system

Abstract

The system according to this embodiment aims to integrate multiple data sources, predict market trends in real time, and quickly grasp the activities of competing companies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a tracking unit, an alert unit, and a proposal unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The tracking unit tracks the activities of competitors based on the data analyzed by the analysis unit. The alert unit provides automatic alerts based on the information obtained by the tracking unit. The proposal unit generates strategic proposals based on the information provided by the alert unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to integrate a plurality of data sources to predict market trends in real time and to quickly grasp the trends of competing companies.

[0005] The system according to the embodiment aims to integrate a plurality of data sources, predict market trends in real time, and quickly grasp the trends of competing companies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a tracking unit, an alert unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The tracking unit tracks the activities of competitors based on the data analyzed by the analysis unit. The alert unit provides automatic alerts based on the information obtained by the tracking unit. The proposal unit generates strategic proposals based on the information provided by the alert unit. [Effects of the Invention]

[0007] The system according to this embodiment can integrate multiple data sources, predict market trends in real time, and quickly grasp the activities of competitors. [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, etc. 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 reception 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 reception 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) An AI-driven platform according to an embodiment of the present invention is a system that integrates multiple data sources and predicts market trends in real time. This system collects diverse data such as social media, news articles, corporate financial data, patent information, and satellite imagery, and provides industry trends, competitive analysis, and consumer behavior predictions by comprehensively analyzing this data using multimodal AI. Furthermore, a competitive analysis AI agent tracks the activities of competitors in real time, provides automatic alerts for new product announcements and changes in market strategies of competitors, and autonomously generates strategic proposals based on these alerts. For example, the AI-driven platform collects data from social media posts, text data from news articles, corporate financial reports, patent information, satellite imagery, etc. This data is acquired and integrated in real time. Next, multimodal AI analyzes this data to perform industry trends, competitive analysis, and consumer behavior predictions. For example, it grasps consumer interests from social media posts, analyzes industry trends from news articles, and evaluates the management status of companies from corporate financial data. Furthermore, a competitive analysis AI agent tracks the activities of competitors in real time, provides automatic alerts for new product announcements and changes in market strategies of competitors, and autonomously generates strategic proposals based on these alerts. For example, when a competitor announces a new product, the platform can acquire that information in real time, analyze the competitor's market strategy, and incorporate it into its own strategy. This prevents missing important market signals due to information overload, facilitates the integration and analysis of diverse data sources, and enables a decision-making process that keeps pace with the speed of market change. It can also grasp the complexity and regional differences of the global market and provide detailed market insights by region and industry. In this way, the AI-driven platform can integrate multiple data sources and predict market trends in real time.

[0029] The AI-driven platform according to this embodiment comprises a data collection unit, an analysis unit, a tracking unit, an alert unit, and a suggestion unit. The data collection unit collects data. The data collection unit collects data such as social media, news articles, corporate financial data, patent information, and satellite imagery. For example, the data collection unit collects social media posts, text data from news articles, corporate financial reports, patent information, and satellite imagery. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses multimodal AI to comprehensively analyze text, images, and numerical data. For example, the analysis unit grasps consumer interests from social media posts, analyzes industry trends from news articles, and evaluates the management status of companies from corporate financial data. The tracking unit tracks the activities of competitors based on the data analyzed by the analysis unit. For example, the tracking unit tracks new product announcements and changes in market strategies of competitors in real time. The tracking unit acquires information in real time, for example, when a competitor announces a new product, analyzes the competitor's market strategy, and incorporates it into the company's own strategy. The alerting unit provides automatic alerts based on the information obtained by the tracking unit. The alerting unit provides automatic alerts, for example, for new product announcements or changes in market strategies by competitors. The alerting unit acquires information in real time, for example, when a competitor announces a new product, and provides automatic alerts. The proposal unit generates strategic proposals based on the information provided by the alerting unit. The proposaling unit autonomously generates strategic proposals, for example, based on new product announcements or changes in market strategies by competitors. The proposaling unit acquires information in real time, for example, when a competitor announces a new product, analyzes the competitor's market strategy, and incorporates it into the company's own strategy. As a result, the AI-driven platform according to this embodiment can integrate multiple data sources and predict market trends in real time.

[0030] The data collection unit collects data from sources such as social media, news articles, corporate financial data, patent information, and satellite imagery. Specifically, when collecting social media posts, it uses APIs to retrieve content in real time and filters relevant posts based on keywords and hashtags. When collecting text data from news articles, it uses web scraping technology to automatically collect articles from news sites and analyzes the content using natural language processing technology. When collecting corporate financial reports, it downloads reports from the company's official website or financial databases and converts them into text data using OCR technology. When collecting patent information, it retrieves patent documents from the Japan Patent Office database and extracts relevant patents based on patent classification codes and keywords. When collecting satellite imagery, it obtains high-resolution images from satellite data provision services and uses image analysis technology to detect changes and anomalies on the Earth's surface. In this way, the data collection unit collects a wide range of data from diverse data sources and provides a foundation for understanding market trends in real time. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, by increasing the frequency of data collection in accordance with specific events or seasons, more detailed information can be obtained. Furthermore, the collected data is stored on a cloud server, making it accessible to the analysis and tracking units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses multimodal AI to comprehensively analyze text, images, and numerical data. Specifically, to understand consumer interests from social media posts, it uses natural language processing technology to analyze post content and perform sentiment analysis and topic modeling. To analyze industry trends from news articles, it classifies the content of articles, extracts important keywords and phrases, and grasps trends. To evaluate a company's management status from corporate financial data, it calculates financial indicators and analyzes performance fluctuations by comparing them with historical data. Furthermore, when analyzing patent information, it analyzes the content of patent documents to understand technological advancements and the R&D trends of competing companies. When analyzing satellite imagery, it uses image recognition technology to detect changes and anomalies on the Earth's surface and explores applications in fields such as agriculture and urban planning. As a result, the analysis unit can quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past consumer interest data, it can predict fluctuations in demand for specific products and services and formulate future marketing strategies. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The tracking unit tracks the activities of competitors based on data analyzed by the analysis unit. For example, the tracking unit tracks new product announcements and changes in market strategies of competitors in real time. Specifically, it monitors the official websites and press releases of competitors to automatically detect new product announcements and other important announcements. It also collects information on competitors from social media and news articles and integrates it with the data obtained by the analysis unit to analyze the market strategies of competitors. Furthermore, it uses patent information to track the research and development trends of competitors and understand the status of new technology development and patent application trends. As a result, the tracking unit can grasp the activities of competitors in real time and provide information to reflect in the company's own strategy. For example, if a competitor announces a new product, the information can be obtained in real time and reflected in the company's own product development and marketing strategy. In addition, by tracking changes in the market strategies of competitors and reviewing the company's own strategy as needed, it is possible to maintain competitiveness. Furthermore, by monitoring the activities of competitors over the long term and understanding trends and patterns, the tracking unit can predict future market trends. As a result, the tracking unit can track the activities of competitors in real time and support quick and appropriate responses.

[0033] The alert unit provides automated alerts based on information obtained by the tracking unit. For example, the alert unit provides automated alerts regarding new product announcements or changes in market strategies by competitors. Specifically, when a competitor announces a new product, it acquires that information in real time and provides an automated alert. Alerts are delivered using multiple communication methods such as email, SMS, and push notifications, ensuring that important information is conveyed quickly and reliably. Furthermore, the content of the alerts is customized according to the user's role and responsibilities, providing only the necessary information. For example, product development personnel are provided with technical details of new products, and marketing personnel are provided with information regarding changes in market strategies. This allows the alert unit to quickly provide appropriate information to each user, minimizing the risk of damage. In addition, the alert unit can collect user feedback and continuously improve the accuracy and effectiveness of the alert content. For example, based on feedback from users who receive alerts, the timing and content of alerts are reviewed to provide more effective information. The alert unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the alert unit to quickly and reliably provide information to users, minimizing the risk of damage.

