system

Abstract

The system according to this embodiment aims to improve the accuracy of future business predictions by utilizing real-time data and user submissions. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an absorption unit, and a collaboration unit. The collection unit collects real-time data. The analysis unit analyzes the data collected by the collection unit and makes future business predictions. The absorption unit absorbs user submissions and adds them to the training data. The collaboration unit collaborates with experts to improve prediction accuracy.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving user speech, adding the user speech to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot speech in response to the user speech.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the utilization of real-time data and the absorption of user contributions in predicting the future of business are not sufficiently carried out, and there is room for improvement in improving the prediction accuracy.

[0005] The system according to the embodiment aims to utilize real-time data and user contributions to improve the accuracy of predicting the future of business.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, an absorption unit, and a collaboration unit. The data collection unit collects real-time data. The analysis unit analyzes the data collected by the data collection unit and makes future business predictions. The absorption unit absorbs user submissions and adds them to the training data. The collaboration unit collaborates with experts to improve prediction accuracy. [Effects of the Invention]

[0007] The system according to this embodiment can improve the accuracy of future business predictions by utilizing real-time data and user submissions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The future prediction system according to an embodiment of the present invention is a system that utilizes generative AI (particularly LLM) to predict the future of a business and support internal and external decision-making. The future prediction system collects real-time data, and the generative AI analyzes this data to make future predictions. Next, it absorbs user-submitted content and adds it to the training data to improve prediction accuracy. Furthermore, it collaborates with experts to increase the accuracy of predictions. For example, the future prediction system collects real-time data. This data includes the latest trend information, market trends, and economic indicators. For example, fluctuations in stock prices and consumer purchasing behavior are collected as data. This data is input into the generative AI. Next, the generative AI analyzes the collected data and makes future business predictions. The generative AI analyzes past data and real-time data together to predict future trends and market movements. For example, it predicts sales forecasts for the next quarter and market acceptance of new products. Furthermore, user-submitted content is also absorbed by the generative AI and added to the training data. This allows the generative AI to always make predictions based on the latest information. For example, market impressions and opinions submitted by users are incorporated into the generative AI, improving prediction accuracy. Furthermore, collaborating with experts can further improve the accuracy of predictions. Experts verify the predictions made by the generative AI and make corrections as needed. This improves the reliability of the predictions and enhances the quality of decision-making. The future prediction system is characterized by its scalability and high accuracy. By leveraging the characteristics of generative AI, future predictions can be performed on a large scale. For example, predictions can be made simultaneously across multiple industries and regions. This allows the system to lead the evolution of industries and support business success. As a result, the future prediction system can make highly accurate predictions about the future of businesses and support decision-making both inside and outside the company.

[0029] The future prediction system according to this embodiment comprises a data collection unit, an analysis unit, an absorption unit, and a linking unit. The data collection unit collects real-time data. Real-time data includes, but is not limited to, the latest trend information, market trends, and economic indicators. The data collection unit also collects, for example, sensor data. The data collection unit can also collect social media posts. Furthermore, the data collection unit can also collect economic indicators. For example, the data collection unit collects stock price fluctuation data. Social media post data includes user opinions and impressions. Economic indicator data includes indicators such as GDP and unemployment rates. The analysis unit uses generative AI to analyze the data collected by the data collection unit and make future business predictions. The analysis unit, for example, combines historical data and real-time data for analysis. Furthermore, the analysis unit can use generative AI to predict future trends and market movements. Furthermore, the analysis unit can use generative AI to predict sales forecasts for the next quarter and market acceptance of new products. For example, the analysis unit combines historical sales data and real-time market trend data to make sales forecasts for the next quarter. The generative AI uses text generation AI (e.g., LLM) to analyze historical and real-time data. Based on historical and real-time data, the generative AI predicts future trends and market movements. The absorption unit absorbs user submissions and adds them to the training data. For example, the absorption unit incorporates user-submitted market opinions and feedback into the generative AI. The absorption unit can also analyze user submissions and add them to the training data. Furthermore, the absorption unit can improve the prediction accuracy of the generative AI based on user submissions. For example, the absorption unit incorporates user-submitted market opinions and feedback into the generative AI and adds them to the training data. User submissions include, for example, reviews, comments, and feedback. The collaboration unit improves prediction accuracy by collaborating with experts. For example, the collaboration unit allows experts to verify the generative AI's predictions. The collaboration unit also allows experts to modify the generative AI's predictions. Furthermore, the collaboration unit can improve the prediction accuracy of the generative AI based on expert opinions. For example, the collaboration unit allows experts to verify the generative AI's predictions and modify them as needed.Experts include, for example, economists, marketing experts, and data scientists. This allows the future prediction system according to the embodiment to make highly accurate predictions about the future of the business and support internal and external decision-making.

[0030] The data collection unit collects real-time data. Real-time data includes, but is not limited to, the latest trend information, market trends, and economic indicators. The data collection unit also collects sensor data, such as environmental data including temperature, humidity, vibration, and light intensity. This data is collected in real time and transmitted to a central database. The data collection unit can also collect social media posts, which include user opinions and feedback. For example, it can collect posts related to specific hashtags or keywords from a platform. Furthermore, the data collection unit can collect economic indicators, such as GDP and unemployment rates. For example, it can collect publicly available data from government agencies and economic research institutions to obtain real-time updated economic indicators. The data collection unit builds a comprehensive database by collecting and centrally managing information from these diverse data sources. This allows the data collection unit to efficiently collect real-time data and make it accessible to the analysis, absorption, and collaboration units. The data collection unit can also adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. For example, if a particular market trend changes rapidly, the data collection frequency can be increased to update the data in real time. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit uses generative AI to analyze data collected by the data collection unit and make future business predictions. For example, the analysis unit combines historical data with real-time data for analysis. Specifically, it combines historical sales data and market trend data with real-time trend information and economic indicators for analysis. The generative AI uses text generation AI (e.g., LLM) to analyze historical data and real-time data. The generative AI utilizes natural language processing technology to analyze text data and predict future trends and market movements. For example, the generative AI combines historical sales data with real-time market trend data to make sales forecasts for the next quarter. The generative AI can also analyze social media posts and predict the market acceptance of new products based on user opinions and feedback. Furthermore, the generative AI can analyze economic indicator data and predict future economic trends. For example, it can predict future economic growth rates and employment conditions based on GDP and unemployment rate data. Based on these analysis results, the analysis unit makes future business predictions, such as forecasting sales for the next quarter and the market acceptance of new products. This allows the analysis unit to quickly and accurately analyze collected data and make highly accurate predictions about the future of the business. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions and time periods based on historical market trend data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The absorption unit absorbs user-submitted content and adds it to the training data. Specifically, it incorporates user-submitted market opinions and feedback into the generating AI. For example, it collects reviews, comments, and feedback posted by users on social media and adds them to the generating AI's training data. The absorption unit analyzes this content and uses it as data to improve the generating AI's prediction accuracy. For example, by analyzing user-submitted opinions and feedback on a new product and incorporating them into the generating AI, it can more accurately predict the market acceptance of the new product. The absorption unit can also improve the generating AI's prediction accuracy based on user-submitted content. For example, by incorporating user-submitted market opinions and feedback into the generating AI and adding it to the training data, the generating AI can make predictions based on a wider range of data. This allows the absorption unit to effectively utilize user feedback and improve the generating AI's prediction accuracy. Furthermore, by collecting user-submitted content in real time and adding it to the generating AI's training data, the absorption unit can always make predictions based on the latest information. For example, by quickly collecting reviews and comments posted by users immediately after the launch of a new product and incorporating them into the generating AI, it can make rapid and accurate predictions of market acceptance. This allows the absorption unit to quickly collect user feedback and improve the prediction accuracy of the generating AI.

