System

Abstract

An object of a system according to an embodiment is to predict and analyze trend information in real time and propose a strategy based on highly reliable data.SOLUTION: A system includes a trend prediction part, a report generation part, a market response prediction part, an interactive session part, a time series data analysis part, a strategy proposal part, and a data reliability securing part. The trend prediction unit predicts and analyzes trend information in real time. A report generator provides reports on future trends. The market response forecasting component forecasts market responses to new products and services. The interactive session portion provides an interactive session with an industry expert. The time-series data analysis unit analyzes and predicts time-series data. The strategy suggestion unit suggests a strategy based on the trend. The data reliability securing unit secures reliability of data.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 technology has the problem that trend information prediction and analysis are not performed in real time, and the reliability of the data is not sufficiently ensured.

[0005] The system according to the embodiment aims to predict and analyze trend information in real time and propose strategies based on highly reliable data. [Means for solving the problem]

[0006] The system according to the embodiment includes a trend prediction unit, a report generation unit, a market response prediction unit, an interactive session unit, a time series data analysis unit, a strategy proposal unit, and a data reliability assurance unit. The trend prediction unit predicts and analyzes trend information in real time. The report generation unit provides reports on future trends. The market response prediction unit predicts market reactions to new products and services. The interactive session unit provides interactive sessions with industry experts. The time series data analysis unit analyzes and predicts time series data. The strategy proposal unit proposes strategies based on the trends. The data reliability assurance unit ensures the reliability of the data. [Effects of the Invention]

[0007] The system according to the embodiment can predict and analyze trend information in real time and propose strategies based on highly reliable data. [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 TrendInsight Pro system according to an embodiment of the present invention is a system that forecasts and analyzes trend information in real time, provides future reports, predicts market reactions, provides interactive sessions with industry experts, analyzes and forecasts time-series data, proposes trend-based strategies, and ensures data reliability. As a result, the TrendInsight Pro system allows users to grasp the latest trend information in real time, predict future market trends, and develop appropriate strategies.

[0029] The TrendInsight Pro system according to an embodiment includes a trend prediction unit, a report generation unit, a market response prediction unit, an interactive session unit, a time-series data analysis unit, a strategy proposal unit, and a data reliability assurance unit. The trend prediction unit predicts and analyzes trend information in real time. For example, the generation AI collects data from social media and news sites and analyzes current trends. The generation AI receives prompts containing instructions for predicting and analyzing trend information, predicts trend information, and outputs analysis results. The report generation unit provides reports on future trends. For example, the generation AI predicts future market trends and consumer behavior based on past data and current trends and provides them in the form of a report. The generation AI receives prompts containing instructions for creating a report and generates a report. The market response prediction unit predicts market responses to new products and services. For example, the generation AI analyzes past market data and consumer feedback to predict how new products and services will be received in the market. The generation AI receives prompts containing instructions for predicting market responses and predicts market responses. The interactive session unit provides interactive sessions with industry experts. Users can directly interact with experts and receive advice on trend information and market trends. For example, users can pose questions about a specific industry to experts and receive answers. The time series data analysis unit analyzes and predicts time series data. For example, the generation AI analyzes sales data and market data to predict future trends. The generation AI receives prompts containing instructions for analyzing and predicting time series data, and then performs the analysis and prediction. The strategy proposal unit proposes strategies based on trends. For example, the generation AI analyzes current trends and, based on those, proposes strategies that companies should take. The generation AI receives prompts containing instructions for making strategic proposals, and then proposes strategies. The data reliability assurance unit uses blockchain technology to ensure the reliability of data. For example, trend information and market data can be recorded on the blockchain to prevent tampering. This allows users to make decisions based on reliable data.As a result, the TrendInsight Pro system according to the embodiment allows users to grasp the latest trend information in real time, predict future market trends, and develop appropriate strategies. For example, small and medium-sized business owners can optimize the timing of new product launches, marketers can plan effective campaigns, investors can use the system to select investment destinations, and policymakers can formulate policies for regional economic development.

[0030] The trend prediction unit can collect data from social media and news sites and analyze trends. The trend prediction unit collects data from social media and news sites and analyzes trends. For example, the generation AI analyzes a user's past search history and browsing history to provide individually customized trend predictions. This allows the latest trend information to be analyzed in real time by collecting data from social media and news sites.

[0031] The report generation unit can predict market trends and consumer behavior based on past data and current trends and provide them in report format. The report generation unit can predict market trends and consumer behavior based on past data and current trends and provide them in report format. For example, when predicting trend information, the generation AI integrates academic papers and patent data to perform a more multifaceted analysis. This allows future market trends and consumer behavior to be predicted based on past data and current trends and provided in report format.

[0032] The market response prediction unit can analyze past market data and consumer feedback to predict how new products and services will be accepted in the market. The market response prediction unit, for example, analyzes past market data and consumer feedback to predict how new products and services will be accepted in the market. For example, it uses an emotion estimation function to analyze the user's emotional state in real time and prioritizes displaying trend information that elicits positive emotions. This makes it possible to predict market acceptance of new products and services based on past market data and consumer feedback.

[0033] The interactive session portion allows a user to pose questions about a specific industry to an expert and receive answers. The interactive session portion allows a user to pose questions about a specific industry to an expert and receive answers. This allows a user to pose questions about a specific industry to an expert and receive answers.

[0034] The time-series data analysis unit analyzes sales data and market data and can predict future trends. The time-series data analysis unit analyzes, for example, sales data and market data and can predict future trends. This makes it possible to predict future trends based on sales data and market data.

