system

Abstract

The system according to this embodiment aims to analyze business data and market trends and propose the optimal strategy. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a provision unit, a feedback collection unit, and a learning unit. The collection unit collects business data and market trend data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a strategy based on the data analyzed by the analysis unit. The provision unit provides the strategy proposed by the proposal unit to the user. The feedback collection unit collects user feedback. The learning unit learns from the feedback collected by the feedback collection unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, business data and market trends have not been sufficiently analyzed effectively to propose an optimal strategy, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze business data and market trends and propose an optimal strategy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a provision unit, a feedback collection unit, and a learning unit. The data collection unit collects business data and market trend data. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes strategies based on the data analyzed by the analysis unit. The provision unit provides the strategies proposed by the proposal unit to the user. The feedback collection unit collects user feedback. The learning unit learns from the feedback collected by the feedback collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze business data and market trends and propose an optimal strategy. [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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable 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 platform according to an embodiment of the present invention is a system that analyzes business data and market trends and proposes optimal strategies for companies and individuals. This AI platform collects business data and market trend data, and the AI ​​analyzes it to propose optimal strategies for companies and individuals. Furthermore, the AI ​​learns and evolves through user feedback, providing more customized services. This AI platform features real-time data analysis, automatic learning capabilities, and evolution based on user behavior. For example, the AI ​​can analyze business data in real time, improving the speed of a company's market adaptation. Also, the AI ​​learns from user feedback and automatically improves strategic proposals, thereby improving customer satisfaction. This system supports companies in making strategic changes to quickly respond to market fluctuations and assists sole proprietors in their strategic decision-making. Furthermore, the AI's self-evolving ability enables continuous service improvement and automation of strategic proposals. Target users include individuals such as young entrepreneurs, freelancers, and small business owners, as well as companies ranging from small to large enterprises. This system is particularly useful for companies in industries with volatile market conditions. This AI platform leverages machine learning and natural language processing for data analysis and strategic proposals, holding a significant market share in the business intelligence and AI sectors. With the evolution of AI technology and increasing market demand, now is the opportune time to enter the market. Through this system, businesses can achieve rapid decision-making and strategic adaptation, aiming to enhance corporate growth and competitiveness. The AI ​​platform can propose optimal strategies for businesses and individuals, enabling rapid decision-making and strategic adaptation.

[0029] The AI ​​platform according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a provision unit, a feedback collection unit, and a learning unit. The data collection unit collects business data and market trend data. The data collection unit can collect business data such as sales data, customer data, and marketing data. The data collection unit can also collect market trend data such as industry reports, consumer behavior data, and competitive analysis data. The data collection unit can collect data from the internet using, for example, web scraping technology. The data collection unit can also obtain data from external databases using APIs. Furthermore, the data collection unit can collect real-time data using sensors and IoT devices. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis, machine learning algorithms, and data mining techniques. The analysis unit can predict data trends using, for example, regression analysis. The analysis unit can also group data using clustering technology. Furthermore, the analysis unit can analyze complex data patterns using deep learning technology. The proposal unit proposes strategies based on the data analyzed by the analysis unit. The proposal department can propose strategies such as marketing strategies, sales strategies, and product development strategies. For example, the proposal department can propose strategies aimed at improving ROI. It can also propose strategies aimed at improving customer satisfaction. Furthermore, it can propose strategies aimed at cost reduction. The delivery department provides the strategies proposed by the proposal department to users. The delivery department can provide strategies through web applications or mobile applications, for example. It can also provide strategies via email or notification functions. Furthermore, it can provide strategies in dashboard or report format. The feedback collection department collects user feedback. The feedback collection department can collect feedback through methods such as surveys, user reviews, and interviews.The feedback collection unit can collect user feedback, for example, using a web form. It can also collect comments and ratings from social media. Furthermore, it can collect feedback in real time using a chatbot. The learning unit learns from the feedback collected by the feedback collection unit. The learning unit can learn from the feedback using, for example, machine learning algorithms. The learning unit can also build a feedback loop and continuously learn. Additionally, the learning unit can analyze the feedback using data analysis techniques to improve the system. As a result, the AI ​​platform according to this embodiment can propose optimal strategies for companies and individuals, enabling rapid decision-making and strategic adaptation.

[0030] The data collection unit collects business data and market trend data. For example, it can collect business data such as sales data, customer data, and marketing data. Specifically, sales data is obtained from POS systems and online sales platforms, while customer data is collected from CRM systems and customer surveys. Marketing data includes the results of advertising campaigns and social media engagement data. The data collection unit can also collect market trend data such as industry reports, consumer behavior data, and competitive analysis data. This data is crucial for understanding industry trends and consumer preferences. For example, industry reports are obtained from specialized organizations, and consumer behavior data is collected from website access logs and purchase history. Competitive analysis data includes evaluations of competitors' products and services, pricing, and marketing strategies. The data collection unit can also collect data from the internet using web scraping techniques. Web scraping is a technique that automatically extracts data from specific websites, such as collecting the latest industry news and trend information from news sites and blogs. The data collection unit can also obtain data from external databases using APIs. By utilizing APIs, it is possible to obtain real-time data from sources such as financial data providers and market research companies. Furthermore, the data collection unit can also collect real-time data using sensors and IoT devices. For example, sensors within a store can be used to monitor customer movement and dwell time, and IoT devices can be used to grasp inventory status and equipment operating status in real time. As a result, the data collection unit can gather a wide range of data from diverse data sources and provide the information necessary for corporate decision-making.

[0031] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze data using, for example, statistical analysis, machine learning algorithms, and data mining techniques. Specifically, statistical analysis reveals data distribution and correlations, and identifies trends and patterns. For example, time-series analysis of sales data can be performed to evaluate seasonal fluctuations and promotional effects. Machine learning algorithms automatically learn from data to build predictive and classification models. For example, customer data can be used to predict customer purchasing behavior and improve the accuracy of targeted marketing. Data mining techniques are used to extract useful information from large amounts of data. For example, clustering techniques can be used to segment customers and formulate optimal marketing strategies for each segment. The analysis unit can predict data trends using, for example, regression analysis. Regression analysis is a method for modeling relationships between variables and predicting future values. For example, the relationship between sales data and advertising costs can be modeled to determine the optimal allocation of advertising costs. The analysis unit can also group data using clustering techniques. Clustering is a method of grouping data based on similarity. For example, customer data can be clustered to identify customer groups with similar purchasing behavior. Furthermore, the analysis unit can analyze complex data patterns using deep learning technology. Deep learning uses multi-layered neural networks to automatically extract data features and perform advanced analyses such as image recognition and natural language processing. This allows the analysis unit to analyze collected data from multiple perspectives and provide insights necessary for corporate strategy formulation.