[0033] The Collaboration Department improves prediction accuracy by working with experts. Specifically, experts verify the predictions of the Generative AI. For example, experts such as economists, marketing experts, and data scientists review the prediction results of the Generative AI and evaluate their accuracy and validity. The Collaboration Department can also have experts modify the predictions of the Generative AI. For example, experts can provide insights into specific market trends or economic indicators based on the prediction results of the Generative AI and modify the prediction results. Furthermore, the Collaboration Department can improve the prediction accuracy of the Generative AI based on expert opinions. For example, experts can improve the prediction accuracy of the Generative AI by verifying the predictions of the Generative AI and making modifications as needed. In this way, the Collaboration Department can improve the prediction accuracy of the Generative AI by utilizing the expertise of experts. In addition, the Collaboration Department can improve the training data of the Generative AI through collaboration with experts. For example, by adding new data and insights provided by experts to the training data of the Generative AI, the Generative AI can make predictions based on more diverse data. In this way, the Collaboration Department can improve the prediction accuracy of the Generative AI through collaboration with experts. Furthermore, the collaboration department can continuously improve the prediction accuracy of the generative AI through regular meetings and workshops with experts. For example, experts can continuously improve the prediction accuracy of the generative AI by regularly reviewing its prediction results and evaluating their accuracy and validity. In this way, the collaboration department can enhance the prediction accuracy of the generative AI through collaboration with experts and make highly accurate predictions about the future of the business.

[0034] The analysis unit can analyze historical data and real-time data to predict future trends and market movements. For example, the analysis unit can analyze historical sales data and real-time market trend data. Furthermore, the analysis unit can use generative AI to predict future trends and market movements. In addition, the analysis unit can use generative AI to predict sales forecasts for the next quarter and the market acceptance of new products. For example, the analysis unit combines historical sales data and real-time market trend data to forecast sales for the next quarter. The generative AI uses text generation AI (e.g., LLM) to analyze historical data and real-time data. Based on historical data and real-time data, the generative AI predicts future trends and market movements. This combination of historical and real-time data enables more accurate future predictions.

[0035] The absorption unit can incorporate user-submitted market opinions and feedback into the generating AI and add it to the training data. For example, the absorption unit can incorporate user-submitted market opinions and feedback into the generating AI. The absorption unit can also analyze user submissions and add them to the training data. Furthermore, the absorption unit can improve the prediction accuracy of the generating AI based on user submissions. For example, the absorption unit can incorporate user-submitted market opinions and feedback into the generating AI and add it to the training data. User submissions include, for example, reviews, comments, and feedback. By adding user submissions to the training data, prediction accuracy is improved.

[0036] The collaboration department allows experts to verify the predictions of the generative AI and make corrections as needed. For example, experts can verify the predictions of the generative AI. Furthermore, experts can make corrections to the predictions of the generative AI. In addition, the collaboration department can improve the accuracy of the generative AI's predictions based on expert opinions. For example, experts can verify the predictions of the generative AI and make corrections as needed. These experts include, for example, economists, marketing experts, and data scientists. This ensures that the reliability of the predictions is improved through expert verification and correction.

[0037] The data collection unit can collect real-time data such as the latest trend information, market trends, and economic indicators. For example, the data collection unit can collect the latest trend information. It can also collect market trends. Furthermore, the data collection unit can collect economic indicators. For example, the data collection unit can collect news articles and social media posts. Market trend data includes sales data and consumer behavior data. Economic indicator data includes GDP, unemployment rate, and inflation rate. By collecting real-time data such as the latest trend information, market trends, and economic indicators, more accurate future predictions become possible.

[0038] The analysis unit can predict sales forecasts for the next quarter and the market acceptance of new products. For example, the analysis unit can forecast sales for the next quarter. It can also predict the market acceptance of new products. Furthermore, the analysis unit can use generative AI to predict sales forecasts for the next quarter and the market acceptance of new products. For example, the analysis unit combines historical sales data with real-time market trend data to forecast sales for the next quarter. The generative AI uses text generation AI (e.g., LLM) to analyze historical data and real-time data. Based on historical data and real-time data, the generative AI predicts sales forecasts for the next quarter and the market acceptance of new products. This supports business decision-making by predicting sales forecasts for the next quarter and the market acceptance of new products.

[0039] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of highly reliable data. For example, the data collection unit can prioritize the collection of data from highly reliable sources based on past reliability evaluations of the data sources. The data collection unit can also evaluate the expertise and past performance of data providers and prioritize the collection of data from highly reliable providers. Furthermore, the data collection unit can evaluate the frequency and recency of data updates and prioritize the collection of the latest data. For example, the data collection unit can prioritize the collection of data from highly reliable sources based on past reliability evaluations of the data sources. The data collection unit can also evaluate the expertise and past performance of data providers and prioritize the collection of data from highly reliable providers. The data collection unit can also evaluate the frequency and recency of data updates and prioritize the collection of the latest data. In this way, by evaluating the reliability of the data, highly reliable data can be prioritized for collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0040] The data collection unit can prioritize the collection of the latest data, taking into account the frequency of data updates. For example, the data collection unit prioritizes the collection of data from sources with a high data update frequency. The data collection unit can also prioritize the collection of data that is updated in real time, reflecting the latest market trends. Furthermore, the data collection unit can monitor regularly updated data sources and automatically collect the latest data. For example, the data collection unit prioritizes the collection of data from sources with a high data update frequency. The data collection unit can also prioritize the collection of data that is updated in real time, reflecting the latest market trends. The data collection unit can also monitor regularly updated data sources and automatically collect the latest data. This allows for the priority collection of the latest data by considering the frequency of data updates. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of market trends and economic indicators related to that region. Furthermore, if the user is on the move, the data collection unit can also collect highly relevant data in real time based on their current location. Additionally, if the user is interested in a specific country or region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is in a specific region, the data collection unit will prioritize the collection of market trends and economic indicators related to that region. If the user is on the move, the data collection unit can also collect highly relevant data in real time based on their current location. If the user is interested in a specific country or region, the data collection unit can also prioritize the collection of data related to that region. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit prioritizes collecting data related to topics the user has shown interest in on social media. The data collection unit can also analyze posts from experts and influencers the user follows and collect relevant data. Furthermore, the data collection unit can analyze the activities of social media groups and communities the user participates in and collect relevant data. For example, the data collection unit prioritizes collecting data related to topics the user has shown interest in on social media. The data collection unit can also analyze posts from experts and influencers the user follows and collect relevant data. The data collection unit can also analyze the activities of social media groups and communities the user participates in and collect relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data to provide specific insights. Conversely, the analysis unit can perform a concise analysis on less important data, presenting only the key points. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data to perform efficient analysis. For example, the analysis unit can perform a detailed analysis on highly important data to provide specific insights. Conversely, the analysis unit can perform a concise analysis on less important data, presenting only the key points. The analysis unit can adjust the depth and scope of the analysis according to the importance of the data.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm using an economic model to economic data. It can also apply a trend analysis algorithm to market trend data. Furthermore, it can apply a behavioral analysis algorithm to consumer behavior data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0045] The analysis unit can prioritize analysis based on the timing of data submission during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. It can also prioritize the analysis of regularly submitted data to grasp ongoing trends. Furthermore, the analysis unit can prioritize the analysis of data with high urgency to support rapid decision-making. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. It can also prioritize the analysis of regularly submitted data to grasp ongoing trends. It can also prioritize the analysis of data with high urgency to support rapid decision-making. This allows for rapid decision-making by prioritizing analysis based on the timing of data submission. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide important insights early. Alternatively, the analysis unit can postpone the analysis of less relevant data to perform efficient analysis. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data to provide optimal analysis results. For example, the analysis unit can prioritize the analysis of highly relevant data to provide important insights early. Alternatively, the analysis unit can postpone the analysis of less relevant data to perform efficient analysis. The analysis unit can dynamically adjust the order of analysis according to the relevance of the data to provide optimal analysis results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0047] The absorption unit can evaluate the reliability of the posted content during absorption and prioritize the absorption of highly reliable posts. For example, the absorption unit can prioritize the absorption of highly reliable posts based on the poster's past reliability evaluation. The absorption unit can also evaluate the specificity and evidence of the posted content and prioritize the absorption of highly reliable posts. Furthermore, the absorption unit can evaluate the update frequency and recency of the posted content and prioritize the absorption of the latest posts. For example, the absorption unit can prioritize the absorption of highly reliable posts based on the poster's past reliability evaluation. The absorption unit can also evaluate the specificity and evidence of the posted content and prioritize the absorption of highly reliable posts. The absorption unit can also evaluate the update frequency and recency of the posted content and prioritize the absorption of the latest posts. In this way, by evaluating the reliability of the posted content, highly reliable posts can be prioritized for absorption. Some or all of the above processing in the absorption unit may be performed using AI, for example, or without using AI.