[0035] The strategy proposal department can analyze current trends and, based on that, propose strategies that companies should take.The strategy proposal department can analyze current trends and, based on that, propose strategies that companies should take.This makes it possible to propose strategies that companies should take based on current trends.

[0036] The data reliability assurance unit records trend information and market data in a blockchain and can prevent tampering. The data reliability assurance unit, for example, records trend information and market data in a blockchain and can prevent tampering. This prevents tampering of trend information and market data and ensures data reliability.

[0037] The trend prediction unit can analyze a user's past search history and browsing history to provide an individually customized trend prediction. The trend prediction unit can analyze a user's past search history and browsing history to provide an individually customized trend prediction. This makes it possible to provide an individually customized trend prediction based on the user's past behavioral data.

[0038] The trend prediction unit can integrate different data sources and perform more multifaceted analysis. The trend prediction unit can integrate different data sources and perform more multifaceted analysis. By integrating different data sources, more multifaceted trend prediction can be performed.

[0039] The trend prediction unit utilizes multimodal data including audio data and image data, and can analyze trends from visual information and audio information as well.The trend prediction unit utilizes multimodal data including audio data and image data, and can analyze trends from visual information and audio information as well.This makes it possible to analyze trends from visual and audio information by utilizing audio data and image data.

[0040] The trend forecasting department can compare trend information from different industries and regions to discover opportunities for crossover innovation.The trend forecasting department can compare trend information from different industries and regions to discover opportunities for crossover innovation.This allows us to compare trend information from different industries and regions to discover opportunities for crossover innovation.

[0041] The report generation unit can provide a customized future report specialized for the user's industry and business needs. The report generation unit can provide a customized future report specialized for the user's industry and business needs, for example. This makes it possible to provide a customized future report specialized for the user's industry and business needs.

[0042] The report generation unit can incorporate different scenario analyses and present multiple future predictions. The report generation unit, for example, incorporates different scenario analyses and presents multiple future predictions. This allows the report generation unit to incorporate different scenario analyses and present multiple future predictions.

[0043] The report generation unit can automatically translate the future report into different languages ​​and obtain feedback from an international perspective.The report generation unit can automatically translate the future report into different languages ​​and obtain feedback from an international perspective.This allows the future report to be automatically translated into different languages ​​and obtain feedback from an international perspective.

[0044] The report generation unit can convert the report content into visual notes or infographics to make it easier to understand visually.The report generation unit can convert the report content into visual notes or infographics to make it easier to understand visually.Therefore, the report content can be converted into visual notes or infographics to make it easier to understand visually.

[0045] The market response prediction unit can analyze the user's past purchase history and feedback and provide an individually customized market response prediction. The market response prediction unit can analyze the user's past purchase history and feedback and provide an individually customized market response prediction. This makes it possible to provide an individually customized market response prediction based on the user's past purchase history and feedback.

[0046] The market response forecasting unit can integrate different data sources and perform more multifaceted analysis. The market response forecasting unit can, for example, integrate different data sources and perform more multifaceted analysis. In this way, by integrating different data sources, more multifaceted market response forecasts can be performed.

[0047] The market response prediction unit utilizes multimodal data including audio data and image data, and can analyze market responses from visual and auditory information as well.The market response prediction unit utilizes multimodal data including audio data and image data, and can analyze market responses from visual and auditory information as well.This makes it possible to analyze market responses from visual and auditory information as well, by utilizing audio data and image data.

[0048] The market response forecasting unit can compare market responses across different industries and regions to discover opportunities for crossover innovation.The market response forecasting unit can compare market responses across different industries and regions to discover opportunities for crossover innovation.This allows you to compare market responses across different industries and regions to discover opportunities for crossover innovation.

[0049] The interactive session unit can use the generation AI to analyze the expert's answer in real time and suggest the most suitable follow-up questions to the user.The interactive session unit can use the generation AI to analyze the expert's answer in real time and suggest the most suitable follow-up questions to the user.This makes it possible to analyze the expert's answer in real time and suggest the most suitable follow-up questions to the user.

[0050] The interactive session unit can automatically record the contents of the interactive session and generate a summary for later reference. The interactive session unit can, for example, automatically record the contents of the interactive session and generate a summary for later reference. This allows the contents of the interactive session to be automatically recorded and generate a summary for later reference.

[0051] The interactive session unit can automatically translate the interactive session into different languages ​​and obtain feedback from an international perspective. The interactive session unit can, for example, automatically translate the interactive session into different languages ​​and obtain feedback from an international perspective. This allows the interactive session to be automatically translated into different languages ​​and obtain feedback from an international perspective.

[0052] The interactive session unit can convert the content of the interactive session into a visual note or infographic to make it easier to understand visually. The interactive session unit can convert the content of the interactive session into a visual note or infographic to make it easier to understand visually. This makes it easier to understand visually by converting the content of the interactive session into a visual note or infographic.

[0053] The time series data analysis unit can analyze the user's past data and provide an individually customized time series data prediction. The time series data analysis unit, for example, analyzes the user's past data and provides an individually customized time series data prediction. This makes it possible to provide an individually customized time series data prediction based on the user's past data.

[0054] The time series data analysis unit can integrate different data sources and perform more multifaceted analysis.The time series data analysis unit can integrate different data sources and perform more multifaceted analysis.In this way, by integrating different data sources, more multifaceted time series data analysis can be performed.

[0055] The time series data analysis unit utilizes multimodal data including audio data and image data for time series data prediction, and can analyze predictions from visual and auditory information as well.The time series data analysis unit utilizes multimodal data including audio data and image data for time series data prediction, and can analyze predictions from visual and auditory information as well.This makes it possible to analyze time series data predictions from visual and auditory information by utilizing audio data and image data.