[0032] The Proposal Department proposes strategies based on data analyzed by the Analysis Department. For example, the Proposal Department can propose strategies such as marketing strategies, sales strategies, and product development strategies. Specifically, as a marketing strategy, they propose optimal advertising campaigns and promotions for target customer segments. For example, by analyzing customer data and delivering personalized advertisements to specific customer segments, advertising effectiveness can be maximized. As a sales strategy, they prioritize leads and propose cross-sell / up-sell strategies to the sales team. For example, by analyzing customer purchase history and proposing products likely to be purchased next, sales can be increased. As a product development strategy, they propose the development of new products or improvements to existing products based on market trends and customer feedback. For example, by analyzing consumer behavior data and adding new features or improving the design to meet customer needs, product competitiveness can be enhanced. The Proposal Department can also propose strategies aimed at improving ROI. ROI (Return on Investment) is an indicator that shows the ratio of profit to investment. For example, by evaluating the effectiveness of advertising campaigns and concentrating the budget on the most effective advertising channels, ROI can be maximized. Furthermore, the Proposal Department can also propose strategies aimed at improving customer satisfaction. Customer satisfaction is an indicator of customer satisfaction and loyalty. For example, customer satisfaction can be improved by improving services and strengthening customer support based on customer feedback. Furthermore, the proposal department can also propose strategies aimed at cost reduction. Cost reduction is a crucial element in maximizing a company's profits. For example, costs can be reduced by proposing supply chain optimization and streamlining business processes. In this way, the proposal department can propose optimal strategies tailored to the company's goals and improve the company's competitiveness.

[0033] The service provider delivers the strategies proposed by the proposal provider to users. The service provider can deliver strategies through, for example, web or mobile applications. Specifically, it provides user-friendly interfaces and displays proposed strategies in a visually clear manner. For example, it can provide a dashboard format to allow users to check sales forecasts and the effectiveness of marketing campaigns in real time. The service provider can also deliver strategies using email and notification functions. For example, it can notify users of important strategic proposals and action items via email to enable quick responses. Furthermore, the service provider can deliver strategies in dashboard and report formats. Dashboards display multiple metrics and data in a unified manner, making it easier for users to grasp the overall situation. Reports document detailed analysis results and proposals for later reference. The service provider can provide customized information according to user needs. For example, it can provide high-level information to help management make strategic decisions and provide specific action plans and implementation procedures to field staff. The service provider can also continuously improve its offerings based on user feedback. For example, it can collect user feedback and make improvements to enhance the accuracy and usefulness of the information provided. This allows the service provider to offer valuable information to users and support corporate decision-making.

[0034] The feedback collection unit collects user feedback. This can be done through methods such as surveys, user reviews, and interviews. Specifically, surveys involve asking users specific questions and collecting their responses. For example, surveys could be conducted to gather user opinions on the usability of a new product or satisfaction with a service. User reviews provide a platform where users can freely post their opinions, collecting evaluations of products and services. Interviews allow for direct interaction with users, enabling detailed feedback. The feedback collection unit can also collect user feedback using web forms. Web forms provide an easy-to-use interface for users to input their opinions and automatically aggregate the collected data. Furthermore, the feedback collection unit can collect comments and ratings from social media. Social media is a valuable source of feedback because it is a platform used daily by users, and many opinions are posted in real time. Additionally, the feedback collection unit can collect feedback in real time using chatbots. Chatbots can collect opinions through interaction with users and respond quickly. For example, customer support chatbots can collect user problems and improvement requests. This allows the feedback collection unit to gather user opinions in a variety of ways and use them to improve the company's products and services.

[0035] The learning unit learns from the feedback collected by the feedback collection unit. The learning unit can learn from feedback using, for example, machine learning algorithms. Specifically, it analyzes feedback data and extracts patterns and trends. For example, it analyzes feedback on customer satisfaction and identifies factors for improving satisfaction. The learning unit can also build feedback loops for continuous learning. A feedback loop is a process where the system is improved based on collected feedback, and the results of these improvements are collected again as feedback, thereby improving the system's accuracy and performance. Furthermore, the learning unit can analyze feedback using data analysis techniques to improve the system. For example, it can use text mining techniques to analyze the content of feedback and extract common problems and requests for improvement. In addition, the learning unit can develop new algorithms and models based on the feedback. For example, it can build a more accurate purchase prediction model based on feedback on customer purchasing behavior. This allows the learning unit to effectively utilize feedback and achieve continuous system improvement. The learning unit can share the results of its feedback learning with other departments and reflect them in overall strategies and policies. For example, by collaborating with the marketing and product development departments, the learning department can support the development of new strategies and products based on feedback. This allows the learning department to contribute to the overall growth and improved competitiveness of the company.

[0036] The data collection unit can collect business data and market trend data. For example, the data collection unit collects business data such as sales data, customer data, and marketing data. For example, the data collection unit collects market trend data such as industry reports, consumer behavior data, and competitive analysis data. For example, the data collection unit can collect data from the internet using web scraping technology. For example, the data collection unit can obtain data from external databases using APIs. For example, the data collection unit can collect real-time data using sensors and IoT devices. This allows the data collection unit to obtain data to propose optimal strategies for companies and individuals. 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 business data and market trend data into a generating AI and have the generating AI perform data collection.

[0037] The analysis unit can analyze collected data and propose optimal strategies for companies and individuals. The analysis unit can analyze data using, for example, statistical analysis, machine learning algorithms, and data mining techniques. The analysis unit can predict data trends using, for example, regression analysis. The analysis unit can group data using, for example, clustering techniques. The analysis unit can analyze complex data patterns using, for example, deep learning techniques. This allows the analysis unit to analyze data in order to propose optimal strategies for companies and individuals. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform data analysis.

[0038] The proposal department can propose strategies based on the analyzed data. The proposal department can propose strategies such as marketing strategies, sales strategies, and product development strategies. The proposal department can propose strategies aimed at improving ROI. The proposal department can propose strategies aimed at improving customer satisfaction. The proposal department can propose strategies aimed at reducing costs. In this way, the proposal department can propose the most suitable strategies for companies and individuals. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analyzed data into a generating AI and have the generating AI execute strategy proposals.

[0039] The service provider can provide the proposed strategy to the user. The service provider can provide the strategy, for example, through a web application or a mobile application. The service provider can provide the strategy, for example, using email or a notification function. The service provider can provide the strategy, for example, in the form of a dashboard or report. This makes it easier for the user to implement the strategy. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the proposed strategy into a generating AI and have the generating AI perform the task of providing the strategy.

[0040] The feedback collection unit can collect user feedback. The feedback collection unit collects feedback through methods such as surveys, user reviews, and interviews. The feedback collection unit collects user feedback using web forms, for example. The feedback collection unit collects comments and ratings from social media, for example. The feedback collection unit collects feedback in real time using a chatbot, for example. This allows the feedback collection unit to use the information to improve the system. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input user feedback into a generating AI and have the generating AI perform the feedback collection.

[0041] The learning unit can learn from the collected feedback and evolve on its own. For example, the learning unit learns from the feedback using machine learning algorithms. For example, the learning unit builds a feedback loop and learns continuously. For example, the learning unit analyzes the feedback using data analysis techniques to improve the system. In this way, the learning unit can improve the accuracy of the system. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the collected feedback into a generative AI and have the generative AI perform learning.

[0042] The data collection unit can analyze past data collection history and select the optimal data collection method when collecting business data. For example, the data collection unit can identify the most effective data collection method from past data collection history and apply it. For example, the data collection unit can analyze past data collection history and optimize the timing of data collection. For example, the data collection unit can narrow down the target of data collection based on past data collection history and collect it efficiently. This enables the data collection unit to collect data efficiently. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal data collection method.