[0048] The absorption unit can apply different absorption algorithms depending on the category of the posted content during absorption. For example, for posts related to economics, the absorption unit can apply an absorption algorithm using an economic model. The absorption unit can also apply a trend analysis algorithm to posts related to market trends. Furthermore, the absorption unit can apply a behavioral analysis algorithm to posts related to consumer behavior. For example, the absorption unit can apply an absorption algorithm using an economic model to posts related to economics. The absorption unit can also apply a trend analysis algorithm to posts related to market trends. The absorption unit can also apply a behavioral analysis algorithm to posts related to consumer behavior. By applying different absorption algorithms depending on the category of the posted content, more appropriate content can be absorbed. Some or all of the above processing in the absorption unit may be performed using AI, for example, or without using AI.

[0049] The absorption unit can prioritize absorbing posts that are highly relevant, taking into account the user's geographical location information during absorption. For example, if the user is in a specific region, the absorption unit will prioritize absorbing posts related to that region. Furthermore, if the user is on the move, the absorption unit can also absorb highly relevant posts in real time based on their current location. Additionally, if the user is interested in a specific country or region, the absorption unit can prioritize absorbing posts related to that region. For example, if the user is in a specific region, the absorption unit will prioritize absorbing posts related to that region. If the user is on the move, the absorption unit can also absorb highly relevant posts in real time based on their current location. If the user is interested in a specific country or region, the absorption unit can also prioritize absorbing posts related to that region. This allows for the prioritization of highly relevant posts by considering the user's geographical location information. Some or all of the above processing in the absorption unit may be performed using, for example, AI, or not.

[0050] The absorption unit can analyze the user's social media activity and absorb relevant posts during the absorption process. For example, the absorption unit prioritizes absorbing posts related to topics the user has shown interest in on social media. The absorption unit can also analyze posts from experts and influencers the user follows and absorb relevant posts. Furthermore, the absorption unit can analyze the activities of social media groups and communities the user participates in and absorb relevant posts. For example, the absorption unit prioritizes absorbing posts related to topics the user has shown interest in on social media. The absorption unit can also analyze posts from experts and influencers the user follows and absorb relevant posts. The absorption unit can also analyze the activities of social media groups and communities the user participates in and absorb relevant posts. This allows the absorption of relevant posts by analyzing the user's social media activity. Some or all of the above processing in the absorption unit may be performed using AI, for example, or without AI.

[0051] The collaboration unit can select the optimal collaboration method by referring to the past evaluation history of experts during collaboration. For example, the collaboration unit prioritizes collaboration with highly reliable experts based on their past evaluation history. The collaboration unit can also evaluate the past performance of experts and select the optimal collaboration method. Furthermore, the collaboration unit can analyze the past feedback of experts and adjust the collaboration method. For example, the collaboration unit prioritizes collaboration with highly reliable experts based on their past evaluation history. The collaboration unit can also evaluate the past performance of experts and select the optimal collaboration method. The collaboration unit can also analyze the past feedback of experts and adjust the collaboration method. This allows the collaboration unit to select the optimal collaboration method by referring to the past evaluation history of experts. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without using AI.

[0052] The collaboration unit can apply different collaboration algorithms depending on the expert's field of expertise during collaboration. For example, the collaboration unit can apply a collaboration algorithm using economic models to economic experts. It can also apply a trend analysis algorithm to market trend experts. Furthermore, it can apply a behavioral analysis algorithm to consumer behavior experts. This allows for more appropriate collaboration by applying different collaboration algorithms depending on the expert's field of expertise. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI.

[0053] The collaboration unit can select the optimal collaboration method by considering the geographical location information of experts during collaboration. For example, if the expert is nearby, the collaboration unit will prioritize face-to-face collaboration. If the expert is far away, the collaboration unit may also prioritize online collaboration. Furthermore, the collaboration unit can select the optimal collaboration method based on the geographical location information of experts. For example, if the expert is nearby, the collaboration unit will prioritize face-to-face collaboration. If the expert is far away, the collaboration unit may also prioritize online collaboration. The collaboration unit can also select the optimal collaboration method based on the geographical location information of experts. This allows the optimal collaboration method to be selected by considering the geographical location information of experts. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI.

[0054] The collaboration unit can analyze the social media activities of experts during the collaboration process and propose relevant collaboration methods. For example, the collaboration unit can propose collaboration methods related to topics that experts have shown interest in on social media. The collaboration unit can also propose collaboration with other experts that the expert follows. Furthermore, the collaboration unit can analyze the activities of social media groups and communities in which the expert participates and propose relevant collaboration methods. For example, the collaboration unit can propose collaboration methods related to topics that experts have shown interest in on social media. The collaboration unit can also propose collaboration with other experts that the expert follows. The collaboration unit can also analyze the activities of social media groups and communities in which the expert participates and propose relevant collaboration methods. In this way, relevant collaboration methods can be proposed by analyzing the expert's social media activities. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The future prediction system can further evaluate the reliability of data and prioritize the analysis of highly reliable data. For example, it can prioritize the analysis of data from highly reliable sources based on past reliability evaluations of those sources. It can also evaluate the expertise and past performance of data providers and prioritize the analysis of data from those providers. Furthermore, it can evaluate the frequency and recency of data updates and prioritize the analysis of the most recent data. In this way, by evaluating the reliability of the data, it can prioritize the analysis of highly reliable data.