[0034] The Proposal Department generates strategic proposals based on information provided by the Alert Department. For example, the Proposal Department autonomously generates strategic proposals based on new product announcements or changes in market strategies by competitors. Specifically, when a competitor announces a new product, it acquires this information in real time, analyzes the competitor's market strategy, and incorporates it into its own strategy. Using AI, the Proposal Department analyzes this data and simulates multiple scenarios to identify the most effective strategy. For example, it proposes the timing of new product launches, pricing, and promotional strategies to strengthen the company's competitiveness. The Proposal Department can also utilize historical data and statistical information to provide long-term strategic proposals. For example, it predicts future market fluctuations based on past market trends and competitor activity, and formulates appropriate strategies. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the Proposal Department to always provide highly accurate strategic proposals based on the latest information, supporting quick and appropriate responses. Additionally, the Proposal Department provides dashboards and reports to visually display the proposals in an easy-to-understand manner, enabling users to easily understand and implement the proposals. This allows the proposal department to provide users with effective strategic proposals and improve the company's competitiveness.

[0035] The data collection unit collects data such as social media posts, news articles, corporate financial data, patent information, and satellite imagery. For example, the data collection unit collects social media posts. For example, the data collection unit collects text data from news articles. For example, the data collection unit collects corporate financial reports. For example, the data collection unit collects patent information. For example, the data collection unit collects satellite imagery. This allows for more comprehensive market trend forecasting by collecting data from diverse data sources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into a generating AI, which can then analyze and collect the posted data.

[0036] The analysis unit uses multimodal AI to comprehensively analyze text, images, and numerical data. For example, the analysis unit can understand consumer interests from social media posts. For example, the analysis unit can analyze industry trends from news articles. For example, the analysis unit can evaluate a company's management status from corporate financial data. By using multimodal AI, it becomes possible to comprehensively analyze data in different formats and predict market trends with greater accuracy. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input social media posts into a generating AI, which can then analyze the posts to understand consumer interests.

[0037] The tracking unit tracks new product announcements and changes in market strategies of competitors in real time. For example, if a competitor announces a new product, the tracking unit acquires that information in real time. For example, the tracking unit analyzes the market strategies of competitors. For example, the tracking unit incorporates the competitor's new product announcements and changes in market strategies into its own strategy. This enables rapid response by tracking the movements of competitors in real time. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input information on a competitor's new product announcement into a generating AI, which can analyze the information and acquire it in real time.

[0038] The alert unit provides automated alerts regarding the activities of competitors. For example, if a competitor announces a new product, the alert unit acquires that information in real time and provides an automated alert. The alert unit also provides automated alerts regarding changes in the market strategy of competitors. By providing automated alerts, it becomes possible to respond quickly without missing important market signals. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input information about a competitor's new product announcement into a generating AI, which can then analyze the information and provide an automated alert.

[0039] The proposal department autonomously generates strategic proposals based on the actions of competitors. For example, if a competitor announces a new product, the proposal department acquires that information in real time, analyzes the competitor's market strategy, and reflects it in its own strategy. The proposal department autonomously generates strategic proposals based on changes in the competitor's market strategy. This enables rapid and effective strategy planning by autonomously generating strategic proposals based on the actions of competitors. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input information on a competitor's new product announcement into a generating AI, which can then analyze the information and autonomously generate strategic proposals.

[0040] The dashboard section provides a customizable dashboard. The dashboard section allows users to customize and display the information they need. The dashboard section allows users to customize the placement of widgets. The dashboard section allows users to customize the types of data to display. This enables efficient information management because users can customize and display the information they need. Some or all of the above-described processes in the dashboard section may be performed using AI, for example, or not using AI. For example, the dashboard section can input a user's customization request into a generating AI, which can then generate a customized dashboard.

[0041] The query unit accepts queries in natural language. The query unit allows users to input queries in natural language, for example. The query unit analyzes queries using natural language processing techniques, for example. The query unit can increase the number of languages ​​it supports, for example. This allows users to input queries in natural language, enabling intuitive operation. Some or all of the above processing in the query unit may be performed using AI, for example, or not using AI. For example, the query unit can input the user's natural language query into a generating AI, which can then analyze the query and provide results.

[0042] The data collection unit evaluates the reliability of each data source and prioritizes the collection of reliable data. For example, the data collection unit analyzes social media posts and prioritizes the collection of data from reliable accounts. For example, the data collection unit evaluates the reliability of news articles and prioritizes the collection of data from reliable media outlets. For example, the data collection unit evaluates the reliability of corporate financial data and prioritizes the collection of data from reliable companies. This improves the accuracy of the analysis results by prioritizing the collection of reliable data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into a generating AI, which can evaluate the reliability and prioritize the collection of reliable data.

[0043] The data collection unit filters data based on specific keywords or topics during the data collection process. For example, the data collection unit filters social media posts by specific keywords and collects relevant data. For example, the data collection unit filters news articles by specific topics and collects relevant data. For example, the data collection unit filters corporate financial data by specific indicators and collects relevant data. This allows for the efficient collection of highly relevant data by filtering data based on specific keywords or topics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into a generating AI, which can then filter them by specific keywords and collect relevant data.

[0044] The data collection unit prioritizes collecting highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit prioritizes collecting data related to that region. For example, if the user is on the move, the data collection unit collects data related to the user's current location in real time. For example, if the user is in a specific country, the data collection unit prioritizes collecting data related to that country. This makes it possible to predict region-specific market trends by collecting highly relevant data based on 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. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the location information and prioritize collecting highly relevant data.

[0045] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects data related to topics the user has shown interest in on social media. For example, the data collection unit analyzes the content of posts from accounts the user follows and collects relevant data. For example, the data collection unit analyzes the activities of groups and communities the user participates in and collects relevant data. This makes it possible to collect data based on the user's interests 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. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then analyze the activity and collect relevant data.

[0046] The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data and a simplified analysis on less important data. For example, the analysis unit applies multiple analysis methods to important data to provide detailed results. For example, the analysis unit collects additional data and performs a more detailed analysis on highly important data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, detailed analysis results can be provided for important data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, which can analyze the importance and adjust the level of detail of the analysis.

[0047] The analysis unit applies different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data and an image analysis algorithm to image data. For example, the analysis unit applies a statistical analysis algorithm to numerical data and a text mining algorithm to text data. For example, the analysis unit applies a deep learning algorithm to image data and a machine learning algorithm to numerical data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply an analysis algorithm appropriate to the category.

[0048] The analysis unit determines the priority of analysis based on the data collection timing. For example, the analysis unit prioritizes the analysis of the most recent data and postpones the analysis of older data. For example, the analysis unit prioritizes the analysis of data collected during a specific period to grasp the trends during that period. For example, the analysis unit prioritizes the analysis of data collected in real time to provide immediate insights. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, which can then analyze the collection timing to determine the priority of analysis.

[0049] The analysis unit adjusts the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analyzing highly relevant data and postpones analyzing less relevant data. For example, the analysis unit prioritizes analyzing data related to a specific topic and provides insights into that topic. For example, the analysis unit groups highly relevant data and analyzes them all at once. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then analyze the relevance and adjust the order of analysis.

[0050] The tracking unit improves tracking accuracy by considering the relationships between competing companies during tracking. For example, the tracking unit considers alliances between competing companies and tracks the activities of all affiliated companies at once. For example, the tracking unit considers competitive relationships between competing companies and prioritizes tracking the activities of companies with high competition. For example, the tracking unit considers market share between competing companies and prioritizes tracking the activities of companies with large market share. This improves tracking accuracy by considering the relationships between competing companies. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data on the relationships between competing companies into a generating AI, which can then analyze the relationships to improve tracking accuracy.

[0051] The tracking unit performs tracking while considering the attribute information of competitors. For example, the tracking unit considers the industry attributes of competitors and prioritizes tracking the activities of other companies in the same industry. For example, the tracking unit considers the size of competitors and prioritizes tracking the activities of large companies. For example, the tracking unit considers the regional attributes of competitors and prioritizes tracking the activities of companies related to a specific region. This allows for more detailed tracking by considering the attribute information of competitors. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the attribute information of competitors into a generating AI, and the generating AI can analyze the attribute information and perform tracking.

[0052] The tracking unit performs tracking while considering the geographical distribution of competitors. For example, the tracking unit considers the location of the competitors' headquarters and prioritizes tracking trends related to the headquarters location. For example, the tracking unit considers the competitors' major markets and prioritizes tracking trends related to those major markets. For example, the tracking unit considers the competitors' production bases and prioritizes tracking trends related to those production bases. This makes it possible to perform region-specific tracking by considering the geographical distribution of competitors. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input geographical distribution data of competitors into a generating AI, which can then analyze the geographical distribution and perform tracking.