[0056] The time series data analysis unit can compare time series data from different industries and regions to discover opportunities for crossover innovation.The time series data analysis unit can compare time series data from different industries and regions to discover opportunities for crossover innovation.This allows you to compare time series data from different industries and regions to discover opportunities for crossover innovation.

[0057] The strategy proposal unit can provide a customized strategy proposal that is specialized for the business needs of the user. The strategy proposal unit, for example, provides a customized strategy proposal that is specialized for the business needs of the user. This makes it possible to provide a customized strategy proposal that is specialized for the business needs of the user.

[0058] The strategy proposal unit can incorporate different scenario analyses and present multiple strategic options.The strategy proposal unit can incorporate different scenario analyses and present multiple strategic options.This allows the strategy proposal unit to incorporate different scenario analyses and present multiple strategic options.

[0059] The strategy proposal unit can automatically translate the strategy proposal into different languages ​​and obtain feedback from an international perspective.The strategy proposal unit can automatically translate the strategy proposal into different languages ​​and obtain feedback from an international perspective.This allows the strategy proposal to be automatically translated into different languages ​​and obtain feedback from an international perspective.

[0060] The strategy proposal unit can convert the content of the strategic proposal into a visual note or infographic to make it easier to understand visually.The strategy proposal unit can convert the content of the strategic proposal into a visual note or infographic to make it easier to understand visually.Therefore, the content of the strategic proposal can be converted into a visual note or infographic to make it easier to understand visually.

[0061] The data reliability assurance unit can use blockchain technology to add a function that not only prevents data tampering but also enables detailed tracking of the origin and history of data.The data reliability assurance unit can use blockchain technology, for example, to add a function that not only prevents data tampering but also enables detailed tracking of the origin and history of data.This not only prevents data tampering but also enables detailed tracking of the origin and history of data.

[0062] The data reliability assurance unit can use blockchain technology to not only ensure the reliability of data but also add a function to precisely manage data sharing and access permissions.The data reliability assurance unit can use blockchain technology, for example, to not only ensure the reliability of data but also add a function to precisely manage data sharing and access permissions.This not only ensures the reliability of data but also allows precise management of data sharing and access permissions.

[0063] The Data Trustworthiness Assurance Department can use blockchain technology to integrate data from different industries and applications and discover opportunities for crossover innovation.The Data Trustworthiness Assurance Department can use blockchain technology to integrate data from different industries and applications and discover opportunities for crossover innovation.This allows data from different industries and applications to be integrated and discover opportunities for crossover innovation.

[0064] The data reliability assurance unit not only ensures the reliability of data by using blockchain technology but also adds functions for visualizing and analyzing data.The data reliability assurance unit not only ensures the reliability of data by using blockchain technology but also adds functions for visualizing and analyzing data.This not only ensures the reliability of data but also adds functions for visualizing and analyzing data.

[0065] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0066] The TrendInsight Pro system can also include a behavioral analysis unit that analyzes user behavioral data and provides individually customized trend predictions. For example, it can analyze a user's past search history and browsing history to provide individually customized trend predictions. This allows for more accurate trend predictions to be provided based on user behavioral data. The behavioral analysis unit can also analyze a user's purchasing history and feedback to provide individually customized market response predictions. Furthermore, the behavioral analysis unit can analyze a user's social media activity to provide individually customized strategy proposals.

[0067] The TrendInsight Pro system can also be equipped with a crossover analysis section that compares trend information from different industries and regions to discover opportunities for crossover innovation. For example, by comparing trend information from different industries, new business opportunities can be discovered. This allows for the discovery of crossover innovation opportunities based on trend information from different industries. The crossover analysis section can also compare trend information from different regions to discover business opportunities specific to each region. Furthermore, the crossover analysis section can integrate different data sources to perform more diversified trend forecasts.

[0068] The TrendInsight Pro system can also be equipped with a multimodal analysis unit that utilizes multimodal data, including audio and image data, to analyze trends from visual and auditory information. For example, audio and image data can be used to analyze trends from visual and auditory information. This allows for more multifaceted trend forecasts based on audio and image data. The multimodal analysis unit can also integrate different data sources to forecast market responses from more diverse perspectives. Furthermore, the multimodal analysis unit can compare trend information from different industries and regions to discover opportunities for crossover innovation.

[0069] The TrendInsight Pro system can also be equipped with a scenario analysis section that incorporates different scenario analyses and presents multiple future forecasts. For example, the system can incorporate different scenario analyses and present multiple future forecasts based on different scenario analyses. The scenario analysis section can also compare trend information from different industries or regions to identify opportunities for crossover innovation. Furthermore, the scenario analysis section can integrate different data sources to provide more diversified trend forecasts.

[0070] The TrendInsight Pro system may further include a session recorder that automatically records the content of an interactive session and generates a summary for later reference. For example, the system may automatically record the content of an interactive session and generate a summary for later reference. This allows for providing a summary based on the content of the interactive session for later reference. The session recorder may also convert the content of the interactive session into visual notes or infographics for easier visual understanding. The session recorder may also automatically translate the interactive session into different languages ​​to obtain feedback from an international perspective.