[0057] The future prediction system can also prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize the collection of market trends and economic indicators related to that region. Furthermore, if the user is on the move, it can collect highly relevant data in real time based on their current location. Additionally, if the user is interested in a specific country or region, it can prioritize the collection of data related to that region. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant data.

[0058] The future prediction system can further analyze users' social media activity and collect relevant data. For example, it can prioritize collecting data related to topics that users have shown interest in on social media. It can also analyze the content of posts from experts and influencers that users follow and collect relevant data. Furthermore, it can analyze the activities of social media groups and communities that users participate in and collect relevant data. In this way, relevant data can be collected by analyzing users' social media activity.

[0059] The future prediction system can further adjust the level of detail of its analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data to provide concrete insights, while performing a concise analysis on less important data to present only the key points. Furthermore, it can adjust the depth and scope of the analysis according to the importance of the data to perform efficient analysis. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0060] The future prediction system can further apply different analytical algorithms depending on the data category during analysis. For example, economic data can be analyzed using an economic model. Market trend data can be analyzed using a trend analysis algorithm. Furthermore, consumer behavior data can be analyzed using a behavioral analysis algorithm. By applying different analytical algorithms depending on the data category, the system can provide more appropriate analytical results.

[0061] The future prediction system can further select the optimal collaboration method by referring to the past evaluation history of experts during the collaboration process. For example, it can prioritize collaboration with highly reliable experts based on their past evaluation history. It can also evaluate the past performance of experts and select the optimal collaboration method. Furthermore, it can analyze past feedback from experts and adjust the collaboration method accordingly. In this way, the optimal collaboration method can be selected by referring to the past evaluation history of experts.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The collection unit collects real-time data. Real-time data includes the latest trend information, market trends, and economic indicators. The collection unit collects sensor data, social media posts, and economic indicator data (e.g., stock price fluctuations, GDP, unemployment rate, etc.). Step 2: The analysis unit analyzes the data collected by the data collection unit to predict the future of the business. The analysis unit uses generative AI to combine historical data and real-time data to predict future trends, market movements, sales forecasts for the next quarter, and market acceptance of new products. Step 3: The absorption unit absorbs user submissions and adds them to the training data. The absorption unit takes user-submitted market feedback and opinions into the generating AI, adds them to the training data, and improves the prediction accuracy of the generating AI. Step 4: The collaboration unit works with experts to improve prediction accuracy. The collaboration unit improves the prediction accuracy of the generated AI by having experts verify the predictions of the generated AI and make corrections as needed.

[0064] (Example of form 2) The future prediction system according to an embodiment of the present invention is a system that utilizes generative AI (particularly LLM) to predict the future of a business and support internal and external decision-making. The future prediction system collects real-time data, and the generative AI analyzes this data to make future predictions. Next, it absorbs user-submitted content and adds it to the training data to improve prediction accuracy. Furthermore, it collaborates with experts to increase the accuracy of predictions. For example, the future prediction system collects real-time data. This data includes the latest trend information, market trends, and economic indicators. For example, fluctuations in stock prices and consumer purchasing behavior are collected as data. This data is input into the generative AI. Next, the generative AI analyzes the collected data and makes future business predictions. The generative AI analyzes past data and real-time data together to predict future trends and market movements. For example, it predicts sales forecasts for the next quarter and market acceptance of new products. Furthermore, user-submitted content is also absorbed by the generative AI and added to the training data. This allows the generative AI to always make predictions based on the latest information. For example, market impressions and opinions submitted by users are incorporated into the generative AI, improving prediction accuracy. Furthermore, collaborating with experts can further improve the accuracy of predictions. Experts verify the predictions made by the generative AI and make corrections as needed. This improves the reliability of the predictions and enhances the quality of decision-making. The future prediction system is characterized by its scalability and high accuracy. By leveraging the characteristics of generative AI, future predictions can be performed on a large scale. For example, predictions can be made simultaneously across multiple industries and regions. This allows the system to lead the evolution of industries and support business success. As a result, the future prediction system can make highly accurate predictions about the future of businesses and support decision-making both inside and outside the company.

[0065] The future prediction system according to this embodiment comprises a data collection unit, an analysis unit, an absorption unit, and a linking unit. The data collection unit collects real-time data. Real-time data includes, but is not limited to, the latest trend information, market trends, and economic indicators. The data collection unit also collects, for example, sensor data. The data collection unit can also collect social media posts. Furthermore, the data collection unit can also collect economic indicators. For example, the data collection unit collects stock price fluctuation data. Social media post data includes user opinions and impressions. Economic indicator data includes indicators such as GDP and unemployment rates. The analysis unit uses generative AI to analyze the data collected by the data collection unit and make future business predictions. The analysis unit, for example, combines historical data and real-time data for analysis. Furthermore, the analysis unit can use generative AI to predict future trends and market movements. Furthermore, the analysis unit can use generative AI to predict sales forecasts for the next quarter and market acceptance of new products. For example, the analysis unit combines historical sales data and real-time market trend data to make sales forecasts for the next quarter. The generative AI uses text generation AI (e.g., LLM) to analyze historical and real-time data. Based on historical and real-time data, the generative AI predicts future trends and market movements. The absorption unit absorbs user submissions and adds them to the training data. For example, the absorption unit incorporates user-submitted market opinions and feedback into the generative AI. The absorption unit can also analyze user submissions and add them to the training data. Furthermore, the absorption unit can improve the prediction accuracy of the generative AI based on user submissions. For example, the absorption unit incorporates user-submitted market opinions and feedback into the generative AI and adds them to the training data. User submissions include, for example, reviews, comments, and feedback. The collaboration unit improves prediction accuracy by collaborating with experts. For example, the collaboration unit allows experts to verify the generative AI's predictions. The collaboration unit also allows experts to modify the generative AI's predictions. Furthermore, the collaboration unit can improve the prediction accuracy of the generative AI based on expert opinions. For example, the collaboration unit allows experts to verify the generative AI's predictions and modify them as needed.Experts include, for example, economists, marketing experts, and data scientists. This allows the future prediction system according to the embodiment to make highly accurate predictions about the future of the business and support internal and external decision-making.