[0043] The data collection unit can filter market trend data by focusing on specific industries or regions. For example, the data collection unit can prioritize collecting data related to a particular industry. For example, the data collection unit can prioritize collecting data related to a particular region. For example, the data collection unit can set different filtering criteria for each industry or region and collect data. This allows the data collection unit to collect highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input market trend data into a generating AI and have the generating AI perform the filtering.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit prioritizes the collection of data related to the user's current location. For example, the data collection unit collects highly relevant data based on the user's past travel history. For example, the data collection unit identifies optimal data collection points based on the user's geographical location information. This enables the data collection unit to collect data efficiently. Some or all of the above-described processes 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 and have the generating AI perform the collection of highly relevant data.

[0045] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant data. For example, the data collection unit can collect data based on information about a user's followers and accounts they follow on social media. For example, the data collection unit can analyze trends in a user's social media activity and collect relevant data. This enables the data collection unit to collect data efficiently. Some or all of the above-described processes 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 and have the generating AI perform the collection of related data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This enables the analysis unit to perform efficient data analysis. 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 and have the generating AI adjust the level of detail of the analysis.

[0047] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific business analysis algorithm to business data. For example, the analysis unit can apply a specific market analysis algorithm to market trend data. For example, the analysis unit can select and apply the most suitable analysis algorithm for each data category. This enables the analysis unit to perform efficient data analysis. Some or all of the above-described processes 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 have the generating AI apply the analysis algorithm.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may give emphasis to the most recent data while referring to past data. For example, the analysis unit may dynamically adjust the priority of analysis according to the data collection timing. This enables the analysis unit to perform efficient data analysis. 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 and have the generating AI determine the priority of analysis.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This enables the analysis unit to perform efficient data analysis. Some or all of the above-described 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 and have the generating AI perform the adjustment of the order of analysis.

[0050] The proposal unit can adjust the level of detail in its proposals based on the importance of the strategies. For example, it can provide detailed proposals for high-importance strategies and simplified proposals for low-importance strategies. The proposal unit can also prioritize proposals according to the importance of the strategies. This enables the proposal unit to produce efficient proposals. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the strategies into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0051] The proposal unit can apply different proposal algorithms depending on the strategy category when making a proposal. For example, the proposal unit applies a specific business proposal algorithm to business strategies. For example, the proposal unit applies a specific market proposal algorithm to market strategies. The proposal unit selects and applies the optimal proposal algorithm for each strategy category. This enables the proposal unit to make efficient proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input strategy categories into a generating AI and have the generating AI apply a proposal algorithm.

[0052] The proposal department can determine the priority of proposals based on the strategy submission deadlines when submitting proposals. For example, the proposal department will prioritize strategies that are urgent. For example, the proposal department will postpone strategies that have ample time for submission. For example, the proposal department can dynamically adjust the priority of proposals according to the strategy submission deadlines. This enables the proposal department to submit proposals efficiently. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the strategy submission deadlines into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0053] The proposal unit can adjust the order of proposals based on the relevance of the strategies during the proposal process. For example, the proposal unit may prioritize proposing strategies that are highly relevant. For example, the proposal unit may postpone proposing strategies that are less relevant. For example, the proposal unit may dynamically adjust the order of proposals according to the relevance of the strategies. This enables the proposal unit to make efficient proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit may input the relevance of strategies into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0054] The service provider can select the optimal service delivery method by referring to the user's past feedback at the time of delivery. For example, the service provider selects the optimal service delivery method based on the user's past feedback. For example, the service provider analyzes the user's past feedback and improves the service delivery method. For example, the service provider customizes the service delivery method by referring to the user's past feedback. This enables the service provider to provide information efficiently. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI select the optimal service delivery method.

[0055] The delivery unit can customize the delivery method based on the user's current situation at the time of delivery. For example, if the user is on the move, the delivery unit will deliver in a manner optimized for mobile devices. For example, if the user is in the office, the delivery unit will deliver in a manner optimized for desktop devices. For example, if the user is in a meeting, the delivery unit will deliver via voice or text message. This enables the delivery unit to deliver information efficiently. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the delivery method.

[0056] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider may prioritize providing information related to the user's current location. For example, the service provider may select the optimal service delivery method based on the user's past travel history. For example, the service provider may customize the service delivery method based on the user's geographical location information. This enables the service provider to provide information efficiently. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0057] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can analyze the content of the user's social media posts and propose the most suitable means of delivery. For example, the service provider can propose a means of delivery based on information about the user's followers and accounts they follow on social media. For example, the service provider can analyze the trends in the user's social media activity and propose the most suitable means of delivery. This enables the service provider to provide information efficiently. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose a means of delivery.

[0058] The feedback collection unit can analyze past feedback history and select the optimal collection method when collecting feedback. For example, the feedback collection unit can identify the most effective feedback collection method from past feedback history and apply it. For example, the feedback collection unit can analyze past feedback history and optimize the timing of feedback collection. For example, the feedback collection unit can narrow down the target of feedback collection based on past feedback history and collect it efficiently. This enables the feedback collection unit to collect feedback efficiently. Some or all of the above processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input past feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0059] The feedback collection unit can prioritize collecting highly relevant feedback by considering the user's geographical location information during feedback collection. For example, the feedback collection unit prioritizes collecting feedback related to the user's current location. For example, the feedback collection unit collects highly relevant feedback based on the user's past travel history. For example, the feedback collection unit identifies the optimal feedback collection point based on the user's geographical location information. This enables the feedback collection unit to collect feedback efficiently. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant feedback.

[0060] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and improve the learning algorithm. For example, the learning unit can customize the learning algorithm by referring to past learning data. This enables the learning unit to learn efficiently. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0061] The learning unit can weight the training data based on the timing of feedback submissions during training. For example, the learning unit prioritizes learning the most recent feedback. For example, the learning unit gives more emphasis to the most recent feedback while referring to past feedback. For example, the learning unit dynamically adjusts the weighting of the training data according to the timing of feedback submissions. This enables the learning unit to learn efficiently. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the timing of feedback submissions into a generating AI and have the generating AI perform the weighting of the training data.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can analyze the user's past behavior history and select the optimal data collection method during data collection. For example, it can identify the most effective data collection method from past behavior history and apply it. It can also analyze past behavior history and optimize the timing of data collection. Furthermore, it can narrow down the target of data collection based on past behavior history and collect data efficiently. This enables the data collection unit to collect data efficiently. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0064] The analysis unit can determine the priority of analysis based on the reliability of the data during the analysis process. For example, it can prioritize the analysis of highly reliable data, and postpone the analysis of less reliable data. Furthermore, it can dynamically adjust the analysis priority according to the reliability of the data. This enables the analysis unit to perform efficient data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input the reliability of the data into a generating AI and have the generating AI determine the analysis priority.

[0065] The proposal unit can select the optimal proposal method by referring to the user's past preferences when making a proposal. For example, it can select the optimal proposal method based on the user's past preferences. It can also analyze the user's past preferences and improve the proposal method. Furthermore, it can customize the proposal method based on the user's past preferences. This enables the proposal unit to make efficient proposals. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the user's past preference data into a generating AI and have the generating AI select the optimal proposal method.