[0053] The tracking unit improves tracking accuracy by referring to relevant literature of competitors during tracking. For example, the tracking unit refers to patent documents of competitors to track trends in new technologies. For example, the tracking unit refers to research papers of competitors to track trends in research and development. For example, the tracking unit refers to market reports of competitors to track trends in market strategies. This improves tracking accuracy by referring to relevant literature of competitors. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input relevant literature data of competitors into a generating AI, which can then analyze the literature to improve tracking accuracy.

[0054] The alert unit predicts the current alert by referring to past alert data when an alert occurs. For example, the alert unit analyzes past alert data and predicts an alert when a similar pattern occurs. For example, the alert unit predicts the occurrence of an alert under specific conditions from past alert data. For example, the alert unit predicts the frequency and timing of alerts based on past alert data. This makes it easier to predict the current alert by referring to past alert data. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input past alert data into a generating AI, which can analyze the data and predict the current alert.

[0055] The alert unit applies different alert methods to each competitor's category when an alert occurs. For example, the alert unit applies different alert methods depending on the competitor's industry category. For example, the alert unit applies different alert methods depending on the competitor's size. For example, the alert unit applies different alert methods depending on the competitor's regional attributes. By applying different alert methods to each competitor's category, more appropriate alerts can be provided. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input competitor category data into a generating AI, and the generating AI can apply an alert method appropriate to the category.

[0056] The alert unit analyzes changes in alerts based on the actions of competitors when an alert occurs. For example, the alert unit generates an alert based on a competitor's new product announcement and analyzes its changes. For example, the alert unit generates an alert based on a competitor's change in market strategy and analyzes its changes. For example, the alert unit generates an alert based on a competitor's change in financial condition and analyzes its changes. By analyzing changes in alerts based on the actions of competitors, it is possible to provide more appropriate alerts. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input competitor action data into a generating AI, which can then analyze the actions and analyze changes in alerts.

[0057] The alert unit analyzes alerts by referring to relevant market data of competitors when an alert occurs. For example, the alert unit analyzes alerts by referring to market share data of competitors. For example, the alert unit analyzes alerts by referring to sales data of competitors. For example, the alert unit analyzes alerts by referring to customer data of competitors. By referring to relevant market data of competitors, the accuracy of alerts is improved. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input market data of competitors into a generating AI, and the generating AI can analyze the data and analyze the alerts.

[0058] The proposal department, when making a proposal, analyzes the past trends of competing companies to select the optimal proposal method. For example, the proposal department may analyze past new product announcement data of competing companies to select the optimal proposal method. For example, the proposal department may analyze past market strategy data of competing companies to select the optimal proposal method. For example, the proposal department may analyze past financial data of competing companies to select the optimal proposal method. In this way, the optimal proposal method can be selected by analyzing the past trends of competing companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past trend data of competing companies into a generating AI, and the generating AI can analyze the data to select the optimal proposal method.

[0059] The proposal department customizes the proposal based on the current market conditions of competitors. For example, the proposal department customizes the proposal based on the current market share data of competitors. For example, the proposal department customizes the proposal based on the current sales data of competitors. For example, the proposal department customizes the proposal based on the current customer data of competitors. By customizing the proposal based on the current market conditions of competitors, more effective proposals become possible. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input data on the current market conditions of competitors into a generating AI, which can then analyze the data and customize the proposal.

[0060] The proposal department selects the optimal proposal method when making a proposal, taking into account the geographical location information of competing companies. For example, the proposal department may select a proposal method based on the location of the competing company's headquarters. For example, the proposal department may select a proposal method based on the competing company's main market. For example, the proposal department may select a proposal method based on the competing company's production base. This allows for region-specific proposals by considering the geographical location information of competing companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the geographical location information of competing companies into a generating AI, which can then analyze the information and select the optimal proposal method.

[0061] The proposal department analyzes the social media activities of competitors and proposes methods for proposals. For example, the proposal department analyzes the content of competitors' social media posts and selects the most suitable proposal method. For example, the proposal department analyzes the reactions of competitors' social media followers and selects the most suitable proposal method. For example, the proposal department analyzes successful social media campaigns of competitors and selects the most suitable proposal method. In this way, the most suitable proposal method can be selected by analyzing the social media activities of competitors. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input competitors' social media activity data into a generating AI, which can then analyze the data and propose methods for proposals.

[0062] The dashboard section, when displaying the dashboard, selects the optimal display method by referring to the user's past operation history. For example, the dashboard section analyzes the user's past operation history and prioritizes the display of frequently used functions. For example, the dashboard section prioritizes the display of specific information from the user's past operation history. For example, the dashboard section provides a customized dashboard based on the user's past operation history. This allows the dashboard section to provide the optimal display method for the user by referring to the user's past operation history. Some or all of the above processing in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input the user's past operation history data into a generating AI, which can then analyze the data and select the optimal display method.

[0063] The dashboard section selects the optimal display method when displaying the dashboard, taking into account the user's device information. For example, if the user is using a smartphone, the dashboard section provides a display method that matches the screen size. For example, if the user is using a tablet, the dashboard section provides a display method optimized for a larger screen. For example, if the user is using a desktop, the dashboard section provides a display method that includes detailed information. In this way, by taking into account the user's device information, it is possible to provide a display method optimized for the device. Some or all of the above processing in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input the user's device information into a generating AI, and the generating AI can analyze the information and select the optimal display method.

[0064] The query unit selects the optimal processing method by referring to the user's past query history when processing queries. For example, the query unit analyzes the user's past query history and prioritizes processing frequently used queries. For example, the query unit prioritizes processing specific information from the user's past query history. For example, the query unit provides customized query results based on the user's past query history. This allows the system to provide the user with the most optimal query results by referring to the user's past query history. Some or all of the above processing in the query unit may be performed using AI, for example, or without AI. For example, the query unit can input the user's past query history data into a generating AI, which can then analyze the data and select the optimal processing method.

[0065] The query unit selects the optimal processing method when processing queries, taking into account the user's device information. For example, if the user is using a smartphone, the query unit provides query results that are adapted to the screen size. If the user is using a tablet, the query unit provides query results optimized for a larger screen. If the user is using a desktop, the query unit provides query results that include detailed information. In this way, by taking into account the user's device information, it is possible to provide query results optimized for the device. Some or all of the above processing in the query unit may be performed using AI, for example, or without AI. For example, the query unit can input the user's device information into a generating AI, which can then analyze the information and select the optimal processing method.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can also prioritize the collection of highly relevant data by referring to the user's past behavior history during data collection. For example, if a user has previously shown interest in a particular industry, data related to that industry can be prioritized. If a user has previously frequently checked the trends of a particular company, data related to that company can be prioritized. If a user has previously shown interest in a particular region, data related to that region can be prioritized. This allows for the efficient collection of more relevant data based on the user's past behavior history.

[0068] The analysis unit can evaluate the reliability of data and prioritize the analysis of highly reliable data. For example, it can analyze social media posts and prioritize data from reliable accounts. It can evaluate the reliability of news articles and prioritize the analysis of data from reliable media outlets. It can evaluate the reliability of corporate financial data and prioritize the analysis of data from reliable companies. By prioritizing the analysis of highly reliable data, the accuracy of the analysis results can be improved.

[0069] The proposal team can also select the most suitable proposal method by referring to the user's past decision-making history when making a proposal. For example, if a user has previously adopted a particular strategy and achieved success, proposals based on that strategy can be prioritized. If a user has a tendency to avoid certain risks in the past, proposals that avoid those risks can be made. If a user has previously shown interest in a particular market, proposals related to that market can be prioritized. This allows for the provision of more relevant proposals based on the user's past decision-making history.

[0070] The query section can also select the optimal processing method by referring to the user's past query results during query processing. For example, if a user has frequently referred to a particular query result in the past, that query result can be provided preferentially. If a user has frequently searched for a particular piece of information in the past, that information can be provided preferentially. If a user has preferred a particular format of query results in the past, the query results can be provided in that format. This allows for the provision of more relevant query results based on the user's past query results.