[0066] The data collection unit collects real-time data. Real-time data includes, but is not limited to, the latest trend information, market trends, and economic indicators. The data collection unit also collects sensor data, such as environmental data including temperature, humidity, vibration, and light intensity. This data is collected in real time and transmitted to a central database. The data collection unit can also collect social media posts, which include user opinions and feedback. For example, it can collect posts related to specific hashtags or keywords from a platform. Furthermore, the data collection unit can collect economic indicators, such as GDP and unemployment rates. For example, it can collect publicly available data from government agencies and economic research institutions to obtain real-time updated economic indicators. The data collection unit builds a comprehensive database by collecting and centrally managing information from these diverse data sources. This allows the data collection unit to efficiently collect real-time data and make it accessible to the analysis, absorption, and collaboration units. The data collection unit can also adjust the frequency and accuracy of data collection to provide flexible responses to specific situations and conditions. For example, if a particular market trend changes rapidly, the data collection frequency can be increased to update the data in real time. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis unit uses generative AI to analyze data collected by the data collection unit and make future business predictions. For example, the analysis unit combines historical data with real-time data for analysis. Specifically, it combines historical sales data and market trend data with real-time trend information and economic indicators for analysis. The generative AI uses text generation AI (e.g., LLM) to analyze historical data and real-time data. The generative AI utilizes natural language processing technology to analyze text data and predict future trends and market movements. For example, the generative AI combines historical sales data with real-time market trend data to make sales forecasts for the next quarter. The generative AI can also analyze social media posts and predict the market acceptance of new products based on user opinions and feedback. Furthermore, the generative AI can analyze economic indicator data and predict future economic trends. For example, it can predict future economic growth rates and employment conditions based on GDP and unemployment rate data. Based on these analysis results, the analysis unit makes future business predictions, such as forecasting sales for the next quarter and the market acceptance of new products. This allows the analysis unit to quickly and accurately analyze collected data and make highly accurate predictions about the future of the business. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, it can predict risk fluctuations in specific regions and time periods based on historical market trend data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0068] The absorption unit absorbs user-submitted content and adds it to the training data. Specifically, it incorporates user-submitted market opinions and feedback into the generating AI. For example, it collects reviews, comments, and feedback posted by users on social media and adds them to the generating AI's training data. The absorption unit analyzes this content and uses it as data to improve the generating AI's prediction accuracy. For example, by analyzing user-submitted opinions and feedback on a new product and incorporating them into the generating AI, it can more accurately predict the market acceptance of the new product. The absorption unit can also improve the generating AI's prediction accuracy based on user-submitted content. For example, by incorporating user-submitted market opinions and feedback into the generating AI and adding it to the training data, the generating AI can make predictions based on a wider range of data. This allows the absorption unit to effectively utilize user feedback and improve the generating AI's prediction accuracy. Furthermore, by collecting user-submitted content in real time and adding it to the generating AI's training data, the absorption unit can always make predictions based on the latest information. For example, by quickly collecting reviews and comments posted by users immediately after the launch of a new product and incorporating them into the generating AI, it can make rapid and accurate predictions of market acceptance. This allows the absorption unit to quickly collect user feedback and improve the prediction accuracy of the generating AI.

[0069] The Collaboration Department improves prediction accuracy by working with experts. Specifically, experts verify the predictions of the Generative AI. For example, experts such as economists, marketing experts, and data scientists review the prediction results of the Generative AI and evaluate their accuracy and validity. The Collaboration Department can also have experts modify the predictions of the Generative AI. For example, experts can provide insights into specific market trends or economic indicators based on the prediction results of the Generative AI and modify the prediction results. Furthermore, the Collaboration Department can improve the prediction accuracy of the Generative AI based on expert opinions. For example, experts can improve the prediction accuracy of the Generative AI by verifying the predictions of the Generative AI and making modifications as needed. In this way, the Collaboration Department can improve the prediction accuracy of the Generative AI by utilizing the expertise of experts. In addition, the Collaboration Department can improve the training data of the Generative AI through collaboration with experts. For example, by adding new data and insights provided by experts to the training data of the Generative AI, the Generative AI can make predictions based on more diverse data. In this way, the Collaboration Department can improve the prediction accuracy of the Generative AI through collaboration with experts. Furthermore, the collaboration department can continuously improve the prediction accuracy of the generative AI through regular meetings and workshops with experts. For example, experts can continuously improve the prediction accuracy of the generative AI by regularly reviewing its prediction results and evaluating their accuracy and validity. In this way, the collaboration department can enhance the prediction accuracy of the generative AI through collaboration with experts and make highly accurate predictions about the future of the business.

[0070] The analysis unit can analyze historical data and real-time data to predict future trends and market movements. For example, the analysis unit can analyze historical sales data and real-time market trend data. Furthermore, the analysis unit can use generative AI to predict future trends and market movements. In addition, the analysis unit can use generative AI to predict sales forecasts for the next quarter and the market acceptance of new products. For example, the analysis unit combines historical sales data and real-time market trend data to forecast sales for the next quarter. The generative AI uses text generation AI (e.g., LLM) to analyze historical data and real-time data. Based on historical data and real-time data, the generative AI predicts future trends and market movements. This combination of historical and real-time data enables more accurate future predictions.

[0071] The absorption unit can incorporate user-submitted market opinions and feedback into the generating AI and add it to the training data. For example, the absorption unit can incorporate user-submitted market opinions and feedback into the generating AI. The absorption unit can also analyze user submissions and add them to the training data. Furthermore, the absorption unit can improve the prediction accuracy of the generating AI based on user submissions. For example, the absorption unit can incorporate user-submitted market opinions and feedback into the generating AI and add it to the training data. User submissions include, for example, reviews, comments, and feedback. By adding user submissions to the training data, prediction accuracy is improved.

[0072] The collaboration department allows experts to verify the predictions of the generative AI and make corrections as needed. For example, experts can verify the predictions of the generative AI. Furthermore, experts can make corrections to the predictions of the generative AI. In addition, the collaboration department can improve the accuracy of the generative AI's predictions based on expert opinions. For example, experts can verify the predictions of the generative AI and make corrections as needed. These experts include, for example, economists, marketing experts, and data scientists. This ensures that the reliability of the predictions is improved through expert verification and correction.

[0073] The data collection unit can collect real-time data such as the latest trend information, market trends, and economic indicators. For example, the data collection unit can collect the latest trend information. It can also collect market trends. Furthermore, the data collection unit can collect economic indicators. For example, the data collection unit can collect news articles and social media posts. Market trend data includes sales data and consumer behavior data. Economic indicator data includes GDP, unemployment rate, and inflation rate. By collecting real-time data such as the latest trend information, market trends, and economic indicators, more accurate future predictions become possible.

[0074] The analysis unit can predict sales forecasts for the next quarter and the market acceptance of new products. For example, the analysis unit can forecast sales for the next quarter. It can also predict the market acceptance of new products. Furthermore, the analysis unit can use generative AI to predict sales forecasts for the next quarter and the market acceptance of new products. For example, the analysis unit combines historical sales data with real-time market trend data to forecast sales for the next quarter. The generative AI uses text generation AI (e.g., LLM) to analyze historical data and real-time data. Based on historical data and real-time data, the generative AI predicts sales forecasts for the next quarter and the market acceptance of new products. This supports business decision-making by predicting sales forecasts for the next quarter and the market acceptance of new products.

[0075] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is feeling optimistic, the data collection unit will prioritize collecting data related to positive market news and growth forecasts. It can also prioritize collecting data related to risk management and market uncertainty if the user is feeling anxious. Furthermore, the data collection unit can prioritize collecting data related to the user's emotions.