[0066] The service provider can select the optimal service delivery method by referring to the user's past behavior history at the time of delivery. For example, it can select the optimal service delivery method based on the user's past behavior history. It can also analyze the user's past behavior history and improve the service delivery method. Furthermore, it can customize the service delivery method based on the user's past behavior history. This enables the service provider to provide information efficiently. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past behavior history data into a generating AI and have the generating AI select the optimal service delivery method.

[0067] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, it can select the optimal collection method based on the user's past feedback history. It can also analyze the user's past feedback history and improve the collection method. Furthermore, it can customize the collection method based on the user's past feedback history. This enables the feedback collection unit to collect feedback efficiently. Some or all of the above-described processes in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects business data and market trend data. Specifically, it collects business data such as sales data, customer data, and marketing data, as well as market trend data such as industry reports, consumer behavior data, and competitive analysis data. Data collection methods include web scraping, acquisition from external databases using APIs, and real-time data collection using sensors and IoT devices. Step 2: The analysis unit analyzes the data collected by the collection unit. Analysis methods include statistical analysis, machine learning algorithms, and data mining techniques. Specifically, this involves predicting data trends using regression analysis, grouping data using clustering techniques, and analyzing complex data patterns using deep learning techniques. Step 3: The proposal department proposes strategies based on the data analyzed by the analysis department. The proposed strategies include marketing strategies, sales strategies, and product development strategies, and specifically aim to improve ROI, enhance customer satisfaction, and reduce costs. Step 4: The delivery team delivers the strategies proposed by the proposal team to the users. Delivery methods include web applications, mobile applications, email and notification functions, dashboards and reports. Step 5: The feedback collection unit collects user feedback. Collection methods include surveys, user reviews, interviews, web forms, social media comments and ratings, and real-time collection using chatbots. Step 6: The learning unit learns from the feedback collected by the feedback collection unit. The learning method includes learning feedback using machine learning algorithms, building feedback loops, and analyzing feedback using data analysis techniques, which helps to improve the system.

[0070] (Example of form 2) An AI platform according to an embodiment of the present invention is a system that analyzes business data and market trends and proposes optimal strategies for companies and individuals. This AI platform collects business data and market trend data, and the AI ​​analyzes it to propose optimal strategies for companies and individuals. Furthermore, the AI ​​learns and evolves through user feedback, providing more customized services. This AI platform features real-time data analysis, automatic learning capabilities, and evolution based on user behavior. For example, the AI ​​can analyze business data in real time, improving the speed of a company's market adaptation. Also, the AI ​​learns from user feedback and automatically improves strategic proposals, thereby improving customer satisfaction. This system supports companies in making strategic changes to quickly respond to market fluctuations and assists sole proprietors in their strategic decision-making. Furthermore, the AI's self-evolving ability enables continuous service improvement and automation of strategic proposals. Target users include individuals such as young entrepreneurs, freelancers, and small business owners, as well as companies ranging from small to large enterprises. This system is particularly useful for companies in industries with volatile market conditions. This AI platform leverages machine learning and natural language processing for data analysis and strategic proposals, holding a significant market share in the business intelligence and AI sectors. With the evolution of AI technology and increasing market demand, now is the opportune time to enter the market. Through this system, businesses can achieve rapid decision-making and strategic adaptation, aiming to enhance corporate growth and competitiveness. The AI ​​platform can propose optimal strategies for businesses and individuals, enabling rapid decision-making and strategic adaptation.

[0071] The AI ​​platform according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a provision unit, a feedback collection unit, and a learning unit. The data collection unit collects business data and market trend data. The data collection unit can collect business data such as sales data, customer data, and marketing data. The data collection unit can also collect market trend data such as industry reports, consumer behavior data, and competitive analysis data. The data collection unit can collect data from the internet using, for example, web scraping technology. The data collection unit can also obtain data from external databases using APIs. Furthermore, the data collection unit can collect real-time data using sensors and IoT devices. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can analyze the data using, for example, statistical analysis, machine learning algorithms, and data mining techniques. The analysis unit can predict data trends using, for example, regression analysis. The analysis unit can also group data using clustering technology. Furthermore, the analysis unit can analyze complex data patterns using deep learning technology. The proposal unit proposes strategies based on the data analyzed by the analysis unit. The proposal department can propose strategies such as marketing strategies, sales strategies, and product development strategies. For example, the proposal department can propose strategies aimed at improving ROI. It can also propose strategies aimed at improving customer satisfaction. Furthermore, it can propose strategies aimed at cost reduction. The delivery department provides the strategies proposed by the proposal department to users. The delivery department can provide strategies through web applications or mobile applications, for example. It can also provide strategies via email or notification functions. Furthermore, it can provide strategies in dashboard or report format. The feedback collection department collects user feedback. The feedback collection department can collect feedback through methods such as surveys, user reviews, and interviews.The feedback collection unit can collect user feedback, for example, using a web form. It can also collect comments and ratings from social media. Furthermore, it can collect feedback in real time using a chatbot. The learning unit learns from the feedback collected by the feedback collection unit. The learning unit can learn from the feedback using, for example, machine learning algorithms. The learning unit can also build a feedback loop and continuously learn. Additionally, the learning unit can analyze the feedback using data analysis techniques to improve the system. As a result, the AI ​​platform according to this embodiment can propose optimal strategies for companies and individuals, enabling rapid decision-making and strategic adaptation.

[0072] The data collection unit collects business data and market trend data. For example, it can collect business data such as sales data, customer data, and marketing data. Specifically, sales data is obtained from POS systems and online sales platforms, while customer data is collected from CRM systems and customer surveys. Marketing data includes the results of advertising campaigns and social media engagement data. The data collection unit can also collect market trend data such as industry reports, consumer behavior data, and competitive analysis data. This data is crucial for understanding industry trends and consumer preferences. For example, industry reports are obtained from specialized organizations, and consumer behavior data is collected from website access logs and purchase history. Competitive analysis data includes evaluations of competitors' products and services, pricing, and marketing strategies. The data collection unit can also collect data from the internet using web scraping techniques. Web scraping is a technique that automatically extracts data from specific websites, such as collecting the latest industry news and trend information from news sites and blogs. The data collection unit can also obtain data from external databases using APIs. By utilizing APIs, it is possible to obtain real-time data from sources such as financial data providers and market research companies. Furthermore, the data collection unit can also collect real-time data using sensors and IoT devices. For example, sensors within a store can be used to monitor customer movement and dwell time, and IoT devices can be used to grasp inventory status and equipment operating status in real time. As a result, the data collection unit can gather a wide range of data from diverse data sources and provide the information necessary for corporate decision-making.