[0071] The TrendInsight Pro system can also be equipped with a data visualization section that uses blockchain technology to not only ensure data reliability but also add data visualization and analysis functions. This allows data visualization and analysis to be performed based on data reliability. The data visualization section can also integrate data from different industries and applications to discover opportunities for crossover innovation. Furthermore, the data visualization section can also add a function that allows detailed tracking of data origin and history.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The trend prediction unit predicts and analyzes trend information in real time. For example, the generation AI collects data from social media and news sites and analyzes current trends. The generation AI receives prompts containing instructions for predicting and analyzing trend information, predicts trend information, and outputs the analysis results. Step 2: The report generator provides a report on future trends. For example, the generator AI uses historical data and current trends to predict future market trends and consumer behavior and provides it in the form of a report. The generator AI receives prompts containing instructions for creating a report and generates the report. Step 3: The market response prediction component predicts the market response to the new product or service. For example, the generative AI analyzes past market data and consumer feedback to predict how the new product or service will be received by the market. The generative AI receives prompts containing instructions for predicting the market response and then predicts the market response. Step 4: The interactive session section provides interactive sessions with industry experts. Users can directly interact with experts and receive trend information and advice on market trends. For example, users can pose questions about a specific industry to experts and receive answers. Step 5: The time series data analysis unit analyzes and predicts the time series data. For example, the generation AI analyzes sales data and market data to predict future trends. The generation AI receives prompts containing instructions for analyzing and predicting the time series data, and then performs the analysis and prediction. Step 6: The strategy proposal unit proposes a strategy based on trends. For example, the generative AI analyzes current trends and proposes a strategy that the company should take based on that. The generative AI receives prompts containing instructions for making a strategy proposal and proposes a strategy. Step 7: The data reliability assurance unit uses blockchain technology to ensure the reliability of data. For example, trend information and market data are recorded on the blockchain to prevent tampering. This allows users to make decisions based on highly reliable data.

[0074] (Example 2) The TrendInsight Pro system according to an embodiment of the present invention is a system that forecasts and analyzes trend information in real time, provides future reports, predicts market reactions, provides interactive sessions with industry experts, analyzes and forecasts time-series data, proposes trend-based strategies, and ensures data reliability. As a result, the TrendInsight Pro system allows users to grasp the latest trend information in real time, predict future market trends, and develop appropriate strategies.

[0075] The TrendInsight Pro system according to an embodiment includes a trend prediction unit, a report generation unit, a market response prediction unit, an interactive session unit, a time-series data analysis unit, a strategy proposal unit, and a data reliability assurance unit. The trend prediction unit predicts and analyzes trend information in real time. For example, the generation AI collects data from social media and news sites and analyzes current trends. The generation AI receives prompts containing instructions for predicting and analyzing trend information, predicts trend information, and outputs analysis results. The report generation unit provides reports on future trends. For example, the generation AI predicts future market trends and consumer behavior based on past data and current trends and provides them in the form of a report. The generation AI receives prompts containing instructions for creating a report and generates a report. The market response prediction unit predicts market responses to new products and services. For example, the generation AI analyzes past market data and consumer feedback to predict how new products and services will be received in the market. The generation AI receives prompts containing instructions for predicting market responses and predicts market responses. The interactive session unit provides interactive sessions with industry experts. Users can directly interact with experts and receive advice on trend information and market trends. For example, users can pose questions about a specific industry to experts and receive answers. The time series data analysis unit analyzes and predicts time series data. For example, the generation AI analyzes sales data and market data to predict future trends. The generation AI receives prompts containing instructions for analyzing and predicting time series data, and then performs the analysis and prediction. The strategy proposal unit proposes strategies based on trends. For example, the generation AI analyzes current trends and, based on those, proposes strategies that companies should take. The generation AI receives prompts containing instructions for making strategic proposals, and then proposes strategies. The data reliability assurance unit uses blockchain technology to ensure the reliability of data. For example, trend information and market data can be recorded on the blockchain to prevent tampering. This allows users to make decisions based on reliable data.As a result, the TrendInsight Pro system according to the embodiment allows users to grasp the latest trend information in real time, predict future market trends, and develop appropriate strategies. For example, small and medium-sized business owners can optimize the timing of new product launches, marketers can plan effective campaigns, investors can use the system to select investment destinations, and policymakers can formulate policies for regional economic development.

[0076] The trend prediction unit can collect data from social media and news sites and analyze trends. The trend prediction unit collects data from social media and news sites and analyzes trends. For example, the generation AI analyzes a user's past search history and browsing history to provide individually customized trend predictions. This allows the latest trend information to be analyzed in real time by collecting data from social media and news sites.

[0077] The report generation unit can predict market trends and consumer behavior based on past data and current trends and provide them in report format. The report generation unit can predict market trends and consumer behavior based on past data and current trends and provide them in report format. For example, when predicting trend information, the generation AI integrates academic papers and patent data to perform a more multifaceted analysis. This allows future market trends and consumer behavior to be predicted based on past data and current trends and provided in report format.

[0078] The market response prediction unit can analyze past market data and consumer feedback to predict how new products and services will be accepted in the market. The market response prediction unit, for example, analyzes past market data and consumer feedback to predict how new products and services will be accepted in the market. For example, it uses an emotion estimation function to analyze the user's emotional state in real time and prioritizes displaying trend information that elicits positive emotions. This makes it possible to predict market acceptance of new products and services based on past market data and consumer feedback.

[0079] The interactive session portion allows a user to pose questions about a specific industry to an expert and receive answers. The interactive session portion allows a user to pose questions about a specific industry to an expert and receive answers. This allows a user to pose questions about a specific industry to an expert and receive answers.

[0080] The time-series data analysis unit analyzes sales data and market data and can predict future trends. The time-series data analysis unit analyzes, for example, sales data and market data and can predict future trends. This makes it possible to predict future trends based on sales data and market data.