[0071] The analysis unit can also adjust the analysis method based on the data source. For example, natural language processing can be applied to data collected from social media. Text mining can be applied to data collected from news articles. Statistical analysis can be applied to corporate financial data. By applying the appropriate analysis method based on the data source, the accuracy of the analysis can be improved.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit collects data. The collection unit collects data such as social media, news articles, corporate financial data, patent information, and satellite imagery. For example, the collection unit collects social media posts, text data from news articles, corporate financial reports, patent information, and satellite imagery. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit comprehensively analyzes text, images, and numerical data, for example, using multimodal AI. For example, the analysis unit understands consumer interests from social media posts, analyzes industry trends from news articles, and evaluates a company's management status from corporate financial data. Step 3: The tracking unit tracks the activities of competitors based on the data analyzed by the analysis unit. For example, the tracking unit tracks new product announcements and changes in market strategies of competitors in real time. For example, if a competitor announces a new product, the tracking unit acquires that information in real time, analyzes the competitor's market strategy, and incorporates it into its own strategy. Step 4: The alert unit provides automated alerts based on the information obtained by the tracking unit. For example, the alert unit provides automated alerts for new product announcements or changes in market strategies by competitors. For example, if a competitor announces a new product, the alert unit will acquire that information in real time and provide an automated alert. Step 5: The proposal unit generates strategic proposals based on the information provided by the alert unit. The proposal unit autonomously generates strategic proposals based, for example, on new product announcements or changes in market strategies of competitors. For example, if a competitor announces a new product, the proposal unit acquires that information in real time, analyzes the competitor's market strategy, and incorporates it into its own strategy.

[0074] (Example of form 2) An AI-driven platform according to an embodiment of the present invention is a system that integrates multiple data sources and predicts market trends in real time. This system collects diverse data such as social media, news articles, corporate financial data, patent information, and satellite imagery, and provides industry trends, competitive analysis, and consumer behavior predictions by comprehensively analyzing this data using multimodal AI. Furthermore, a competitive analysis AI agent tracks the activities of competitors in real time, provides automatic alerts for new product announcements and changes in market strategies of competitors, and autonomously generates strategic proposals based on these alerts. For example, the AI-driven platform collects data from social media posts, text data from news articles, corporate financial reports, patent information, satellite imagery, etc. This data is acquired and integrated in real time. Next, multimodal AI analyzes this data to perform industry trends, competitive analysis, and consumer behavior predictions. For example, it grasps consumer interests from social media posts, analyzes industry trends from news articles, and evaluates the management status of companies from corporate financial data. Furthermore, a competitive analysis AI agent tracks the activities of competitors in real time, provides automatic alerts for new product announcements and changes in market strategies of competitors, and autonomously generates strategic proposals based on these alerts. For example, when a competitor announces a new product, the platform can acquire that information in real time, analyze the competitor's market strategy, and incorporate it into its own strategy. This prevents missing important market signals due to information overload, facilitates the integration and analysis of diverse data sources, and enables a decision-making process that keeps pace with the speed of market change. It can also grasp the complexity and regional differences of the global market and provide detailed market insights by region and industry. In this way, the AI-driven platform can integrate multiple data sources and predict market trends in real time.

[0075] The AI-driven platform according to this embodiment comprises a data collection unit, an analysis unit, a tracking unit, an alert unit, and a suggestion unit. The data collection unit collects data. The data collection unit collects data such as social media, news articles, corporate financial data, patent information, and satellite imagery. For example, the data collection unit collects social media posts, text data from news articles, corporate financial reports, patent information, and satellite imagery. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses multimodal AI to comprehensively analyze text, images, and numerical data. For example, the analysis unit grasps consumer interests from social media posts, analyzes industry trends from news articles, and evaluates the management status of companies from corporate financial data. The tracking unit tracks the activities of competitors based on the data analyzed by the analysis unit. For example, the tracking unit tracks new product announcements and changes in market strategies of competitors in real time. The tracking unit acquires information in real time, for example, when a competitor announces a new product, analyzes the competitor's market strategy, and incorporates it into the company's own strategy. The alerting unit provides automatic alerts based on the information obtained by the tracking unit. The alerting unit provides automatic alerts, for example, for new product announcements or changes in market strategies by competitors. The alerting unit acquires information in real time, for example, when a competitor announces a new product, and provides automatic alerts. The proposal unit generates strategic proposals based on the information provided by the alerting unit. The proposaling unit autonomously generates strategic proposals, for example, based on new product announcements or changes in market strategies by competitors. The proposaling unit acquires information in real time, for example, when a competitor announces a new product, analyzes the competitor's market strategy, and incorporates it into the company's own strategy. As a result, the AI-driven platform according to this embodiment can integrate multiple data sources and predict market trends in real time.

[0076] The data collection unit collects data from sources such as social media, news articles, corporate financial data, patent information, and satellite imagery. Specifically, when collecting social media posts, it uses APIs to retrieve content in real time and filters relevant posts based on keywords and hashtags. When collecting text data from news articles, it uses web scraping technology to automatically collect articles from news sites and analyzes the content using natural language processing technology. When collecting corporate financial reports, it downloads reports from the company's official website or financial databases and converts them into text data using OCR technology. When collecting patent information, it retrieves patent documents from the Japan Patent Office database and extracts relevant patents based on patent classification codes and keywords. When collecting satellite imagery, it obtains high-resolution images from satellite data provision services and uses image analysis technology to detect changes and anomalies on the Earth's surface. In this way, the data collection unit collects a wide range of data from diverse data sources and provides a foundation for understanding market trends in real time. Furthermore, the data collection unit can flexibly respond to specific situations and conditions by adjusting the frequency and accuracy of data collection. For example, by increasing the frequency of data collection in accordance with specific events or seasons, more detailed information can be obtained. Furthermore, the collected data is stored on a cloud server, making it accessible to the analysis and tracking units. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0077] The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit uses multimodal AI to comprehensively analyze text, images, and numerical data. Specifically, to understand consumer interests from social media posts, it uses natural language processing technology to analyze post content and perform sentiment analysis and topic modeling. To analyze industry trends from news articles, it classifies the content of articles, extracts important keywords and phrases, and grasps trends. To evaluate a company's management status from corporate financial data, it calculates financial indicators and analyzes performance fluctuations by comparing them with historical data. Furthermore, when analyzing patent information, it analyzes the content of patent documents to understand technological advancements and the R&D trends of competing companies. When analyzing satellite imagery, it uses image recognition technology to detect changes and anomalies on the Earth's surface and explores applications in fields such as agriculture and urban planning. As a result, the analysis unit can quickly and accurately analyze collected data and grasp the surrounding risk situation in real time. Furthermore, the analysis unit can also utilize historical data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past consumer interest data, it can predict fluctuations in demand for specific products and services and formulate future marketing strategies. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis unit to not only understand the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0078] The tracking unit tracks the activities of competitors based on data analyzed by the analysis unit. For example, the tracking unit tracks new product announcements and changes in market strategies of competitors in real time. Specifically, it monitors the official websites and press releases of competitors to automatically detect new product announcements and other important announcements. It also collects information on competitors from social media and news articles and integrates it with the data obtained by the analysis unit to analyze the market strategies of competitors. Furthermore, it uses patent information to track the research and development trends of competitors and understand the status of new technology development and patent application trends. As a result, the tracking unit can grasp the activities of competitors in real time and provide information to reflect in the company's own strategy. For example, if a competitor announces a new product, the information can be obtained in real time and reflected in the company's own product development and marketing strategy. In addition, by tracking changes in the market strategies of competitors and reviewing the company's own strategy as needed, it is possible to maintain competitiveness. Furthermore, by monitoring the activities of competitors over the long term and understanding trends and patterns, the tracking unit can predict future market trends. As a result, the tracking unit can track the activities of competitors in real time and support quick and appropriate responses.

[0079] The alert unit provides automated alerts based on information obtained by the tracking unit. For example, the alert unit provides automated alerts regarding new product announcements or changes in market strategies by competitors. Specifically, when a competitor announces a new product, it acquires that information in real time and provides an automated alert. Alerts are delivered using multiple communication methods such as email, SMS, and push notifications, ensuring that important information is conveyed quickly and reliably. Furthermore, the content of the alerts is customized according to the user's role and responsibilities, providing only the necessary information. For example, product development personnel are provided with technical details of new products, and marketing personnel are provided with information regarding changes in market strategies. This allows the alert unit to quickly provide appropriate information to each user, minimizing the risk of damage. In addition, the alert unit can collect user feedback and continuously improve the accuracy and effectiveness of the alert content. For example, based on feedback from users who receive alerts, the timing and content of alerts are reviewed to provide more effective information. The alert unit can also use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing warnings early. This allows the alert unit to quickly and reliably provide information to users, minimizing the risk of damage.