[0073] The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze data using, for example, statistical analysis, machine learning algorithms, and data mining techniques. Specifically, statistical analysis reveals data distribution and correlations, and identifies trends and patterns. For example, time-series analysis of sales data can be performed to evaluate seasonal fluctuations and promotional effects. Machine learning algorithms automatically learn from data to build predictive and classification models. For example, customer data can be used to predict customer purchasing behavior and improve the accuracy of targeted marketing. Data mining techniques are used to extract useful information from large amounts of data. For example, clustering techniques can be used to segment customers and formulate optimal marketing strategies for each segment. The analysis unit can predict data trends using, for example, regression analysis. Regression analysis is a method for modeling relationships between variables and predicting future values. For example, the relationship between sales data and advertising costs can be modeled to determine the optimal allocation of advertising costs. The analysis unit can also group data using clustering techniques. Clustering is a method of grouping data based on similarity. For example, customer data can be clustered to identify customer groups with similar purchasing behavior. Furthermore, the analysis unit can analyze complex data patterns using deep learning technology. Deep learning uses multi-layered neural networks to automatically extract data features and perform advanced analyses such as image recognition and natural language processing. This allows the analysis unit to analyze collected data from multiple perspectives and provide insights necessary for corporate strategy formulation.

[0074] The Proposal Department proposes strategies based on data analyzed by the Analysis Department. For example, the Proposal Department can propose strategies such as marketing strategies, sales strategies, and product development strategies. Specifically, as a marketing strategy, they propose optimal advertising campaigns and promotions for target customer segments. For example, by analyzing customer data and delivering personalized advertisements to specific customer segments, advertising effectiveness can be maximized. As a sales strategy, they prioritize leads and propose cross-sell / up-sell strategies to the sales team. For example, by analyzing customer purchase history and proposing products likely to be purchased next, sales can be increased. As a product development strategy, they propose the development of new products or improvements to existing products based on market trends and customer feedback. For example, by analyzing consumer behavior data and adding new features or improving the design to meet customer needs, product competitiveness can be enhanced. The Proposal Department can also propose strategies aimed at improving ROI. ROI (Return on Investment) is an indicator that shows the ratio of profit to investment. For example, by evaluating the effectiveness of advertising campaigns and concentrating the budget on the most effective advertising channels, ROI can be maximized. Furthermore, the Proposal Department can also propose strategies aimed at improving customer satisfaction. Customer satisfaction is an indicator of customer satisfaction and loyalty. For example, customer satisfaction can be improved by improving services and strengthening customer support based on customer feedback. Furthermore, the proposal department can also propose strategies aimed at cost reduction. Cost reduction is a crucial element in maximizing a company's profits. For example, costs can be reduced by proposing supply chain optimization and streamlining business processes. In this way, the proposal department can propose optimal strategies tailored to the company's goals and improve the company's competitiveness.

[0075] The service provider delivers the strategies proposed by the proposal provider to users. The service provider can deliver strategies through, for example, web or mobile applications. Specifically, it provides user-friendly interfaces and displays proposed strategies in a visually clear manner. For example, it can provide a dashboard format to allow users to check sales forecasts and the effectiveness of marketing campaigns in real time. The service provider can also deliver strategies using email and notification functions. For example, it can notify users of important strategic proposals and action items via email to enable quick responses. Furthermore, the service provider can deliver strategies in dashboard and report formats. Dashboards display multiple metrics and data in a unified manner, making it easier for users to grasp the overall situation. Reports document detailed analysis results and proposals for later reference. The service provider can provide customized information according to user needs. For example, it can provide high-level information to help management make strategic decisions and provide specific action plans and implementation procedures to field staff. The service provider can also continuously improve its offerings based on user feedback. For example, it can collect user feedback and make improvements to enhance the accuracy and usefulness of the information provided. This allows the service provider to offer valuable information to users and support corporate decision-making.

[0076] The feedback collection unit collects user feedback. This can be done through methods such as surveys, user reviews, and interviews. Specifically, surveys involve asking users specific questions and collecting their responses. For example, surveys could be conducted to gather user opinions on the usability of a new product or satisfaction with a service. User reviews provide a platform where users can freely post their opinions, collecting evaluations of products and services. Interviews allow for direct interaction with users, enabling detailed feedback. The feedback collection unit can also collect user feedback using web forms. Web forms provide an easy-to-use interface for users to input their opinions and automatically aggregate the collected data. Furthermore, the feedback collection unit can collect comments and ratings from social media. Social media is a valuable source of feedback because it is a platform used daily by users, and many opinions are posted in real time. Additionally, the feedback collection unit can collect feedback in real time using chatbots. Chatbots can collect opinions through interaction with users and respond quickly. For example, customer support chatbots can collect user problems and improvement requests. This allows the feedback collection unit to gather user opinions in a variety of ways and use them to improve the company's products and services.

[0077] The learning unit learns from the feedback collected by the feedback collection unit. The learning unit can learn from feedback using, for example, machine learning algorithms. Specifically, it analyzes feedback data and extracts patterns and trends. For example, it analyzes feedback on customer satisfaction and identifies factors for improving satisfaction. The learning unit can also build feedback loops for continuous learning. A feedback loop is a process where the system is improved based on collected feedback, and the results of these improvements are collected again as feedback, thereby improving the system's accuracy and performance. Furthermore, the learning unit can analyze feedback using data analysis techniques to improve the system. For example, it can use text mining techniques to analyze the content of feedback and extract common problems and requests for improvement. In addition, the learning unit can develop new algorithms and models based on the feedback. For example, it can build a more accurate purchase prediction model based on feedback on customer purchasing behavior. This allows the learning unit to effectively utilize feedback and achieve continuous system improvement. The learning unit can share the results of its feedback learning with other departments and reflect them in overall strategies and policies. For example, by collaborating with the marketing and product development departments, the learning department can support the development of new strategies and products based on feedback. This allows the learning department to contribute to the overall growth and improved competitiveness of the company.

[0078] The data collection unit can collect business data and market trend data. For example, the data collection unit collects business data such as sales data, customer data, and marketing data. For example, the data collection unit collects market trend data such as industry reports, consumer behavior data, and competitive analysis data. For example, the data collection unit can collect data from the internet using web scraping technology. For example, the data collection unit can obtain data from external databases using APIs. For example, the data collection unit can collect real-time data using sensors and IoT devices. This allows the data collection unit to obtain data to propose optimal strategies for companies and individuals. 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 business data and market trend data into a generating AI and have the generating AI perform data collection.

[0079] The analysis unit can analyze collected data and propose optimal strategies for companies and individuals. The analysis unit can analyze data using, for example, statistical analysis, machine learning algorithms, and data mining techniques. The analysis unit can predict data trends using, for example, regression analysis. The analysis unit can group data using, for example, clustering techniques. The analysis unit can analyze complex data patterns using, for example, deep learning techniques. This allows the analysis unit to analyze data in order to propose optimal strategies for companies and individuals. Some or all of the above-described processes in the analysis unit may be performed using, for example, AI, or not. For example, the analysis unit can input collected data into a generating AI and have the generating AI perform data analysis.

[0080] The proposal department can propose strategies based on the analyzed data. The proposal department can propose strategies such as marketing strategies, sales strategies, and product development strategies. The proposal department can propose strategies aimed at improving ROI. The proposal department can propose strategies aimed at improving customer satisfaction. The proposal department can propose strategies aimed at reducing costs. In this way, the proposal department can propose the most suitable strategies for companies and individuals. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the analyzed data into a generating AI and have the generating AI execute strategy proposals.

