and display real-time data. This allows users to check their own data usage status in real time, thereby strengthening privacy protection.

[0090] The security protection unit can use the emotion estimation function to suggest privacy settings according to the user's emotions. For example, the security protection unit uses the emotion estimation function to build a system that suggests privacy settings according to the user's emotions. For example, if the user feels anxious, the security protection unit suggests strengthening the privacy settings. Furthermore, the security protection unit can use an emotion analysis algorithm to dynamically change the privacy settings. This improves the user's sense of security by suggesting privacy settings according to the user's emotions.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The data collection unit can collect region-specific data by utilizing the user's location information. For example, if the user is in a specific region, news and market trend data related to that region can be collected preferentially. The data collection unit can also collect local event information and data on local businesses based on the location information. This makes it possible to provide region-specific insights based on the user's location information.

[0093] The data analysis unit can perform analysis by combining multiple analysis methods to improve the reliability of the analysis results. For example, it can analyze data using a combination of statistical methods and machine learning algorithms. The data analysis unit can also compare the results of different analysis methods and select the most reliable result. In this way, combining multiple analysis methods improves the reliability of the analysis results.

[0094] The data collection unit can use the emotion estimation function to maintain the emotional balance of the data it collects. For example, it can collect data with both positive and negative emotions equally. The data collection unit can also use the emotion estimation function to sort data based on emotion scores to maintain emotional balance. This allows it to provide more diversified insights by maintaining emotional balance.

[0095] The data analysis unit can diversify the visualization methods of analysis results to enable users to intuitively understand them. For example, data can be visualized using 3D graphs or interactive dashboards. The data analysis unit can also provide analysis results in the form of animations or visual storytelling. This diversification of visualization methods allows users to intuitively understand the analysis results.

[0096] The data collection unit can use the emotion estimation function to change the priority of data collection according to the user's emotions. For example, if the user is feeling stressed, the data collection unit can prioritize collecting positive data that will help the user relax. The data collection unit can also use the emotion estimation function to adjust the data collection strategy according to the user's emotions. This improves the user experience by collecting data according to the user's emotions.

[0097] The data analysis unit can introduce an anomaly detection algorithm in the data preprocessing stage to improve the accuracy of the analysis results. For example, it can automatically detect and correct outliers and inconsistencies in the data. The data analysis unit can also use an anomaly detection algorithm to improve the quality of the data. As a result, the introduction of an anomaly detection algorithm improves the accuracy of the analysis results.

[0098] The data collection unit can use the emotion estimation function to filter data based on the user's emotions. For example, if the user has positive emotions, positive data is preferentially displayed. The data collection unit can also use the emotion estimation function to select data based on emotion scores in order to filter data according to the user's emotions. This improves the user experience by filtering data based on the user's emotions.

[0099] The data analysis unit can use crowdsourcing to verify the data in order to improve the reliability of the analysis results. For example, the analysis results can be reviewed by multiple experts to collect feedback. The data analysis unit can also use crowdsourcing to incorporate the opinions of experts in order to improve the accuracy of the analysis results. In this way, the reliability of the analysis results can be improved by utilizing crowdsourcing.

[0100] The data analysis unit can use the emotion estimation function to analyze the emotional tone included in the analysis results and provide emotional insights to the user. For example, the data analysis unit can calculate an emotion score for the text data of the analysis results to indicate the emotional tone. The data analysis unit can also use the emotion estimation function to introduce an emotion analysis algorithm to provide emotional insights. This allows for more emotional insights to be provided to the user by analyzing the emotional tone.

[0101] The data analysis department can integrate and analyze data from different data sources to ensure diversity in the analysis results. For example, data from public databases and in-house databases can be integrated and analyzed. The data analysis department can also use data merging and data fusion techniques to integrate data from different data sources. This allows the integration of data from different data sources to provide more diverse insights.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The data collection unit collects the necessary data from public databases on the Internet or from internal company databases. For example, when a user enters a prompt such as "I want to know the latest market trends," the generation AI collects relevant data from the Internet. It can also collect necessary data from internal company databases. Step 2: The data analysis unit analyzes the collected data. For example, the generative AI can analyze the collected data using statistical methods and machine learning algorithms to extract the insights the user is looking for. It can also analyze the collected market trend data to provide insights that indicate future market forecasts and trends. Step 3: The insight provider provides the analysis results to the user. For example, the generative AI presents the analysis results to the user in an easy-to-understand format. It displays them visually in an easy-to-understand manner using graphs and charts. It can also respond to user questions in natural language. For example, in response to a question such as "What is the future market growth rate?", it can respond with "The future market growth rate is predicted to be 5% per year."

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

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

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

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 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.

[0133] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0136] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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]

[0171] 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 necessary data from public databases on the Internet or from in-house databases; a data analysis unit that analyzes the data collected by the data collection unit; an insight providing unit that provides the results analyzed by the data analysis unit to a user. A system characterized by:

2. The data collection unit Expand the types of data collected to include not only text data but also image or audio data.

2. The system of claim 1.

3. The data analysis unit In order to improve the accuracy of the data to be analyzed, we introduce a noise removal algorithm in the preprocessing stage.

2. The system of claim 1.

4. The insight providing unit Implementing algorithms that take into account the user's past search history and behavioral patterns to improve the accuracy of the insights provided.

2. The system of claim 1.

5. The user interface providing unit: To improve the usability of the user interface, the behavioral patterns of the user are learned and the interface is automatically optimized.

2. The system of claim 1.

6. The data collection unit Uses sentiment estimation to analyze the emotional tone of collected data and prioritize collection of data with positive sentiment.

2. The system of claim 1.

7. The data analysis unit Using emotion estimation function, emotional elements contained in the analysis results are extracted and emotional insights are provided to the user.

2. The system of claim 1.

8. The insight providing unit Using emotion estimation functionality, the insights that the user feels most positive about are prioritized.

2. The system of claim 1.

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