[0080] The Proposal Department generates strategic proposals based on information provided by the Alert Department. For example, the Proposal Department autonomously generates strategic proposals based on new product announcements or changes in market strategies by competitors. Specifically, when a competitor announces a new product, it acquires this information in real time, analyzes the competitor's market strategy, and incorporates it into its own strategy. Using AI, the Proposal Department analyzes this data and simulates multiple scenarios to identify the most effective strategy. For example, it proposes the timing of new product launches, pricing, and promotional strategies to strengthen the company's competitiveness. The Proposal Department can also utilize historical data and statistical information to provide long-term strategic proposals. For example, it predicts future market fluctuations based on past market trends and competitor activity, and formulates appropriate strategies. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the Proposal Department to always provide highly accurate strategic proposals based on the latest information, supporting quick and appropriate responses. Additionally, the Proposal Department provides dashboards and reports to visually display the proposals in an easy-to-understand manner, enabling users to easily understand and implement the proposals. This allows the proposal department to provide users with effective strategic proposals and improve the company's competitiveness.

[0081] The data collection unit collects data such as social media posts, news articles, corporate financial data, patent information, and satellite imagery. For example, the data collection unit collects social media posts. For example, the data collection unit collects text data from news articles. For example, the data collection unit collects corporate financial reports. For example, the data collection unit collects patent information. For example, the data collection unit collects satellite imagery. This allows for more comprehensive market trend forecasting by collecting data from diverse data sources. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into a generating AI, which can then analyze and collect the posted data.

[0082] The analysis unit uses multimodal AI to comprehensively analyze text, images, and numerical data. For example, the analysis unit can understand consumer interests from social media posts. For example, the analysis unit can analyze industry trends from news articles. For example, the analysis unit can evaluate a company's management status from corporate financial data. By using multimodal AI, it becomes possible to comprehensively analyze data in different formats and predict market trends with greater accuracy. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input social media posts into a generating AI, which can then analyze the posts to understand consumer interests.

[0083] The tracking unit tracks new product announcements and changes in market strategies of competitors in real time. For example, if a competitor announces a new product, the tracking unit acquires that information in real time. For example, the tracking unit analyzes the market strategies of competitors. For example, the tracking unit incorporates the competitor's new product announcements and changes in market strategies into its own strategy. This enables rapid response by tracking the movements of competitors in real time. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input information on a competitor's new product announcement into a generating AI, which can analyze the information and acquire it in real time.

[0084] The alert unit provides automated alerts regarding the activities of competitors. For example, if a competitor announces a new product, the alert unit acquires that information in real time and provides an automated alert. The alert unit also provides automated alerts regarding changes in the market strategy of competitors. By providing automated alerts, it becomes possible to respond quickly without missing important market signals. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input information about a competitor's new product announcement into a generating AI, which can then analyze the information and provide an automated alert.

[0085] The proposal department autonomously generates strategic proposals based on the actions of competitors. For example, if a competitor announces a new product, the proposal department acquires that information in real time, analyzes the competitor's market strategy, and reflects it in its own strategy. The proposal department autonomously generates strategic proposals based on changes in the competitor's market strategy. This enables rapid and effective strategy planning by autonomously generating strategic proposals based on the actions of competitors. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input information on a competitor's new product announcement into a generating AI, which can then analyze the information and autonomously generate strategic proposals.

[0086] The dashboard section provides a customizable dashboard. The dashboard section allows users to customize and display the information they need. The dashboard section allows users to customize the placement of widgets. The dashboard section allows users to customize the types of data to display. This enables efficient information management because users can customize and display the information they need. Some or all of the above-described processes in the dashboard section may be performed using AI, for example, or not using AI. For example, the dashboard section can input a user's customization request into a generating AI, which can then generate a customized dashboard.

[0087] The query unit accepts queries in natural language. The query unit allows users to input queries in natural language, for example. The query unit analyzes queries using natural language processing techniques, for example. The query unit can increase the number of languages ​​it supports, for example. This allows users to input queries in natural language, enabling intuitive operation. Some or all of the above processing in the query unit may be performed using AI, for example, or not using AI. For example, the query unit can input the user's natural language query into a generating AI, which can then analyze the query and provide results.

[0088] The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit reduces the frequency of data collection and collects only important data. For example, if the user is relaxed, the data collection unit increases the frequency of data collection and collects detailed data. For example, if the user is in a hurry, the data collection unit prioritizes collecting important data in real time. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the timing of data collection.

[0089] The data collection unit evaluates the reliability of each data source and prioritizes the collection of reliable data. For example, the data collection unit analyzes social media posts and prioritizes the collection of data from reliable accounts. For example, the data collection unit evaluates the reliability of news articles and prioritizes the collection of data from reliable media outlets. For example, the data collection unit evaluates the reliability of corporate financial data and prioritizes the collection of data from reliable companies. This improves the accuracy of the analysis results by prioritizing the collection of reliable data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into a generating AI, which can evaluate the reliability and prioritize the collection of reliable data.

[0090] The data collection unit filters data based on specific keywords or topics during the data collection process. For example, the data collection unit filters social media posts by specific keywords and collects relevant data. For example, the data collection unit filters news articles by specific topics and collects relevant data. For example, the data collection unit filters corporate financial data by specific indicators and collects relevant data. This allows for the efficient collection of highly relevant data by filtering data based on specific keywords or topics. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input social media posts into a generating AI, which can then filter them by specific keywords and collect relevant data.

[0091] The data collection unit estimates the user's emotions and determines the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes collecting only important data. If the user is relaxed, the data collection unit prioritizes collecting detailed data. If the user is in a hurry, the data collection unit prioritizes collecting important data in real time. This allows for the priority collection of important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can analyze the emotions and determine the priority of the data.

[0092] The data collection unit prioritizes collecting highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit prioritizes collecting data related to that region. For example, if the user is on the move, the data collection unit collects data related to the user's current location in real time. For example, if the user is in a specific country, the data collection unit prioritizes collecting data related to that country. This makes it possible to predict region-specific market trends by collecting highly relevant data based on 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. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then analyze the location information and prioritize collecting highly relevant data.

[0093] The data collection unit analyzes the user's social media activity and collects relevant data during data collection. For example, the data collection unit collects data related to topics the user has shown interest in on social media. For example, the data collection unit analyzes the content of posts from accounts the user follows and collects relevant data. For example, the data collection unit analyzes the activities of groups and communities the user participates in and collects relevant data. This makes it possible to collect data based on the user's interests 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. For example, the data collection unit can input the user's social media activity data into a generating AI, which can then analyze the activity and collect relevant data.

[0094] The analysis unit estimates the user's emotions and adjusts the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. By adjusting the presentation of the analysis according to the user's emotions, the analysis unit can provide results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the presentation of the analysis.

[0095] The analysis unit adjusts the level of detail of the analysis based on the importance of the data. For example, the analysis unit performs a detailed analysis on important data and a simplified analysis on less important data. For example, the analysis unit applies multiple analysis methods to important data to provide detailed results. For example, the analysis unit collects additional data and performs a more detailed analysis on highly important data. In this way, by adjusting the level of detail of the analysis based on the importance of the data, detailed analysis results can be provided for important data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, which can analyze the importance and adjust the level of detail of the analysis.

[0096] The analysis unit applies different analysis algorithms depending on the data category. For example, the analysis unit applies a natural language processing algorithm to text data and an image analysis algorithm to image data. For example, the analysis unit applies a statistical analysis algorithm to numerical data and a text mining algorithm to text data. For example, the analysis unit applies a deep learning algorithm to image data and a machine learning algorithm to numerical data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, and the generating AI can apply an analysis algorithm appropriate to the category.

[0097] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides an analysis result with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis result. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input the user's emotion data into a generative AI, which can then analyze the emotions and adjust the length of the analysis.

[0098] The analysis unit determines the priority of analysis based on the data collection timing. For example, the analysis unit prioritizes the analysis of the most recent data and postpones the analysis of older data. For example, the analysis unit prioritizes the analysis of data collected during a specific period to grasp the trends during that period. For example, the analysis unit prioritizes the analysis of data collected in real time to provide immediate insights. This allows for the prioritization of analysis of the most recent data by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI, which can then analyze the collection timing to determine the priority of analysis.