[0081] The strategy proposal department can analyze current trends and, based on that, propose strategies that companies should take.The strategy proposal department can analyze current trends and, based on that, propose strategies that companies should take.This makes it possible to propose strategies that companies should take based on current trends.

[0082] The data reliability assurance unit records trend information and market data in a blockchain and can prevent tampering. The data reliability assurance unit, for example, records trend information and market data in a blockchain and can prevent tampering. This prevents tampering of trend information and market data and ensures data reliability.

[0083] The trend prediction unit can analyze a user's past search history and browsing history to provide an individually customized trend prediction. The trend prediction unit can analyze a user's past search history and browsing history to provide an individually customized trend prediction. This makes it possible to provide an individually customized trend prediction based on the user's past behavioral data.

[0084] The trend prediction unit can integrate different data sources and perform more multifaceted analysis. The trend prediction unit can integrate different data sources and perform more multifaceted analysis. By integrating different data sources, more multifaceted trend prediction can be performed.

[0085] The trend prediction unit can use the emotion estimation function to analyze the emotional state of the user in real time and preferentially display trend information that elicits positive emotions.The trend prediction unit can use, for example, the emotion estimation function to analyze the emotional state of the user in real time and preferentially display trend information that elicits positive emotions.This makes it possible to analyze the emotional state of the user and preferentially display trend information that elicits positive emotions.

[0086] The trend prediction unit utilizes multimodal data including audio data and image data, and can analyze trends from visual information and audio information as well.The trend prediction unit utilizes multimodal data including audio data and image data, and can analyze trends from visual information and audio information as well.This makes it possible to analyze trends from visual and audio information by utilizing audio data and image data.

[0087] The trend forecasting department can compare trend information from different industries and regions to discover opportunities for crossover innovation.The trend forecasting department can compare trend information from different industries and regions to discover opportunities for crossover innovation.This allows us to compare trend information from different industries and regions to discover opportunities for crossover innovation.

[0088] The trend prediction unit uses the emotion estimation function to collect emotional reactions when a user views trend information and can preferentially display trend information that elicits a positive reaction.The trend prediction unit, for example, uses the emotion estimation function to collect emotional reactions when a user views trend information and can preferentially display trend information that elicits a positive reaction.In this way, it is possible to collect emotional reactions from users and preferentially display trend information that elicits a positive reaction.

[0089] The report generation unit can provide a customized future report specialized for the user's industry and business needs. The report generation unit can provide a customized future report specialized for the user's industry and business needs, for example. This makes it possible to provide a customized future report specialized for the user's industry and business needs.

[0090] The report generation unit can incorporate different scenario analyses and present multiple future predictions. The report generation unit, for example, incorporates different scenario analyses and presents multiple future predictions. This allows the report generation unit to incorporate different scenario analyses and present multiple future predictions.

[0091] The report generation unit can use the emotion estimation function to analyze the emotional state of the user and provide preferentially report content that elicits positive emotions. The report generation unit can use the emotion estimation function to analyze the emotional state of the user and provide preferentially report content that elicits positive emotions, for example. This makes it possible to analyze the emotional state of the user and provide preferentially report content that elicits positive emotions.

[0092] The report generation unit can automatically translate the future report into different languages ​​and obtain feedback from an international perspective.The report generation unit can automatically translate the future report into different languages ​​and obtain feedback from an international perspective.This allows the future report to be automatically translated into different languages ​​and obtain feedback from an international perspective.

[0093] The report generation unit can convert the report content into visual notes or infographics to make it easier to understand visually.The report generation unit can convert the report content into visual notes or infographics to make it easier to understand visually.Therefore, the report content can be converted into visual notes or infographics to make it easier to understand visually.

[0094] The report generation unit can use the emotion estimation function to collect the user's emotional reactions to the report content and provide preferentially content that elicits a positive reaction.The report generation unit can use the emotion estimation function to collect the user's emotional reactions to the report content and provide preferentially content that elicits a positive reaction, for example.This allows the report generation unit to collect the user's emotional reactions to the report content and provide preferentially content that elicits a positive reaction.

[0095] The market response prediction unit can analyze the user's past purchase history and feedback and provide an individually customized market response prediction. The market response prediction unit can analyze the user's past purchase history and feedback and provide an individually customized market response prediction. This makes it possible to provide an individually customized market response prediction based on the user's past purchase history and feedback.

[0096] The market response forecasting unit can integrate different data sources and perform more multifaceted analysis. The market response forecasting unit can, for example, integrate different data sources and perform more multifaceted analysis. In this way, by integrating different data sources, more multifaceted market response forecasts can be performed.

[0097] The market response prediction unit can use the emotion estimation function to analyze the emotional state of the user and preferentially provide a market response prediction that elicits positive emotions.The market response prediction unit can, for example, use the emotion estimation function to analyze the emotional state of the user and preferentially provide a market response prediction that elicits positive emotions.This makes it possible to analyze the emotional state of the user and preferentially provide a market response prediction that elicits positive emotions.

[0098] The market response prediction unit utilizes multimodal data including audio data and image data, and can analyze market responses from visual and auditory information as well.The market response prediction unit utilizes multimodal data including audio data and image data, and can analyze market responses from visual and auditory information as well.This makes it possible to analyze market responses from visual and auditory information as well, by utilizing audio data and image data.

[0099] The market response forecasting unit can compare market responses across different industries and regions to discover opportunities for crossover innovation.The market response forecasting unit can compare market responses across different industries and regions to discover opportunities for crossover innovation.This allows you to compare market responses across different industries and regions to discover opportunities for crossover innovation.