[0081] The service provider can provide the proposed strategy to the user. The service provider can provide the strategy, for example, through a web application or a mobile application. The service provider can provide the strategy, for example, using email or a notification function. The service provider can provide the strategy, for example, in the form of a dashboard or report. This makes it easier for the user to implement the strategy. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the proposed strategy into a generating AI and have the generating AI perform the task of providing the strategy.

[0082] The feedback collection unit can collect user feedback. The feedback collection unit collects feedback through methods such as surveys, user reviews, and interviews. The feedback collection unit collects user feedback using web forms, for example. The feedback collection unit collects comments and ratings from social media, for example. The feedback collection unit collects feedback in real time using a chatbot, for example. This allows the feedback collection unit to use the information to improve the system. Some or all of the above-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input user feedback into a generating AI and have the generating AI perform the feedback collection.

[0083] The learning unit can learn from the collected feedback and evolve on its own. For example, the learning unit learns from the feedback using machine learning algorithms. For example, the learning unit builds a feedback loop and learns continuously. For example, the learning unit analyzes the feedback using data analysis techniques to improve the system. In this way, the learning unit can improve the accuracy of the system. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the collected feedback into a generative AI and have the generative AI perform learning.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to lessen the user's burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can collect data quickly to provide the necessary information promptly. This allows the data collection unit to reduce the user's burden and enable efficient data collection. 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 using AI. For example, the data collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can analyze past data collection history and select the optimal data collection method when collecting business data. For example, the data collection unit can identify the most effective data collection method from past data collection history and apply it. For example, the data collection unit can analyze past data collection history and optimize the timing of data collection. For example, the data collection unit can narrow down the target of data collection based on past data collection history and collect it efficiently. This enables the data collection unit to collect data efficiently. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal data collection method.

[0086] The data collection unit can filter market trend data by focusing on specific industries or regions. For example, the data collection unit can prioritize collecting data related to a particular industry. For example, the data collection unit can prioritize collecting data related to a particular region. For example, the data collection unit can set different filtering criteria for each industry or region and collect data. This allows the data collection unit to collect highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input market trend data into a generating AI and have the generating AI perform the filtering.

[0087] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit will prioritize collecting data that can be collected quickly. This enables the data collection unit to collect data efficiently. 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 data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, the data collection unit prioritizes the collection of data related to the user's current location. For example, the data collection unit collects highly relevant data based on the user's past travel history. For example, the data collection unit identifies optimal data collection points based on the user's geographical location information. This enables the data collection unit to collect data efficiently. Some or all of the above-described processes 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 and have the generating AI perform the collection of highly relevant data.

[0089] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of a user's social media posts and collect relevant data. For example, the data collection unit can collect data based on information about a user's followers and accounts they follow on social media. For example, the data collection unit can analyze trends in a user's social media activity and collect relevant data. This enables the data collection unit to collect data efficiently. Some or all of the above-described processes 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 and have the generating AI perform the collection of related data.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit provides a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is in a hurry, the analysis unit provides a concise analysis result that gets straight to the point. This allows the analysis unit to provide analysis 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 not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit performs a detailed analysis on data with high importance. For example, the analysis unit performs a simplified analysis on data with low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the data. This enables the analysis unit to perform efficient data analysis. 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 and have the generating AI adjust the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific business analysis algorithm to business data. For example, the analysis unit can apply a specific market analysis algorithm to market trend data. For example, the analysis unit can select and apply the most suitable analysis algorithm for each data category. This enables the analysis unit to perform efficient data analysis. Some or all of the above-described processes 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 have the generating AI apply the analysis algorithm.

[0093] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is stressed, 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 in a hurry, the analysis unit provides a concise analysis result that can be quickly understood. In this way, the analysis unit can provide analysis 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 not using AI. For example, the analysis unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may give emphasis to the most recent data while referring to past data. For example, the analysis unit may dynamically adjust the priority of analysis according to the data collection timing. This enables the analysis unit to perform efficient data analysis. 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 and have the generating AI determine the priority of analysis.

[0095] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. For example, the analysis unit may dynamically adjust the order of analysis according to the relevance of the data. This enables the analysis unit to perform efficient data analysis. Some or all of the above-described 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 and have the generating AI perform the adjustment of the order of analysis.

[0096] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is stressed, the suggestion unit will provide simple and easily understandable 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 and to-the-point suggestions. This allows the suggestion unit to provide suggestions that are easy for the user to understand. 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 processing described above 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 and have the generative AI perform emotion estimation.

[0097] The proposal unit can adjust the level of detail in its proposals based on the importance of the strategies. For example, it can provide detailed proposals for high-importance strategies and simplified proposals for low-importance strategies. The proposal unit can also prioritize proposals according to the importance of the strategies. This enables the proposal unit to produce efficient proposals. Some or all of the above processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the strategies into a generating AI and have the generating AI adjust the level of detail in the proposals.

[0098] The proposal unit can apply different proposal algorithms depending on the strategy category when making a proposal. For example, the proposal unit applies a specific business proposal algorithm to business strategies. For example, the proposal unit applies a specific market proposal algorithm to market strategies. The proposal unit selects and applies the optimal proposal algorithm for each strategy category. This enables the proposal unit to make efficient proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input strategy categories into a generating AI and have the generating AI apply a proposal algorithm.

[0099] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the estimated emotions. For example, if the user is stressed, the suggestion unit will provide a short, concise suggestion. If the user is relaxed, the suggestion unit will provide a detailed suggestion. If the user is in a hurry, the suggestion unit will provide a concise suggestion that can be quickly understood. This allows the suggestion unit to provide suggestions that are easy for the user to understand. 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 processing described above 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 and have the generative AI perform emotion estimation.

[0100] The proposal department can determine the priority of proposals based on the strategy submission deadlines when submitting proposals. For example, the proposal department will prioritize strategies that are urgent. For example, the proposal department will postpone strategies that have ample time for submission. For example, the proposal department can dynamically adjust the priority of proposals according to the strategy submission deadlines. This enables the proposal department to submit proposals efficiently. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the strategy submission deadlines into a generating AI and have the generating AI perform the task of determining the priority of proposals.

[0101] The proposal unit can adjust the order of proposals based on the relevance of the strategies during the proposal process. For example, the proposal unit may prioritize proposing strategies that are highly relevant. For example, the proposal unit may postpone proposing strategies that are less relevant. For example, the proposal unit may dynamically adjust the order of proposals according to the relevance of the strategies. This enables the proposal unit to make efficient proposals. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit may input the relevance of strategies into a generating AI and have the generating AI perform the adjustment of the order of proposals.

[0102] The service provider can estimate the user's emotions and adjust the delivery method based on the estimated emotions. For example, if the user is stressed, the service provider will deliver information in a simple and visually clear manner. If the user is relaxed, the service provider will deliver information in a way that includes detailed information. If the user is in a hurry, the service provider will deliver information in a concise manner that can be quickly understood. This enables the service provider to deliver information that is 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 service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The service provider can select the optimal service delivery method by referring to the user's past feedback at the time of delivery. For example, the service provider selects the optimal service delivery method based on the user's past feedback. For example, the service provider analyzes the user's past feedback and improves the service delivery method. For example, the service provider customizes the service delivery method by referring to the user's past feedback. This enables the service provider to provide information efficiently. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past feedback data into a generating AI and have the generating AI select the optimal service delivery method.