[0099] The analysis unit adjusts the order of analysis based on the relevance of the data. For example, the analysis unit prioritizes analyzing highly relevant data and postpones analyzing less relevant data. For example, the analysis unit prioritizes analyzing data related to a specific topic and provides insights into that topic. For example, the analysis unit groups highly relevant data and analyzes them all at once. This allows for prioritizing the analysis of highly relevant data by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can then analyze the relevance and adjust the order of analysis.

[0100] The tracking unit estimates the user's emotions and adjusts the tracking criteria based on the estimated emotions. For example, if the user is stressed, the tracking unit prioritizes tracking the activities of important competitors. If the user is relaxed, the tracking unit tracks the activities of detailed competitors. If the user is in a hurry, the tracking unit tracks the activities of important competitors in real time. This allows for more appropriate tracking by adjusting the tracking criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not using AI. For example, the tracking unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the tracking criteria.

[0101] The tracking unit improves tracking accuracy by considering the relationships between competing companies during tracking. For example, the tracking unit considers alliances between competing companies and tracks the activities of all affiliated companies at once. For example, the tracking unit considers competitive relationships between competing companies and prioritizes tracking the activities of companies with high competition. For example, the tracking unit considers market share between competing companies and prioritizes tracking the activities of companies with large market share. This improves tracking accuracy by considering the relationships between competing companies. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input data on the relationships between competing companies into a generating AI, which can then analyze the relationships to improve tracking accuracy.

[0102] The tracking unit performs tracking while considering the attribute information of competitors. For example, the tracking unit considers the industry attributes of competitors and prioritizes tracking the activities of other companies in the same industry. For example, the tracking unit considers the size of competitors and prioritizes tracking the activities of large companies. For example, the tracking unit considers the regional attributes of competitors and prioritizes tracking the activities of companies related to a specific region. This allows for more detailed tracking by considering the attribute information of competitors. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input the attribute information of competitors into a generating AI, and the generating AI can analyze the attribute information and perform tracking.

[0103] The tracking unit estimates the user's emotions and adjusts the order in which tracking results are displayed based on the estimated emotions. For example, if the user is stressed, the tracking unit displays the activities of important competitors first. If the user is relaxed, the tracking unit displays detailed competitor activities sequentially. If the user is in a hurry, the tracking unit displays the activities of important competitors first in real time. This allows for the provision of optimal information to the user by adjusting the display order of tracking results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the tracking unit may be performed using AI or not. For example, the tracking unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the display order of tracking results.

[0104] The tracking unit performs tracking while considering the geographical distribution of competitors. For example, the tracking unit considers the location of the competitors' headquarters and prioritizes tracking trends related to the headquarters location. For example, the tracking unit considers the competitors' major markets and prioritizes tracking trends related to those major markets. For example, the tracking unit considers the competitors' production bases and prioritizes tracking trends related to those production bases. This makes it possible to perform region-specific tracking by considering the geographical distribution of competitors. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input geographical distribution data of competitors into a generating AI, which can then analyze the geographical distribution and perform tracking.

[0105] The tracking unit improves tracking accuracy by referring to relevant literature of competitors during tracking. For example, the tracking unit refers to patent documents of competitors to track trends in new technologies. For example, the tracking unit refers to research papers of competitors to track trends in research and development. For example, the tracking unit refers to market reports of competitors to track trends in market strategies. This improves tracking accuracy by referring to relevant literature of competitors. Some or all of the above processing in the tracking unit may be performed using AI, for example, or without AI. For example, the tracking unit can input relevant literature data of competitors into a generating AI, which can then analyze the literature to improve tracking accuracy.

[0106] The alert unit estimates the user's emotions and adjusts how alerts are displayed based on the estimated emotions. For example, if the user is nervous, the alert unit displays a simple and highly visible alert. If the user is relaxed, the alert unit displays an alert with detailed information. If the user is in a hurry, the alert unit displays a concise alert. By adjusting how alerts are displayed according to the user's emotions, the system can provide the most appropriate alerts for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into a generative AI, which can analyze the emotions and adjust how alerts are displayed.

[0107] The alert unit predicts the current alert by referring to past alert data when an alert occurs. For example, the alert unit analyzes past alert data and predicts an alert when a similar pattern occurs. For example, the alert unit predicts the occurrence of an alert under specific conditions from past alert data. For example, the alert unit predicts the frequency and timing of alerts based on past alert data. This makes it easier to predict the current alert by referring to past alert data. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input past alert data into a generating AI, which can analyze the data and predict the current alert.

[0108] The alert unit applies different alert methods to each competitor's category when an alert occurs. For example, the alert unit applies different alert methods depending on the competitor's industry category. For example, the alert unit applies different alert methods depending on the competitor's size. For example, the alert unit applies different alert methods depending on the competitor's regional attributes. By applying different alert methods to each competitor's category, more appropriate alerts can be provided. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input competitor category data into a generating AI, and the generating AI can apply an alert method appropriate to the category.

[0109] The alert unit estimates the user's emotions and adjusts the importance of alerts based on the estimated emotions. For example, if the user is stressed, the alert unit displays only important alerts. If the user is relaxed, the alert unit displays detailed alerts. If the user is in a hurry, the alert unit displays concise and important alerts. This allows for the priority delivery of important alerts by adjusting their importance according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the importance of alerts.

[0110] The alert unit analyzes changes in alerts based on the actions of competitors when an alert occurs. For example, the alert unit generates an alert based on a competitor's new product announcement and analyzes its changes. For example, the alert unit generates an alert based on a competitor's change in market strategy and analyzes its changes. For example, the alert unit generates an alert based on a competitor's change in financial condition and analyzes its changes. By analyzing changes in alerts based on the actions of competitors, it is possible to provide more appropriate alerts. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input competitor action data into a generating AI, which can then analyze the actions and analyze changes in alerts.

[0111] The alert unit analyzes alerts by referring to relevant market data of competitors when an alert occurs. For example, the alert unit analyzes alerts by referring to market share data of competitors. For example, the alert unit analyzes alerts by referring to sales data of competitors. For example, the alert unit analyzes alerts by referring to customer data of competitors. By referring to relevant market data of competitors, the accuracy of alerts is improved. Some or all of the above processing in the alert unit may be performed using AI, for example, or without AI. For example, the alert unit can input market data of competitors into a generating AI, and the generating AI can analyze the data and analyze the alerts.

[0112] The suggestion unit estimates the user's emotions and adjusts its suggestion method based on the estimated emotions. For example, if the user is nervous, the suggestion unit will provide simple and highly visible suggestions. If the user is relaxed, the suggestion unit will provide detailed suggestions. If the user is in a hurry, the suggestion unit will provide concise suggestions. By adjusting the suggestion method according to the user's emotions, the system can provide the most suitable suggestions for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the suggestion method.

[0113] The proposal department, when making a proposal, analyzes the past trends of competing companies to select the optimal proposal method. For example, the proposal department may analyze past new product announcement data of competing companies to select the optimal proposal method. For example, the proposal department may analyze past market strategy data of competing companies to select the optimal proposal method. For example, the proposal department may analyze past financial data of competing companies to select the optimal proposal method. In this way, the optimal proposal method can be selected by analyzing the past trends of competing companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past trend data of competing companies into a generating AI, and the generating AI can analyze the data to select the optimal proposal method.

[0114] The proposal department customizes the proposal based on the current market conditions of competitors. For example, the proposal department customizes the proposal based on the current market share data of competitors. For example, the proposal department customizes the proposal based on the current sales data of competitors. For example, the proposal department customizes the proposal based on the current customer data of competitors. By customizing the proposal based on the current market conditions of competitors, more effective proposals become possible. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input data on the current market conditions of competitors into a generating AI, which can then analyze the data and customize the proposal.

[0115] The suggestion unit estimates the user's emotions and determines the priority of suggestions based on the estimated emotions. For example, if the user is tense, the suggestion unit will prioritize important suggestions. For example, if the user is relaxed, the suggestion unit will prioritize detailed suggestions. For example, if the user is in a hurry, the suggestion unit will prioritize concise and important suggestions. In this way, by determining the priority of suggestions according to the user's emotions, important suggestions can be provided preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI, which can analyze the emotions and determine the priority of suggestions.