[0076] The data collection unit can evaluate the reliability of the data during collection and prioritize the collection of highly reliable data. For example, the data collection unit can prioritize the collection of data from highly reliable sources based on past reliability evaluations of the data sources. The data collection unit can also evaluate the expertise and past performance of data providers and prioritize the collection of data from highly reliable providers. Furthermore, the data collection unit can evaluate the frequency and recency of data updates and prioritize the collection of the latest data. For example, the data collection unit can prioritize the collection of data from highly reliable sources based on past reliability evaluations of the data sources. The data collection unit can also evaluate the expertise and past performance of data providers and prioritize the collection of data from highly reliable providers. The data collection unit can also evaluate the frequency and recency of data updates and prioritize the collection of the latest data. In this way, by evaluating the reliability of the data, highly reliable data can be prioritized for collection. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0077] The data collection unit can prioritize the collection of the latest data, taking into account the frequency of data updates. For example, the data collection unit prioritizes the collection of data from sources with a high data update frequency. The data collection unit can also prioritize the collection of data that is updated in real time, reflecting the latest market trends. Furthermore, the data collection unit can monitor regularly updated data sources and automatically collect the latest data. For example, the data collection unit prioritizes the collection of data from sources with a high data update frequency. The data collection unit can also prioritize the collection of data that is updated in real time, reflecting the latest market trends. The data collection unit can also monitor regularly updated data sources and automatically collect the latest data. This allows for the priority collection of the latest data by considering the frequency of data updates. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without using AI.

[0078] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is feeling optimistic, the unit will prioritize collecting data on positive market news and growth forecasts. If the user is feeling anxious, the unit can also prioritize collecting data on risk management and market uncertainty. Furthermore, if the user is excited, the unit can prioritize collecting data on emerging markets and innovative technologies. This allows for the collection of more relevant data by prioritizing data collection based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of market trends and economic indicators related to that region. Furthermore, if the user is on the move, the data collection unit can also collect highly relevant data in real time based on their current location. Additionally, if the user is interested in a specific country or region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is in a specific region, the data collection unit will prioritize the collection of market trends and economic indicators related to that region. If the user is on the move, the data collection unit can also collect highly relevant data in real time based on their current location. If the user is interested in a specific country or region, the data collection unit can also prioritize the collection of data related to that region. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data during the collection process. For example, the data collection unit prioritizes collecting data related to topics the user has shown interest in on social media. The data collection unit can also analyze posts from experts and influencers the user follows and collect relevant data. Furthermore, the data collection unit can analyze the activities of social media groups and communities the user participates in and collect relevant data. For example, the data collection unit prioritizes collecting data related to topics the user has shown interest in on social media. The data collection unit can also analyze posts from experts and influencers the user follows and collect relevant data. The data collection unit can also analyze the activities of social media groups and communities the user participates in and collect relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is feeling optimistic, the analysis unit will present the analysis results using positive language. If the user is feeling anxious, the analysis unit can also highlight information related to risk management and countermeasures. Furthermore, if the user is feeling excited, the analysis unit can present the analysis results using visually stimulating graphs and charts. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on highly important data to provide specific insights. Conversely, the analysis unit can perform a concise analysis on less important data, presenting only the key points. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data to perform efficient analysis. For example, the analysis unit can perform a detailed analysis on highly important data to provide specific insights. Conversely, the analysis unit can perform a concise analysis on less important data, presenting only the key points. The analysis unit can adjust the depth and scope of the analysis according to the importance of the data.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply an analysis algorithm using an economic model to economic data. It can also apply a trend analysis algorithm to market trend data. Furthermore, it can apply a behavioral analysis algorithm to consumer behavior data. By applying different analysis algorithms depending on the data category, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with more detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, if the user is in a hurry, the analysis unit will provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with more detailed explanations. If the user is excited, the analysis unit can also provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0085] The analysis unit can prioritize analysis based on the timing of data submission during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. It can also prioritize the analysis of regularly submitted data to grasp ongoing trends. Furthermore, the analysis unit can prioritize the analysis of data with high urgency to support rapid decision-making. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. It can also prioritize the analysis of regularly submitted data to grasp ongoing trends. It can also prioritize the analysis of data with high urgency to support rapid decision-making. This allows for rapid decision-making by prioritizing analysis based on the timing of data submission. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide important insights early. Alternatively, the analysis unit can postpone the analysis of less relevant data to perform efficient analysis. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data to provide optimal analysis results. For example, the analysis unit can prioritize the analysis of highly relevant data to provide important insights early. Alternatively, the analysis unit can postpone the analysis of less relevant data to perform efficient analysis. The analysis unit can dynamically adjust the order of analysis according to the relevance of the data to provide optimal analysis results. This enables efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without generative AI.

[0087] The absorption unit can estimate the user's emotions and determine the priority of posts to absorb based on the estimated emotions. For example, if the user is feeling optimistic, the absorption unit will prioritize absorbing positive posts. It can also prioritize absorbing posts related to risk management and market uncertainty if the user is feeling anxious. Furthermore, if the user is excited, the absorption unit can prioritize absorbing posts related to emerging markets and innovative technologies. This allows for the absorption of more appropriate posts by prioritizing content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0088] The absorption unit can evaluate the reliability of the posted content during absorption and prioritize the absorption of highly reliable posts. For example, the absorption unit can prioritize the absorption of highly reliable posts based on the poster's past reliability evaluation. The absorption unit can also evaluate the specificity and evidence of the posted content and prioritize the absorption of highly reliable posts. Furthermore, the absorption unit can evaluate the update frequency and recency of the posted content and prioritize the absorption of the latest posts. For example, the absorption unit can prioritize the absorption of highly reliable posts based on the poster's past reliability evaluation. The absorption unit can also evaluate the specificity and evidence of the posted content and prioritize the absorption of highly reliable posts. The absorption unit can also evaluate the update frequency and recency of the posted content and prioritize the absorption of the latest posts. In this way, by evaluating the reliability of the posted content, highly reliable posts can be prioritized for absorption. Some or all of the above processing in the absorption unit may be performed using AI, for example, or without using AI.

[0089] The absorption unit can apply different absorption algorithms depending on the category of the posted content during absorption. For example, for posts related to economics, the absorption unit can apply an absorption algorithm using an economic model. The absorption unit can also apply a trend analysis algorithm to posts related to market trends. Furthermore, the absorption unit can apply a behavioral analysis algorithm to posts related to consumer behavior. For example, the absorption unit can apply an absorption algorithm using an economic model to posts related to economics. The absorption unit can also apply a trend analysis algorithm to posts related to market trends. The absorption unit can also apply a behavioral analysis algorithm to posts related to consumer behavior. By applying different absorption algorithms depending on the category of the posted content, more appropriate content can be absorbed. Some or all of the above processing in the absorption unit may be performed using AI, for example, or without using AI.