[0104] The delivery unit can customize the delivery method based on the user's current situation at the time of delivery. For example, if the user is on the move, the delivery unit will deliver in a manner optimized for mobile devices. For example, if the user is in the office, the delivery unit will deliver in a manner optimized for desktop devices. For example, if the user is in a meeting, the delivery unit will deliver via voice or text message. This enables the delivery unit to deliver information efficiently. Some or all of the above processing in the delivery unit may be performed using AI, for example, or not using AI. For example, the delivery unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the delivery method.

[0105] The service provider can estimate the user's emotions and determine the priority of its offerings based on those emotions. For example, if the user is stressed, the service provider will prioritize providing high-priority information. If the user is relaxed, the service provider will prioritize providing detailed information. If the user is in a hurry, the service provider will prioritize providing information that can be delivered quickly. This enables the service provider to deliver information efficiently. 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The service provider can select the optimal service delivery method at the time of delivery, taking into account the user's geographical location information. For example, the service provider may prioritize providing information related to the user's current location. For example, the service provider may select the optimal service delivery method based on the user's past travel history. For example, the service provider may customize the service delivery method based on the user's geographical location information. This enables the service provider to provide information efficiently. Some or all of the above-described processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider may input the user's geographical location information into a generating AI and have the generating AI select the optimal service delivery method.

[0107] The service provider can analyze the user's social media activity and propose a means of delivery at the time of delivery. For example, the service provider can analyze the content of the user's social media posts and propose the most suitable means of delivery. For example, the service provider can propose a means of delivery based on information about the user's followers and accounts they follow on social media. For example, the service provider can analyze the trends in the user's social media activity and propose the most suitable means of delivery. This enables the service provider to provide information efficiently. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI propose a means of delivery.

[0108] The feedback collection unit can estimate the user's emotions and adjust the timing of feedback collection based on the estimated emotions. For example, if the user is stressed, the feedback collection unit can reduce the frequency of feedback collection to alleviate the user's burden. For example, if the user is relaxed, the feedback collection unit can increase the frequency of feedback collection to collect more detailed feedback. For example, if the user is in a hurry, the feedback collection unit can collect feedback quickly and provide the necessary information promptly. This allows the feedback collection unit to reduce the user's burden and enable efficient feedback collection. 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-described processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's emotion data into a generative AI and have the generative AI perform emotion estimation.

[0109] The feedback collection unit can analyze past feedback history and select the optimal collection method when collecting feedback. For example, the feedback collection unit can identify the most effective feedback collection method from past feedback history and apply it. For example, the feedback collection unit can analyze past feedback history and optimize the timing of feedback collection. For example, the feedback collection unit can narrow down the target of feedback collection based on past feedback history and collect it efficiently. This enables the feedback collection unit to collect feedback efficiently. Some or all of the above processes in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input past feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0110] The feedback collection unit can estimate the user's emotions and determine the priority of feedback to collect based on the estimated emotions. For example, if the user is stressed, the feedback collection unit will prioritize collecting high-importance feedback. For example, if the user is relaxed, the feedback collection unit will prioritize collecting detailed feedback. For example, if the user is in a hurry, the feedback collection unit will prioritize collecting feedback that can be collected quickly. This enables the feedback collection unit to collect feedback efficiently. 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 feedback collection unit may be performed using AI, for example, or not using AI. For example, the feedback collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0111] The feedback collection unit can prioritize collecting highly relevant feedback by considering the user's geographical location information during feedback collection. For example, the feedback collection unit prioritizes collecting feedback related to the user's current location. For example, the feedback collection unit collects highly relevant feedback based on the user's past travel history. For example, the feedback collection unit identifies the optimal feedback collection point based on the user's geographical location information. This enables the feedback collection unit to collect feedback efficiently. Some or all of the above processing in the feedback collection unit may be performed using AI, for example, or without AI. For example, the feedback collection unit can input the user's geographical location information into a generating AI and have the generating AI collect highly relevant feedback.

[0112] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is stressed, the learning unit will prioritize learning data of high importance. For example, if the user is relaxed, the learning unit will prioritize learning detailed data. For example, if the user is in a hurry, the learning unit will prioritize learning data that can be learned quickly. This enables the learning unit to learn efficiently. 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 learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The learning unit can optimize its learning algorithm by referring to past learning data during the learning process. For example, the learning unit can select the optimal learning algorithm based on past learning data. For example, the learning unit can analyze past learning data and improve the learning algorithm. For example, the learning unit can customize the learning algorithm by referring to past learning data. This enables the learning unit to learn efficiently. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past learning data into a generating AI and have the generating AI perform the optimization of the learning algorithm.

[0114] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit will reduce the learning frequency to alleviate the user's burden. For example, if the user is relaxed, the learning unit will increase the learning frequency to learn more detailed data. For example, if the user is in a hurry, the learning unit will increase the learning frequency to quickly provide the necessary information. This enables the learning unit to learn efficiently. 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 learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0115] The learning unit can weight the training data based on the timing of feedback submissions during training. For example, the learning unit prioritizes learning the most recent feedback. For example, the learning unit gives more emphasis to the most recent feedback while referring to past feedback. For example, the learning unit dynamically adjusts the weighting of the training data according to the timing of feedback submissions. This enables the learning unit to learn efficiently. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the timing of feedback submissions into a generating AI and have the generating AI perform the weighting of the training data.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is stressed, it can prioritize the analysis of high-priority data. If the user is relaxed, it can prioritize the analysis of detailed data. Furthermore, if the user is in a hurry, it can prioritize the analysis of data that can be quickly analyzed. This enables the analysis unit to perform efficient data analysis in accordance with the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0118] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on those emotions. For example, if the user is stressed, the frequency of suggestions can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the frequency of suggestions can be increased, and more detailed suggestions can be provided. Furthermore, if the user is in a hurry, suggestions can be made quickly, providing the necessary information promptly. This enables the suggestion unit to make efficient suggestions tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the processing described above 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 and have the generative AI perform emotion estimation.

[0119] The information delivery unit can estimate the user's emotions and adjust the timing of deliveries based on the estimated emotions. For example, if the user is stressed, the frequency of deliveries can be reduced to lessen the user's burden. If the user is relaxed, the frequency of deliveries can be increased to provide more detailed information. Furthermore, if the user is in a hurry, deliveries can be made quickly to provide the necessary information promptly. This enables the information delivery unit to provide information efficiently in accordance with the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0120] The feedback collection unit can estimate the user's emotions and adjust the feedback collection method based on the estimated emotions. For example, if the user is stressed, feedback can be collected in the form of simple questions. If the user is relaxed, feedback can be collected in the form of detailed questions. Furthermore, if the user is in a hurry, feedback can be collected in a format that can be answered quickly. This enables the feedback collection unit to efficiently collect feedback according to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0121] The learning unit can estimate the user's emotions and adjust the timing of learning based on the estimated emotions. For example, if the user is stressed, the learning frequency can be reduced to lessen the user's burden. Conversely, if the user is relaxed, the learning frequency can be increased to learn more detailed data. Furthermore, if the user is in a hurry, learning can be performed quickly to provide the necessary information promptly. This enables the learning unit to learn efficiently in accordance with the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0122] The data collection unit can analyze the user's past behavior history and select the optimal data collection method during data collection. For example, it can identify the most effective data collection method from past behavior history and apply it. It can also analyze past behavior history and optimize the timing of data collection. Furthermore, it can narrow down the target of data collection based on past behavior history and collect data efficiently. This enables the data collection unit to collect data efficiently. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past behavior history data into a generating AI and have the generating AI select the optimal data collection method.