[0100] The market response prediction unit can use the emotion estimation function to collect users' emotional reactions to the market response predictions and provide predictions that elicit positive reactions preferentially. The market response prediction unit can use the emotion estimation function to collect users' emotional reactions to the market response predictions and provide predictions that elicit positive reactions preferentially. This allows users' emotional reactions to the market response predictions to be collected and predictions that elicit positive reactions to be provided preferentially.

[0101] The interactive session unit can use the generation AI to analyze the expert's answer in real time and suggest the most suitable follow-up questions to the user.The interactive session unit can use the generation AI to analyze the expert's answer in real time and suggest the most suitable follow-up questions to the user.This makes it possible to analyze the expert's answer in real time and suggest the most suitable follow-up questions to the user.

[0102] The interactive session unit can automatically record the contents of the interactive session and generate a summary for later reference. The interactive session unit can, for example, automatically record the contents of the interactive session and generate a summary for later reference. This allows the contents of the interactive session to be automatically recorded and generate a summary for later reference.

[0103] The interactive session unit can use the emotion estimation function to analyze the emotional state of the user and preferentially provide dialogue content that elicits positive emotions.The interactive session unit can use the emotion estimation function to analyze the emotional state of the user and preferentially provide dialogue content that elicits positive emotions.This makes it possible to analyze the emotional state of the user and preferentially provide dialogue content that elicits positive emotions.

[0104] The interactive session unit can automatically translate the interactive session into different languages ​​and obtain feedback from an international perspective. The interactive session unit can, for example, automatically translate the interactive session into different languages ​​and obtain feedback from an international perspective. This allows the interactive session to be automatically translated into different languages ​​and obtain feedback from an international perspective.

[0105] The interactive session unit can convert the content of the interactive session into a visual note or infographic to make it easier to understand visually. The interactive session unit can convert the content of the interactive session into a visual note or infographic to make it easier to understand visually. This makes it easier to understand visually by converting the content of the interactive session into a visual note or infographic.

[0106] The interactive session unit can use the emotion estimation function to collect the user's emotional reactions to the interactive session and preferentially provide content that elicits a positive reaction.The interactive session unit can use the emotion estimation function to collect the user's emotional reactions to the interactive session and preferentially provide content that elicits a positive reaction, for example.This makes it possible to collect the user's emotional reactions to the interactive session and preferentially provide content that elicits a positive reaction.

[0107] The time series data analysis unit can analyze the user's past data and provide an individually customized time series data prediction. The time series data analysis unit, for example, analyzes the user's past data and provides an individually customized time series data prediction. This makes it possible to provide an individually customized time series data prediction based on the user's past data.

[0108] The time series data analysis unit can integrate different data sources and perform more multifaceted analysis.The time series data analysis unit can integrate different data sources and perform more multifaceted analysis.In this way, by integrating different data sources, more multifaceted time series data analysis can be performed.

[0109] The time-series data analysis unit can use the emotion estimation function to analyze the emotional state of the user and preferentially provide time-series data predictions that elicit positive emotions.The time-series data analysis unit can use the emotion estimation function to analyze the emotional state of the user and preferentially provide time-series data predictions that elicit positive emotions.This makes it possible to analyze the emotional state of the user and preferentially provide time-series data predictions that elicit positive emotions.

[0110] The time series data analysis unit utilizes multimodal data including audio data and image data for time series data prediction, and can analyze predictions from visual and auditory information as well.The time series data analysis unit utilizes multimodal data including audio data and image data for time series data prediction, and can analyze predictions from visual and auditory information as well.This makes it possible to analyze time series data predictions from visual and auditory information by utilizing audio data and image data.

[0111] The time series data analysis unit can compare time series data from different industries and regions to discover opportunities for crossover innovation.The time series data analysis unit can compare time series data from different industries and regions to discover opportunities for crossover innovation.This allows you to compare time series data from different industries and regions to discover opportunities for crossover innovation.

[0112] The time-series data analysis unit uses the emotion estimation function to collect users' emotional reactions to the time-series data predictions and can provide preferentially predictions that elicit positive reactions. The time-series data analysis unit uses, for example, the emotion estimation function to collect users' emotional reactions to the time-series data predictions and can provide preferentially predictions that elicit positive reactions. This makes it possible to collect users' emotional reactions to the time-series data predictions and provide preferentially predictions that elicit positive reactions.

[0113] The strategy proposal unit can provide a customized strategy proposal that is specialized for the business needs of the user. The strategy proposal unit, for example, provides a customized strategy proposal that is specialized for the business needs of the user. This makes it possible to provide a customized strategy proposal that is specialized for the business needs of the user.

[0114] The strategy proposal unit can incorporate different scenario analyses and present multiple strategic options.The strategy proposal unit can incorporate different scenario analyses and present multiple strategic options.This allows the strategy proposal unit to incorporate different scenario analyses and present multiple strategic options.

[0115] The strategy proposal unit can use the emotion estimation function to analyze the emotional state of the user and provide preferentially strategy proposals that elicit positive emotions. The strategy proposal unit can, for example, use the emotion estimation function to analyze the emotional state of the user and provide preferentially strategy proposals that elicit positive emotions. This makes it possible to analyze the emotional state of the user and provide preferentially strategy proposals that elicit positive emotions.

[0116] The strategy proposal unit can automatically translate the strategy proposal into different languages ​​and obtain feedback from an international perspective.The strategy proposal unit can automatically translate the strategy proposal into different languages ​​and obtain feedback from an international perspective.This allows the strategy proposal to be automatically translated into different languages ​​and obtain feedback from an international perspective.