[0090] The absorption unit can estimate the user's emotions and adjust how the absorbed content is displayed based on the estimated emotions. For example, if the user is feeling optimistic, the absorption unit will highlight positive content. It can also highlight content related to risk management and market uncertainty if the user is feeling anxious. Furthermore, if the user is excited, the absorption unit can highlight content related to emerging markets and innovative technologies. This allows for more appropriate display by adjusting how the absorbed content is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0091] The absorption unit can prioritize absorbing posts that are highly relevant, taking into account the user's geographical location information during absorption. For example, if the user is in a specific region, the absorption unit will prioritize absorbing posts related to that region. Furthermore, if the user is on the move, the absorption unit can also absorb highly relevant posts in real time based on their current location. Additionally, if the user is interested in a specific country or region, the absorption unit can prioritize absorbing posts related to that region. For example, if the user is in a specific region, the absorption unit will prioritize absorbing posts related to that region. If the user is on the move, the absorption unit can also absorb highly relevant posts in real time based on their current location. If the user is interested in a specific country or region, the absorption unit can also prioritize absorbing posts related to that region. This allows for the prioritization of highly relevant posts by considering the user's geographical location information. Some or all of the above processing in the absorption unit may be performed using, for example, AI, or not.

[0092] The absorption unit can analyze the user's social media activity and absorb relevant posts during the absorption process. For example, the absorption unit prioritizes absorbing posts related to topics the user has shown interest in on social media. The absorption unit can also analyze posts from experts and influencers the user follows and absorb relevant posts. Furthermore, the absorption unit can analyze the activities of social media groups and communities the user participates in and absorb relevant posts. For example, the absorption unit prioritizes absorbing posts related to topics the user has shown interest in on social media. The absorption unit can also analyze posts from experts and influencers the user follows and absorb relevant posts. The absorption unit can also analyze the activities of social media groups and communities the user participates in and absorb relevant posts. This allows the absorption of relevant posts by analyzing the user's social media activity. Some or all of the above processing in the absorption unit may be performed using AI, for example, or without AI.

[0093] The collaboration unit can estimate the user's emotions and adjust the collaboration method with experts based on the estimated emotions. For example, if the user is feeling optimistic, the collaboration unit will provide a collaboration method that emphasizes positive feedback. If the user is feeling anxious, the collaboration unit can also provide a collaboration method that emphasizes information on risk management and countermeasures. Furthermore, if the user is feeling excited, the collaboration unit can also provide a collaboration method that emphasizes new ideas and innovative suggestions. This allows for more appropriate collaboration by adjusting the collaboration method with experts based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0094] The collaboration unit can select the optimal collaboration method by referring to the past evaluation history of experts during collaboration. For example, the collaboration unit prioritizes collaboration with highly reliable experts based on their past evaluation history. The collaboration unit can also evaluate the past performance of experts and select the optimal collaboration method. Furthermore, the collaboration unit can analyze the past feedback of experts and adjust the collaboration method. For example, the collaboration unit prioritizes collaboration with highly reliable experts based on their past evaluation history. The collaboration unit can also evaluate the past performance of experts and select the optimal collaboration method. The collaboration unit can also analyze the past feedback of experts and adjust the collaboration method. This allows the collaboration unit to select the optimal collaboration method by referring to the past evaluation history of experts. Some or all of the above processes in the collaboration unit may be performed using AI, for example, or without using AI.

[0095] The collaboration unit can apply different collaboration algorithms depending on the expert's field of expertise during collaboration. For example, the collaboration unit can apply a collaboration algorithm using economic models to economic experts. It can also apply a trend analysis algorithm to market trend experts. Furthermore, it can apply a behavioral analysis algorithm to consumer behavior experts. This allows for more appropriate collaboration by applying different collaboration algorithms depending on the expert's field of expertise. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without AI.

[0096] The collaboration unit can estimate the user's emotions and determine the priority of collaborations based on those estimated emotions. For example, if the user is feeling optimistic, the collaboration unit will prioritize collaborations that emphasize positive feedback. If the user is feeling anxious, the collaboration unit can also prioritize collaborations related to risk management and countermeasures. Furthermore, if the user is excited, the collaboration unit can prioritize collaborations that emphasize new ideas and innovative suggestions. This allows for more appropriate collaborations by prioritizing collaborations based on the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0097] The collaboration unit can select the optimal collaboration method by considering the geographical location information of experts during collaboration. For example, if the expert is nearby, the collaboration unit will prioritize face-to-face collaboration. If the expert is far away, the collaboration unit may also prioritize online collaboration. Furthermore, the collaboration unit can select the optimal collaboration method based on the geographical location information of experts. For example, if the expert is nearby, the collaboration unit will prioritize face-to-face collaboration. If the expert is far away, the collaboration unit may also prioritize online collaboration. The collaboration unit can also select the optimal collaboration method based on the geographical location information of experts. This allows the optimal collaboration method to be selected by considering the geographical location information of experts. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or without using AI.

[0098] The collaboration unit can analyze the social media activities of experts during the collaboration process and propose relevant collaboration methods. For example, the collaboration unit can propose collaboration methods related to topics that experts have shown interest in on social media. The collaboration unit can also propose collaboration with other experts that the expert follows. Furthermore, the collaboration unit can analyze the activities of social media groups and communities in which the expert participates and propose relevant collaboration methods. For example, the collaboration unit can propose collaboration methods related to topics that experts have shown interest in on social media. The collaboration unit can also propose collaboration with other experts that the expert follows. The collaboration unit can also analyze the activities of social media groups and communities in which the expert participates and propose relevant collaboration methods. In this way, relevant collaboration methods can be proposed by analyzing the expert's social media activities. Some or all of the above processing in the collaboration unit may be performed using AI, for example, or not using AI.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The future prediction system can further estimate the user's emotions and adjust how the prediction results are presented based on those emotions. For example, if the user is optimistic, the prediction results can be presented using positive language. If the user is anxious, the prediction results can be presented with emphasis on information related to risk management and countermeasures. Furthermore, if the user is excited, the prediction results can be presented using visually stimulating graphs and charts. By adjusting how the prediction results are presented based on the user's emotions, it becomes possible to provide more appropriate information.

[0101] The future prediction system can further evaluate the reliability of data and prioritize the analysis of highly reliable data. For example, it can prioritize the analysis of data from highly reliable sources based on past reliability evaluations of those sources. It can also evaluate the expertise and past performance of data providers and prioritize the analysis of data from those providers. Furthermore, it can evaluate the frequency and recency of data updates and prioritize the analysis of the most recent data. In this way, by evaluating the reliability of the data, it can prioritize the analysis of highly reliable data.

[0102] The future prediction system can also prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, it will prioritize the collection of market trends and economic indicators related to that region. Furthermore, if the user is on the move, it can collect highly relevant data in real time based on their current location. Additionally, if the user is interested in a specific country or region, it can prioritize the collection of data related to that region. In this way, by considering the user's geographical location, it can prioritize the collection of highly relevant data.

[0103] The future prediction system can further analyze users' social media activity and collect relevant data. For example, it can prioritize collecting data related to topics that users have shown interest in on social media. It can also analyze the content of posts from experts and influencers that users follow and collect relevant data. Furthermore, it can analyze the activities of social media groups and communities that users participate in and collect relevant data. In this way, relevant data can be collected by analyzing users' social media activity.

[0104] The future prediction system can further estimate the user's emotions and adjust the types of data collected based on those emotions. For example, if the user is optimistic, it can prioritize collecting data on positive market news and growth forecasts. If the user is anxious, it can prioritize collecting data on risk management and market uncertainty. Furthermore, if the user is excited, it can prioritize collecting data on emerging markets and innovative technologies. By adjusting the types of data collected based on the user's emotions, the system can collect more relevant data.