[0123] The analysis unit can determine the priority of analysis based on the reliability of the data during the analysis process. For example, it can prioritize the analysis of highly reliable data, and postpone the analysis of less reliable data. Furthermore, it can dynamically adjust the analysis priority according to the reliability of the data. This enables the analysis unit to perform efficient data analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input the reliability of the data into a generating AI and have the generating AI determine the analysis priority.

[0124] The proposal unit can select the optimal proposal method by referring to the user's past preferences when making a proposal. For example, it can select the optimal proposal method based on the user's past preferences. It can also analyze the user's past preferences and improve the proposal method. Furthermore, it can customize the proposal method based on the user's past preferences. This enables the proposal unit to make efficient proposals. Some or all of the above processes in the proposal unit may be performed using AI or not. For example, the proposal unit can input the user's past preference data into a generating AI and have the generating AI select the optimal proposal method.

[0125] The service provider can select the optimal service delivery method by referring to the user's past behavior history at the time of delivery. For example, it can select the optimal service delivery method based on the user's past behavior history. It can also analyze the user's past behavior history and improve the service delivery method. Furthermore, it can customize the service delivery method based on the user's past behavior history. This enables the service provider to provide information efficiently. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input the user's past behavior history data into a generating AI and have the generating AI select the optimal service delivery method.

[0126] The feedback collection unit can select the optimal collection method by referring to the user's past feedback history when collecting feedback. For example, it can select the optimal collection method based on the user's past feedback history. It can also analyze the user's past feedback history and improve the collection method. Furthermore, it can customize the collection method based on the user's past feedback history. This enables the feedback collection unit to collect feedback efficiently. Some or all of the above-described processes in the feedback collection unit may be performed using AI or not. For example, the feedback collection unit can input the user's past feedback history data into a generating AI and have the generating AI select the optimal collection method.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects business data and market trend data. Specifically, it collects business data such as sales data, customer data, and marketing data, as well as market trend data such as industry reports, consumer behavior data, and competitive analysis data. Data collection methods include web scraping, acquisition from external databases using APIs, and real-time data collection using sensors and IoT devices. Step 2: The analysis unit analyzes the data collected by the collection unit. Analysis methods include statistical analysis, machine learning algorithms, and data mining techniques. Specifically, this involves predicting data trends using regression analysis, grouping data using clustering techniques, and analyzing complex data patterns using deep learning techniques. Step 3: The proposal department proposes strategies based on the data analyzed by the analysis department. The proposed strategies include marketing strategies, sales strategies, and product development strategies, and specifically aim to improve ROI, enhance customer satisfaction, and reduce costs. Step 4: The delivery team delivers the strategies proposed by the proposal team to the users. Delivery methods include web applications, mobile applications, email and notification functions, dashboards and reports. Step 5: The feedback collection unit collects user feedback. Collection methods include surveys, user reviews, interviews, web forms, social media comments and ratings, and real-time collection using chatbots. Step 6: The learning unit learns from the feedback collected by the feedback collection unit. The learning method includes learning feedback using machine learning algorithms, building feedback loops, and analyzing feedback using data analysis techniques, which helps to improve the system.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, feedback collection unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit can collect business data and market trend data using the camera 42 and sensors of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a strategy based on the analysis results. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the proposed strategy to the user. The feedback collection unit collects user feedback using the microphone 38B and touch panel 38A of the smart device 14. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from the collected feedback. 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.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, feedback collection unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit can collect business data and market trend data using the camera 42 and sensors of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a strategy based on the analysis results. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the proposed strategy to the user. The feedback collection unit collects user feedback using the microphone 238 of the smart glasses 214. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from the collected feedback. 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.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

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

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

[0159] In 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.

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

[0161] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0163] The data processing system 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.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, provision unit, feedback collection unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit can collect business data and market trend data using the camera 42 and sensors of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a strategy based on the analysis results. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the proposed strategy to the user. The feedback collection unit collects user feedback using the microphone 238 of the headset terminal 314. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from the collected feedback. 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.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, provision unit, feedback collection unit, and learning unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit can collect business data and market trend data using the camera 42 and sensors of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes a strategy based on the analysis results. The provision unit is implemented by the control unit 46A of the robot 414 and provides the proposed strategy to the user. The feedback collection unit collects user feedback using the microphone 238 of the robot 414. The learning unit is implemented by the specific processing unit 290 of the data processing unit 12 and learns from the collected feedback. 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The data collection department collects business data and market trend data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a strategy based on the data analyzed by the aforementioned analysis unit, A provisioning unit that provides the user with the strategy proposed by the aforementioned proposal unit, A feedback collection unit that collects user feedback, The system includes a learning unit that learns the feedback collected by the feedback collection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect business data and market trend data. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, We analyze the collected data and propose the optimal strategy for businesses and individuals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose strategies based on the analyzed data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Provide the proposed strategy to the user. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback collection unit is Collect user feedback The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned learning unit, It learns from collected feedback and evolves on its own. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned collection unit is When collecting business data, analyze past collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting market trend data, filter it to focus on specific industries or regions. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) 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 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the strategy. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the strategy category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on when the strategy will be submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the strategy. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates the user's emotions and adjusts the delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing the service, we will refer to past user feedback to select the most suitable delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing the service, the delivery method will be customized based on the user's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of offerings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and propose a delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned feedback collection unit is It estimates the user's emotions and adjusts the timing of feedback collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned feedback collection unit is When collecting feedback, analyze past feedback history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned feedback collection unit is It estimates the user's emotions and determines the priority of feedback to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned feedback collection unit is When collecting feedback, the system prioritizes collecting highly relevant feedback by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned learning unit, During training, the training data is weighted based on when feedback was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0201] 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. The data collection department collects business data and market trend data, An analysis unit analyzes the data collected by the aforementioned collection unit, A proposal unit proposes a strategy based on the data analyzed by the aforementioned analysis unit, A provisioning unit that provides the user with the strategy proposed by the aforementioned proposal unit, A feedback collection unit that collects user feedback, The system includes a learning unit that learns the feedback collected by the feedback collection unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect business data and market trend data. The system according to feature 1.

3. The aforementioned analysis unit, We analyze the collected data and propose the optimal strategy for businesses and individuals. The system according to feature 1.

4. The aforementioned proposal section is, We propose strategies based on the analyzed data. The system according to feature 1.

5. The aforementioned supply unit is, Provide the proposed strategy to the user. The system according to feature 1.

6. The aforementioned feedback collection unit is Collect user feedback The system according to feature 1.

7. The aforementioned learning unit, It learns from collected feedback and evolves on its own. The system according to feature 1.

8. 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.

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