[0117] The strategy proposal unit can convert the content of the strategic proposal into a visual note or infographic to make it easier to understand visually.The strategy proposal unit can convert the content of the strategic proposal into a visual note or infographic to make it easier to understand visually.Therefore, the content of the strategic proposal can be converted into a visual note or infographic to make it easier to understand visually.

[0118] The strategy proposal unit can use the emotion estimation function to collect the user's emotional reactions to the strategy proposals and provide preferentially proposals that elicit a positive reaction. The strategy proposal unit can use the emotion estimation function to collect the user's emotional reactions to the strategy proposals and provide preferentially proposals that elicit a positive reaction, for example. This makes it possible to collect the user's emotional reactions to the strategy proposals and provide preferentially proposals that elicit a positive reaction.

[0119] The data reliability assurance unit can use blockchain technology to add a function that not only prevents data tampering but also enables detailed tracking of the origin and history of data.The data reliability assurance unit can use blockchain technology, for example, to add a function that not only prevents data tampering but also enables detailed tracking of the origin and history of data.This not only prevents data tampering but also enables detailed tracking of the origin and history of data.

[0120] The data reliability assurance unit can use blockchain technology to not only ensure the reliability of data but also add a function to precisely manage data sharing and access permissions.The data reliability assurance unit can use blockchain technology, for example, to not only ensure the reliability of data but also add a function to precisely manage data sharing and access permissions.This not only ensures the reliability of data but also allows precise management of data sharing and access permissions.

[0121] The data reliability assurance unit uses the emotion estimation function to analyze the emotion a user has toward the reliability of data and can provide a data management method that elicits positive emotions.The data reliability assurance unit uses, for example, the emotion estimation function to analyze the emotion a user has toward the reliability of data and can provide a data management method that elicits positive emotions.This makes it possible to provide a data management method that analyzes the emotion a user has toward the reliability of data and elicits positive emotions.

[0122] The Data Trustworthiness Assurance Department can use blockchain technology to integrate data from different industries and applications and discover opportunities for crossover innovation.The Data Trustworthiness Assurance Department can use blockchain technology to integrate data from different industries and applications and discover opportunities for crossover innovation.This allows data from different industries and applications to be integrated and discover opportunities for crossover innovation.

[0123] The data reliability assurance unit not only ensures the reliability of data by using blockchain technology but also adds functions for visualizing and analyzing data.The data reliability assurance unit not only ensures the reliability of data by using blockchain technology but also adds functions for visualizing and analyzing data.This not only ensures the reliability of data but also adds functions for visualizing and analyzing data.

[0124] The data reliability assurance unit uses the emotion estimation function to collect users' emotional reactions to the reliability of data, thereby providing a data management method that elicits positive reactions.The data reliability assurance unit uses, for example, the emotion estimation function to collect users' emotional reactions to the reliability of data, thereby providing a data management method that elicits positive reactions.This makes it possible to provide a data management method that collects users' emotional reactions to the reliability of data, thereby eliciting positive reactions.

[0125] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0126] The TrendInsight Pro system can also include a behavioral analysis unit that analyzes user behavioral data and provides individually customized trend predictions. For example, it can analyze a user's past search history and browsing history to provide individually customized trend predictions. This allows for more accurate trend predictions to be provided based on user behavioral data. The behavioral analysis unit can also analyze a user's purchasing history and feedback to provide individually customized market response predictions. Furthermore, the behavioral analysis unit can analyze a user's social media activity to provide individually customized strategy proposals.

[0127] The TrendInsight Pro system can also be equipped with a crossover analysis section that compares trend information from different industries and regions to discover opportunities for crossover innovation. For example, by comparing trend information from different industries, new business opportunities can be discovered. This allows for the discovery of crossover innovation opportunities based on trend information from different industries. The crossover analysis section can also compare trend information from different regions to discover business opportunities specific to each region. Furthermore, the crossover analysis section can integrate different data sources to perform more diversified trend forecasts.

[0128] The TrendInsight Pro system can also include an emotion analysis unit that uses the emotion estimation function to analyze a user's emotional state in real time and prioritize displaying trend information that elicits positive emotions. For example, the system can collect the user's emotional reactions when viewing trend information and prioritize displaying trend information that elicits positive reactions. This allows the system to analyze the user's emotional state and prioritize displaying trend information that elicits positive emotions. The emotion analysis unit can also provide individually customized strategy proposals based on the user's emotional state. Furthermore, the emotion analysis unit can provide market reaction predictions that elicit positive emotions based on the user's emotional state.

[0129] The TrendInsight Pro system can also be equipped with a multimodal analysis unit that utilizes multimodal data, including audio and image data, to analyze trends from visual and auditory information. For example, audio and image data can be used to analyze trends from visual and auditory information. This allows for more multifaceted trend forecasts based on audio and image data. The multimodal analysis unit can also integrate different data sources to forecast market responses from more diverse perspectives. Furthermore, the multimodal analysis unit can compare trend information from different industries and regions to discover opportunities for crossover innovation.

[0130] The TrendInsight Pro system may further include an emotion reporting unit that uses the emotion estimation function to analyze the user's emotional state and prioritize report content that elicits positive emotions. For example, the system may analyze the user's emotional state and prioritize report content that elicits positive emotions. This allows report content that elicits positive emotions to be provided based on the user's emotional state. The emotion reporting unit may also provide individually customized future reports based on the user's emotional state. Furthermore, the emotion reporting unit may provide market reaction forecasts that elicit positive emotions based on the user's emotional state.