[0105] The future prediction system can further adjust the level of detail of its analysis based on the importance of the data. For example, it can perform a detailed analysis on highly important data to provide concrete insights, while performing a concise analysis on less important data to present only the key points. Furthermore, it can adjust the depth and scope of the analysis according to the importance of the data to perform efficient analysis. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0106] The future prediction system can further estimate the user's emotions and adjust the length of the analysis based on those emotions. For example, if the user is in a hurry, it can provide a short, concise analysis. If the user is relaxed, it can provide a longer analysis with more detailed explanations. Furthermore, if the user is excited, it can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, it can provide more appropriate results.

[0107] The future prediction system can further apply different analytical algorithms depending on the data category during analysis. For example, economic data can be analyzed using an economic model. Market trend data can be analyzed using a trend analysis algorithm. Furthermore, consumer behavior data can be analyzed using a behavioral analysis algorithm. By applying different analytical algorithms depending on the data category, the system can provide more appropriate analytical results.

[0108] The future prediction system can further estimate the user's emotions and determine the priority of posts to absorb based on those emotions. For example, if a user is optimistic, it will prioritize positive posts. If a user is anxious, it can prioritize posts about risk management and market uncertainty. Furthermore, if a user is excited, it can prioritize posts about emerging markets and innovative technologies. By prioritizing posts based on the user's emotions, it can absorb more relevant content.

[0109] The future prediction system can further select the optimal collaboration method by referring to the past evaluation history of experts during the collaboration process. For example, it can prioritize collaboration with highly reliable experts based on their past evaluation history. It can also evaluate the past performance of experts and select the optimal collaboration method. Furthermore, it can analyze past feedback from experts and adjust the collaboration method accordingly. In this way, the optimal collaboration method can be selected by referring to the past evaluation history of experts.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The collection unit collects real-time data. Real-time data includes the latest trend information, market trends, and economic indicators. The collection unit collects sensor data, social media posts, and economic indicator data (e.g., stock price fluctuations, GDP, unemployment rate, etc.). Step 2: The analysis unit analyzes the data collected by the data collection unit to predict the future of the business. The analysis unit uses generative AI to combine historical data and real-time data to predict future trends, market movements, sales forecasts for the next quarter, and market acceptance of new products. Step 3: The absorption unit absorbs user submissions and adds them to the training data. The absorption unit takes user-submitted market feedback and opinions into the generating AI, adds them to the training data, and improves the prediction accuracy of the generating AI. Step 4: The collaboration unit works with experts to improve prediction accuracy. The collaboration unit improves the prediction accuracy of the generated AI by having experts verify the predictions of the generated AI and make corrections as needed.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, absorption unit, and collaboration unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the collection unit is implemented as a function to collect sensor data from the smart device 14 and social media posts. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which analyzes data using generation AI and makes future predictions. The absorption unit is implemented, for example, by the control unit 46A of the smart device 14, which absorbs user posts and adds them to the training data. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which improves prediction accuracy in collaboration with experts. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, absorption unit, and collaboration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented as a function to collect sensor data from the smart glasses 214 and social media posts. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to analyze data using generating AI and make future predictions. The absorption unit is implemented in the control unit 46A of the smart glasses 214, for example, to absorb user posts and add them to the training data. The collaboration unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, to improve prediction accuracy in collaboration with experts. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the collection unit, analysis unit, absorption unit, and collaboration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented as a function to collect sensor data from the headset terminal 314 and social media posts. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes data using generation AI and makes future predictions. The absorption unit is implemented, for example, by the control unit 46A of the headset terminal 314, which absorbs user posts and adds them to the training data. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which improves prediction accuracy in collaboration with experts. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, absorption unit, and collaboration unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented as a function to collect sensor data from the robot 414 and social media posts. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which analyzes data using generated AI and makes future predictions. The absorption unit is implemented, for example, by the control unit 46A of the robot 414, which absorbs user posts and adds them to the training data. The collaboration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which improves prediction accuracy in collaboration with experts. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A data collection unit that collects real-time data, The analysis unit analyzes the data collected by the aforementioned collection unit and makes predictions about the future of the business, An absorption unit that absorbs user submissions and adds them to the training data, It includes a collaboration unit that improves prediction accuracy in cooperation with experts. A system characterized by the following features. (Note 2) The aforementioned analysis unit, By combining historical data with real-time data, we can analyze and predict future trends and market movements. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned absorbent part is User-submitted market feedback and opinions are incorporated into the AI ​​and added to its training data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned linkage unit is, Experts verify the predictions generated by the AI ​​and make corrections as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is Collects real-time data such as the latest trend information, market trends, and economic indicators. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We forecast sales for the next quarter and market acceptance of new products. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is During data collection, the reliability of the data is evaluated, and reliable data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, the most recent data is collected first, taking into account the frequency of data updates. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes the user's social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned absorbent part is It estimates the user's emotions and determines the priority of content to absorb based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned absorbent part is During the absorption process, the reliability of the submitted content is evaluated, and highly reliable submissions are prioritized. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned absorbent part is During the absorption process, different absorption algorithms are applied depending on the category of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned absorbent part is We estimate the user's emotions and adjust how posts are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned absorbent part is During the absorption process, the system prioritizes absorbing posts that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned absorbent part is During the absorption process, the system analyzes the user's social media activity and absorbs relevant posts. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned linkage unit is, It estimates the user's emotions and adjusts how it collaborates with experts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned linkage unit is, When collaborating, the optimal collaboration method is selected by referring to the past evaluation history of experts. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned linkage unit is, When collaborating, different collaboration algorithms are applied depending on the expertise of each specialist. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned linkage unit is, It estimates the user's emotions and determines the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned linkage unit is, When collaborating, the optimal collaboration method will be selected, taking into account the geographical location information of the experts. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned linkage unit is, During the collaboration process, we analyze the experts' social media activities and propose relevant collaboration methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects real-time data, The analysis unit analyzes the data collected by the aforementioned collection unit and makes predictions about the future of the business, An absorption unit that absorbs user submissions and adds them to the training data, It includes a collaboration unit that improves prediction accuracy in cooperation with experts. A system characterized by the following features.

2. The aforementioned analysis unit, By combining historical data with real-time data, we can analyze and predict future trends and market movements. The system according to feature 1.

3. The aforementioned absorbent part is We incorporate user-submitted market feedback and opinions into the AI ​​and add them to its training data. The system according to feature 1.

4. The aforementioned linkage unit is, Experts will verify the predictions generated by the AI ​​and make corrections as needed. The system according to feature 1.

5. The aforementioned collection unit is Collects real-time data such as the latest trend information, market trends, and economic indicators. The system according to feature 1.

6. The aforementioned analysis unit, We forecast sales for the next quarter and market acceptance of new products. The system according to feature 1.

7. The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is During data collection, the reliability of the data is evaluated, and reliable data is prioritized for collection. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, the most recent data is collected first, taking into account the frequency of data updates. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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