[0116] The proposal department selects the optimal proposal method when making a proposal, taking into account the geographical location information of competing companies. For example, the proposal department may select a proposal method based on the location of the competing company's headquarters. For example, the proposal department may select a proposal method based on the competing company's main market. For example, the proposal department may select a proposal method based on the competing company's production base. This allows for region-specific proposals by considering the geographical location information of competing companies. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input the geographical location information of competing companies into a generating AI, which can then analyze the information and select the optimal proposal method.

[0117] The proposal department analyzes the social media activities of competitors and proposes methods for proposals. For example, the proposal department analyzes the content of competitors' social media posts and selects the most suitable proposal method. For example, the proposal department analyzes the reactions of competitors' social media followers and selects the most suitable proposal method. For example, the proposal department analyzes successful social media campaigns of competitors and selects the most suitable proposal method. In this way, the most suitable proposal method can be selected by analyzing the social media activities of competitors. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input competitors' social media activity data into a generating AI, which can then analyze the data and propose methods for proposals.

[0118] The dashboard section estimates the user's emotions and adjusts the dashboard display method based on the estimated emotions. For example, if the user is stressed, the dashboard section provides a simple and highly visible dashboard. For example, if the user is relaxed, the dashboard section provides a dashboard with detailed information. For example, if the user is in a hurry, the dashboard section provides a dashboard that gets straight to the point. This allows for the provision of optimal information to the user by adjusting the dashboard display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dashboard section may be performed using AI, or not using AI. For example, the dashboard section can input user emotion data into a generative AI, which can analyze the emotions and adjust the dashboard display method.

[0119] The dashboard section, when displaying the dashboard, selects the optimal display method by referring to the user's past operation history. For example, the dashboard section analyzes the user's past operation history and prioritizes the display of frequently used functions. For example, the dashboard section prioritizes the display of specific information from the user's past operation history. For example, the dashboard section provides a customized dashboard based on the user's past operation history. This allows the dashboard section to provide the optimal display method for the user by referring to the user's past operation history. Some or all of the above processing in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input the user's past operation history data into a generating AI, which can then analyze the data and select the optimal display method.

[0120] The dashboard section estimates the user's emotions and adjusts the dashboard's operating procedures based on the estimated emotions. For example, if the user is tense, the dashboard section provides simple and intuitive operating procedures. For example, if the user is relaxed, the dashboard section provides detailed operating procedures. For example, if the user is in a hurry, the dashboard section provides concise operating procedures. By adjusting the dashboard's operating procedures according to the user's emotions, optimal operation is possible for the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dashboard section may be performed using AI, or not using AI. For example, the dashboard section can input user emotion data into a generative AI, which can analyze the emotions and adjust the dashboard's operating procedures.

[0121] The dashboard section selects the optimal display method when displaying the dashboard, taking into account the user's device information. For example, if the user is using a smartphone, the dashboard section provides a display method that matches the screen size. For example, if the user is using a tablet, the dashboard section provides a display method optimized for a larger screen. For example, if the user is using a desktop, the dashboard section provides a display method that includes detailed information. In this way, by taking into account the user's device information, it is possible to provide a display method optimized for the device. Some or all of the above processing in the dashboard section may be performed using AI, for example, or without AI. For example, the dashboard section can input the user's device information into a generating AI, and the generating AI can analyze the information and select the optimal display method.

[0122] The query unit estimates the user's emotions and adjusts the query processing method based on the estimated emotions. For example, if the user is nervous, the query unit provides simple and easy-to-understand query results. If the user is relaxed, the query unit provides detailed query results. If the user is in a hurry, the query unit provides concise query results. By adjusting the query processing method according to the user's emotions, the system can provide the user with the most optimal query results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the query unit may be performed using AI or not. For example, the query unit can input user emotion data into a generative AI, which can analyze the emotions and adjust the query processing method.

[0123] The query unit selects the optimal processing method by referring to the user's past query history when processing queries. For example, the query unit analyzes the user's past query history and prioritizes processing frequently used queries. For example, the query unit prioritizes processing specific information from the user's past query history. For example, the query unit provides customized query results based on the user's past query history. This allows the system to provide the user with the most optimal query results by referring to the user's past query history. Some or all of the above processing in the query unit may be performed using AI, for example, or without AI. For example, the query unit can input the user's past query history data into a generating AI, which can then analyze the data and select the optimal processing method.

[0124] The query unit estimates the user's emotions and prioritizes queries based on the estimated emotions. For example, if the user is stressed, the query unit prioritizes important queries. If the user is relaxed, the query unit prioritizes detailed queries. If the user is in a hurry, the query unit prioritizes concise and important queries. This ensures that important queries are prioritized by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the query unit may be performed using AI or not. For example, the query unit can input user emotion data into a generative AI, which can analyze the emotions and determine the query priorities.

[0125] The query unit selects the optimal processing method when processing queries, taking into account the user's device information. For example, if the user is using a smartphone, the query unit provides query results that are adapted to the screen size. If the user is using a tablet, the query unit provides query results optimized for a larger screen. If the user is using a desktop, the query unit provides query results that include detailed information. In this way, by taking into account the user's device information, it is possible to provide query results optimized for the device. Some or all of the above processing in the query unit may be performed using AI, for example, or without AI. For example, the query unit can input the user's device information into a generating AI, which can then analyze the information and select the optimal processing method.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The suggestion function can also estimate the user's emotions and adjust the content of its suggestions based on those emotions. For example, if the user is stressed, the suggestion function can provide simple and easy-to-implement suggestions. If the user is relaxed, the suggestion function can provide suggestions that include detailed analysis and multiple options. If the user is in a hurry, the suggestion function can provide concise and quick suggestions. This enables flexible suggestions that respond to the user's emotions and supports their decision-making.

[0128] The data collection unit can also prioritize the collection of highly relevant data by referring to the user's past behavior history during data collection. For example, if a user has previously shown interest in a particular industry, data related to that industry can be prioritized. If a user has previously frequently checked the trends of a particular company, data related to that company can be prioritized. If a user has previously shown interest in a particular region, data related to that region can be prioritized. This allows for the efficient collection of more relevant data based on the user's past behavior history.

[0129] The analysis unit can evaluate the reliability of data and prioritize the analysis of highly reliable data. For example, it can analyze social media posts and prioritize data from reliable accounts. It can evaluate the reliability of news articles and prioritize the analysis of data from reliable media outlets. It can evaluate the reliability of corporate financial data and prioritize the analysis of data from reliable companies. By prioritizing the analysis of highly reliable data, the accuracy of the analysis results can be improved.

[0130] The tracking unit can also estimate the user's emotions and adjust the tracking frequency based on that estimation. For example, if the user is stressed, the tracking frequency can be reduced, and only important events can be tracked. If the user is relaxed, the tracking frequency can be increased, allowing for more detailed tracking of their activities. If the user is in a hurry, important activities can be prioritized and tracked in real time. This allows for more appropriate tracking by adjusting the tracking frequency according to the user's emotions.

[0131] The alerting unit can also estimate the user's emotions and adjust the content of the alert based on those emotions. For example, if the user is stressed, the alerting unit can provide a simple alert containing only essential information. If the user is relaxed, the alerting unit can provide an alert with more detailed information. If the user is in a hurry, the alerting unit can provide a concise and rapid alert. This enables flexible alerts that respond to the user's emotions, supporting their decision-making.

[0132] The proposal team can also select the most suitable proposal method by referring to the user's past decision-making history when making a proposal. For example, if a user has previously adopted a particular strategy and achieved success, proposals based on that strategy can be prioritized. If a user has a tendency to avoid certain risks in the past, proposals that avoid those risks can be made. If a user has previously shown interest in a particular market, proposals related to that market can be prioritized. This allows for the provision of more relevant proposals based on the user's past decision-making history.

[0133] The dashboard can also estimate the user's emotions and adjust the dashboard layout based on those emotions. For example, if the user is stressed, a simple and highly visible layout can be provided. If the user is relaxed, a layout with detailed information can be provided. If the user is in a hurry, a concise and quick layout can be provided. This allows for flexible layouts that respond to the user's emotions, supporting their information gathering.