[0131] The TrendInsight Pro system can also be equipped with a scenario analysis section that incorporates different scenario analyses and presents multiple future forecasts. For example, the system can incorporate different scenario analyses and present multiple future forecasts based on different scenario analyses. The scenario analysis section can also compare trend information from different industries or regions to identify opportunities for crossover innovation. Furthermore, the scenario analysis section can integrate different data sources to provide more diversified trend forecasts.

[0132] The TrendInsight Pro system may further include an emotional strategy unit that uses the emotion estimation function to analyze a user's emotional state and prioritize providing strategy proposals that elicit positive emotions. For example, the system may analyze a user's emotional state and prioritize providing strategy proposals that elicit positive emotions. This allows for strategy proposals that elicit positive emotions to be provided based on the user's emotional state. The emotional strategy unit may also provide individually customized strategy proposals based on the user's emotional state. Furthermore, the emotional strategy unit may provide market reaction predictions that elicit positive emotions based on the user's emotional state.

[0133] The TrendInsight Pro system may further include a session recorder that automatically records the content of an interactive session and generates a summary for later reference. For example, the system may automatically record the content of an interactive session and generate a summary for later reference. This allows for providing a summary based on the content of the interactive session for later reference. The session recorder may also convert the content of the interactive session into visual notes or infographics for easier visual understanding. The session recorder may also automatically translate the interactive session into different languages ​​to obtain feedback from an international perspective.

[0134] The TrendInsight Pro system may further include an emotional session unit that uses an emotion estimation function to collect users' emotional reactions to an interactive session and prioritize providing content that elicits positive reactions. For example, the system may collect users' emotional reactions to an interactive session and prioritize providing content that elicits positive reactions. This allows for providing dialogue content that elicits positive reactions based on the users' emotional reactions. The emotional session unit may also provide individually customized dialogue content based on the user's emotional state. Furthermore, the emotional session unit may provide market reaction predictions that elicit positive emotions based on the user's emotional state.

[0135] The TrendInsight Pro system can also be equipped with a data visualization section that uses blockchain technology to not only ensure data reliability but also add data visualization and analysis functions. This allows data visualization and analysis to be performed based on data reliability. The data visualization section can also integrate data from different industries and applications to discover opportunities for crossover innovation. Furthermore, the data visualization section can also add a function that allows detailed tracking of data origin and history.

[0136] The processing flow of the second embodiment will be briefly explained below.

[0137] Step 1: The trend prediction unit predicts and analyzes trend information in real time. For example, the generation AI collects data from social media and news sites and analyzes current trends. The generation AI receives prompts containing instructions for predicting and analyzing trend information, predicts trend information, and outputs the analysis results. Step 2: The report generator provides a report on future trends. For example, the generator AI uses historical data and current trends to predict future market trends and consumer behavior and provides it in the form of a report. The generator AI receives prompts containing instructions for creating a report and generates the report. Step 3: The market response prediction component predicts the market response to the new product or service. For example, the generative AI analyzes past market data and consumer feedback to predict how the new product or service will be received by the market. The generative AI receives prompts containing instructions for predicting the market response and then predicts the market response. Step 4: The interactive session section provides interactive sessions with industry experts. Users can directly interact with experts and receive trend information and advice on market trends. For example, users can pose questions about a specific industry to experts and receive answers. Step 5: The time series data analysis unit analyzes and predicts the time series data. For example, the generation AI analyzes sales data and market data to predict future trends. The generation AI receives prompts containing instructions for analyzing and predicting the time series data, and then performs the analysis and prediction. Step 6: The strategy proposal unit proposes a strategy based on trends. For example, the generative AI analyzes current trends and proposes a strategy that the company should take based on that. The generative AI receives prompts containing instructions for making a strategy proposal and proposes a strategy. Step 7: The data reliability assurance unit uses blockchain technology to ensure the reliability of data. For example, trend information and market data are recorded on the blockchain to prevent tampering. This allows users to make decisions based on highly reliable data.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0142] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] Fig. 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.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0151] 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.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0155] The data processing system 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.

[0156] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0157] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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 AI 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.

[0170] 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.

[0171] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0172] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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 AI 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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).

[0191] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0192] 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."

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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]

[0205] 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 trend forecasting unit that forecasts and analyzes trend information in real time; a report generator that provides reports on future trends; a market response forecasting department that forecasts market reactions to new products and services; The interactive session section will provide interactive sessions with industry experts. a time series data analysis unit that analyzes and predicts time series data; A strategy proposal department that proposes strategies based on trends; a data reliability assurance unit that ensures the reliability of data A system characterized by:

2. The trend prediction unit Utilizing multimodal data, including audio and image data, the trends will also be analyzed using visual and auditory information.

2. The system of claim 1.

3. The report generation unit Providing customized future reports specific to your industry and business needs 2. The system of claim 1.

4. The market response prediction unit Analyze users' past purchase history and feedback to provide individually customized market response forecasts 2. The system of claim 1.

5. The interactive session unit includes: Generative AI is used to analyze expert answers in real time and suggest optimal follow-up questions to the user.

2. The system of claim 1.

6. The time series data analysis unit Analyzes your historical data and provides personalized time series forecasts 2. The system of claim 1.

7. The strategy proposal unit Providing customized strategy proposals specific to your business needs 2. The system of claim 1.

8. The data reliability assurance unit To provide a data management method that analyzes the feelings that users have about the reliability of the data and elicits positive feelings.

2. The system of claim 1.

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