[0134] The query section can also select the optimal processing method by referring to the user's past query results during query processing. For example, if a user has frequently referred to a particular query result in the past, that query result can be provided preferentially. If a user has frequently searched for a particular piece of information in the past, that information can be provided preferentially. If a user has preferred a particular format of query results in the past, the query results can be provided in that format. This allows for the provision of more relevant query results based on the user's past query results.

[0135] The data collection unit can also estimate the user's emotions during data collection and adjust the type of data collected based on the estimated emotions. For example, if the user is stressed, only important data can be collected. If the user is relaxed, detailed data can be collected. If the user is in a hurry, important data can be prioritized and collected in real time. This allows for more appropriate data collection by adjusting the type of data collected according to the user's emotions.

[0136] The analysis unit can also adjust the analysis method based on the data source. For example, natural language processing can be applied to data collected from social media. Text mining can be applied to data collected from news articles. Statistical analysis can be applied to corporate financial data. By applying the appropriate analysis method based on the data source, the accuracy of the analysis can be improved.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The collection unit collects data. The collection unit collects data such as social media, news articles, corporate financial data, patent information, and satellite imagery. For example, the collection unit collects social media posts, text data from news articles, corporate financial reports, patent information, and satellite imagery. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit comprehensively analyzes text, images, and numerical data, for example, using multimodal AI. For example, the analysis unit understands consumer interests from social media posts, analyzes industry trends from news articles, and evaluates a company's management status from corporate financial data. Step 3: The tracking unit tracks the activities of competitors based on the data analyzed by the analysis unit. For example, the tracking unit tracks new product announcements and changes in market strategies of competitors in real time. For example, if a competitor announces a new product, the tracking unit acquires that information in real time, analyzes the competitor's market strategy, and incorporates it into its own strategy. Step 4: The alert unit provides automated alerts based on the information obtained by the tracking unit. For example, the alert unit provides automated alerts for new product announcements or changes in market strategies by competitors. For example, if a competitor announces a new product, the alert unit will acquire that information in real time and provide an automated alert. Step 5: The proposal unit generates strategic proposals based on the information provided by the alert unit. The proposal unit autonomously generates strategic proposals based, for example, on new product announcements or changes in market strategies of competitors. For example, if a competitor announces a new product, the proposal unit acquires that information in real time, analyzes the competitor's market strategy, and incorporates it into its own strategy.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the data collection unit, analysis unit, tracking unit, alert unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and integrates the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using multimodal AI. The tracking unit is implemented in the specific processing unit 290 of the data processing unit 12 and tracks the activities of competitors in real time. The alert unit is implemented in the specific processing unit 46A of the smart device 14 and provides automatic alerts for new product announcements and changes in market strategies of competitors. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and autonomously generates strategic proposals based on the activities of competitors. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the data collection unit, analysis unit, tracking unit, alert unit, and proposal unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the smart glasses 214 and integrates the data with the control unit 46A. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and analyzes the data using multimodal AI. The tracking unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and tracks the activities of competitors in real time. The alert unit is implemented, for example, in the control unit 46A of the smart glasses 214 and provides automatic alerts for new product announcements and changes in market strategies of competitors. The proposal unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12 and autonomously generates strategic proposals based on the activities of competitors. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the data collection unit, analysis unit, tracking unit, alert unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and integrates the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using multimodal AI. The tracking unit is implemented in the specific processing unit 290 of the data processing unit 12 and tracks the activities of competitors in real time. The alert unit is implemented in the specific processing unit 46A of the headset terminal 314 and provides automatic alerts for new product announcements and changes in market strategies of competitors. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and autonomously generates strategic proposals based on the activities of competitors. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Each of the multiple elements described above, including the data collection unit, analysis unit, tracking unit, alert unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the robot 414 and integrates the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and analyzes the data using multimodal AI. The tracking unit is implemented in the specific processing unit 290 of the data processing unit 12 and tracks the activities of competitors in real time. The alert unit is implemented in the specific processing unit 46A of the robot 414 and provides automatic alerts for new product announcements and changes in market strategies of competitors. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12 and autonomously generates strategic proposals based on the activities of competitors. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A tracking unit tracks the trends of competing companies based on the data analyzed by the aforementioned analysis unit, An alert unit provides an automatic alert based on the information obtained by the tracking unit, The system includes a proposal unit that generates strategic proposals based on information provided by the alert unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as social media, news articles, corporate financial data, patent information, and satellite imagery. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Multimodal AI enables integrated analysis of text, images, and numerical data. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned tracking unit is Track competitors' new product announcements and changes in market strategy in real time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The alert unit is, Provides automated alerts on the activities of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Autonomously generates strategic proposals based on the actions of competing companies. The system described in Appendix 1, characterized by the features described herein. (Note 7) Features a dashboard section that provides a customizable dashboard. The system described in Appendix 1, characterized by the features described herein. (Note 8) It has a query section that accepts queries in natural language. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Evaluate the reliability of each data source and prioritize collecting the most reliable data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, filter it based on specific keywords and topics. The system described in Appendix 1, characterized by the features described herein. (Note 12) 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 13) The aforementioned collection unit is Prioritize the collection of relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) 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 16) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) 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 19) The aforementioned analysis unit, Prioritize analysis based on the data collection period. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit, 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 21) The aforementioned tracking unit is We estimate the user's emotions and adjust the tracking criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned tracking unit is When tracking, we improve tracking accuracy by considering the relationships between competing companies. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned tracking unit is When tracking, consider the attribute information of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned tracking unit is It estimates the user's sentiment and adjusts the order in which tracking results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned tracking unit is When tracking, the geographical distribution of competitors is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned tracking unit is During tracking, we refer to relevant literature from competitors to improve tracking accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 27) The alert unit is, It estimates the user's emotions and adjusts how alerts are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The alert unit is, When an alert occurs, past alert data is referenced to predict the current alert. The system described in Appendix 1, characterized by the features described herein. (Note 29) The alert unit is, When an alert is triggered, different alert methods are applied depending on the category of the competitor. The system described in Appendix 1, characterized by the features described herein. (Note 30) The alert unit is, It estimates the user's emotions and adjusts the importance of alerts based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The alert unit is, When an alert is triggered, the alert changes based on the actions of competitors. The system described in Appendix 1, characterized by the features described herein. (Note 32) The alert unit is, When an alert is triggered, the alert is analyzed by referring to relevant market data from competitors. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestion method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned proposal section is, When making a proposal, we analyze the past trends of competing companies and select the most suitable proposal method. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned proposal section is, When making a proposal, customize the proposal's approach based on the current market conditions of competing companies. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned proposal section is, When making a proposal, we will select the most suitable proposal method by considering the geographical location information of our competitors. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned proposal section is, When making a proposal, we analyze the social media activities of competing companies and propose methods for doing so. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned dashboard section is It estimates the user's emotions and adjusts how the dashboard is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned dashboard section is When displaying the dashboard, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned dashboard section is It estimates the user's emotions and adjusts the dashboard's operation procedures based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned dashboard section is When displaying the dashboard, the system selects the optimal display method considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned query section is, It estimates the user's sentiment and adjusts how queries are processed based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned query section is, During query processing, the system references the user's past query history to select the optimal processing method. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned query section is, It estimates the user's sentiment and prioritizes queries based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned query section is, During query processing, the system selects the optimal processing method by considering the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0211] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A tracking unit tracks the trends of competing companies based on the data analyzed by the aforementioned analysis unit, An alert unit provides an automatic alert based on the information obtained by the tracking unit, The system comprises a proposal unit that generates strategic proposals based on information provided by the alert unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as social media, news articles, corporate financial data, patent information, and satellite imagery. The system according to feature 1.

3. The aforementioned analysis unit, Multimodal AI enables integrated analysis of text, image, and numerical data. The system according to feature 1.

4. The aforementioned tracking unit is Track competitors' new product announcements and changes in market strategy in real time. The system according to feature 1.

5. The alert unit is, Provides automated alerts on the activities of competitors. The system according to feature 1.

6. The aforementioned proposal section is, Autonomously generates strategic proposals based on the actions of competing companies. The system according to feature 1.

7. Features a dashboard section that provides a customizable dashboard. The system according to feature 1.

8. It has a query section that accepts queries in natural language. The system according to feature 1.

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

10. The aforementioned collection unit is Evaluate the reliability of each data source and prioritize collecting the most reliable data. The system according to feature 1.

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