system

Abstract

The system according to this embodiment aims to propose optimal strategies and measures to respond to highly immediate market fluctuations. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The simulation unit performs market simulations based on the analysis results obtained by the analysis unit. The proposal unit proposes optimal strategies and measures based on the simulation results obtained by the simulation unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including the 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, strategies and measures for coping with highly immediate changes in the market have not been sufficiently proposed, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal strategy and measures for coping with highly immediate changes in the market.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The simulation unit performs market simulations based on the analysis results obtained by the analysis unit. The proposal unit proposes optimal strategies and measures based on the simulation results obtained by the simulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose optimal strategies and measures to respond to highly immediate market fluctuations. [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 multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The market simulation system according to an embodiment of the present invention is a system that, in addition to market simulation using AI, incorporates highly timely data (such as SNS trends and news) to predict how strategies and measures will respond to market fluctuations requiring immediate action. First, the market simulation system collects highly timely data such as SNS trends and news. Next, the AI ​​analyzes the collected data to predict market trends and fluctuations. Furthermore, the AI ​​performs a market simulation to simulate how strategies and measures will respond to market fluctuations. Finally, based on the simulation results, it proposes optimal strategies and measures. This system enables companies to quickly respond to market fluctuations by utilizing highly timely data and implement effective strategies and measures. For example, the market simulation system collects topics that are rapidly spreading on SNS and the latest news articles. This data is collected in real time by AI. Next, the AI ​​analyzes the collected data in the market simulation system. Based on the collected data, the AI ​​analyzes market trends and fluctuations and predicts what kind of impact they will have. For example, if a positive trend regarding a particular product occurs on SNS, it is predicted that demand for that product may increase. Furthermore, the AI ​​performs a market simulation in the market simulation system. Market simulations use collected data and analysis results to simulate how strategies and measures will respond to market fluctuations. For example, if a new marketing campaign is implemented, the simulation will show how that campaign will impact the market. Finally, the market simulation system proposes optimal strategies and measures based on the simulation results. AI analyzes the simulation results and proposes the most effective strategies and measures. For example, if an increase in demand for a particular product is predicted, it will suggest strengthening promotion for that product. This system allows companies to leverage highly timely data to respond quickly to market fluctuations and implement effective strategies and measures.This allows market simulation systems to leverage highly timely data, enabling companies to respond quickly to market fluctuations and implement effective strategies and measures.

[0029] The market simulation system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit collects trends and news from social media. The data collection unit can collect topics that are rapidly spreading on social media and the latest news articles. The data collection unit can also collect data in real time using AI. The data collection unit can filter data based on specific keywords or hashtags, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes market trends and fluctuations based on the collected data. For example, the analysis unit can analyze market trends and fluctuations using time series analysis or trend analysis. The analysis unit can also analyze data using AI and predict what kind of impact it will have. The simulation unit performs market simulations based on the analysis results obtained by the analysis unit. For example, the simulation unit can simulate a new marketing campaign. Based on the collected data and analysis results, the simulation unit simulates how strategies and measures will respond to market fluctuations. The simulation unit can also perform market simulations using AI. The proposal department proposes optimal strategies and measures based on the simulation results obtained by the simulation department. For example, the proposal department may propose strengthening promotions. The proposal department analyzes the simulation results and proposes the most effective strategies and measures. The proposal department can also propose optimal strategies and measures using AI. As a result, the market simulation system according to this embodiment can respond quickly to highly immediate market fluctuations by consistently performing everything from data collection and analysis to simulation and proposal.

[0030] The data collection unit collects data. For example, it collects trends and news from social media. Specifically, it can collect topics that are rapidly spreading on social media and the latest news articles. The data collection unit can also collect data in real time using AI. For example, it can filter data based on specific keywords or hashtags. The data collection unit uses social media APIs to automatically collect posts related to specific keywords or hashtags. This allows the data collection unit to quickly grasp the latest trends and topics. The data collection unit also monitors RSS feeds of news sites and retrieves data whenever new articles are published. Furthermore, the data collection unit can use web scraping technology to extract necessary information from specific websites. This allows the data collection unit to collect a wide range of data from diverse sources and understand market trends in real time. The data collection unit centrally manages the collected data and makes it accessible to the analysis and simulation units. For example, the collected data is stored on a cloud server and can be linked with other systems and departments as needed. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. Based on the collected data, the analysis unit analyzes market trends and fluctuations. Specifically, the analysis unit can analyze market trends and fluctuations using time series analysis and trend analysis. For example, it can use time series analysis to extract market fluctuation patterns from past data and predict future trends. The analysis unit can also use AI to analyze data and predict what kind of impact it will have. Using machine learning algorithms, the AI ​​can find patterns and correlations from large amounts of data and predict future market trends. For example, it can use natural language processing technology to analyze the content of social media posts and extract consumer sentiment and opinions. This allows the analysis unit to grasp changes in consumer interest and opinions in real time and predict market trends. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The Simulation Department conducts market simulations based on the analysis results obtained by the Analysis Department. Specifically, the Simulation Department can simulate new marketing campaigns. For example, based on collected data and analysis results, the Simulation Department simulates how strategies and measures will respond to market fluctuations. The Simulation Department can also perform market simulations using AI. The AI ​​builds simulation models and predicts the most likely outcome by simulating multiple scenarios. For example, it can simulate fluctuations in sales when a new promotional campaign is implemented, or the market's reaction to the actions of competitors. This allows the Simulation Department to evaluate the effectiveness of strategies and measures in advance and formulate optimal countermeasures. Furthermore, the Simulation Department can continuously revise simulation results based on real-time updated data to respond to the latest situations. For example, if market trends or consumer reactions change rapidly, the Simulation Department immediately incorporates new data and updates the simulation results. This allows the Simulation Department to always provide highly accurate simulations based on the latest information, supporting quick and appropriate responses.

[0033] The Proposal Department proposes optimal strategies and measures based on the simulation results obtained by the Simulation Department. Specifically, the Proposal Department can propose strengthening promotions. For example, it can analyze simulation results and propose the most effective strategies and measures. The Proposal Department can also propose optimal strategies and measures using AI. Based on the simulation results, the AI ​​evaluates multiple strategies and measures and selects the most effective one. For example, it can evaluate the effectiveness of a promotion campaign for a specific target group and propose the most suitable advertising media and message. This allows the Proposal Department to support companies in responding quickly to market fluctuations and implementing effective strategies. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can monitor the results after the implementation of proposed strategies and measures and revise the proposals based on the feedback. In addition, the Proposal Department can reliably transmit information using multiple communication methods. For example, it can quickly communicate proposals to company representatives via email or dashboards. This allows the Proposal Department to quickly and reliably propose optimal strategies and measures to companies and support them in responding to market fluctuations.

[0034] The data collection unit can collect social media trends and news. For example, it can collect topics that are rapidly spreading on social media and the latest news articles. The data collection unit can also collect data in real time using AI. For example, the data collection unit can filter data based on specific keywords or hashtags. This allows for the use of highly timely data by collecting social media trends and news. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input specific keywords or hashtags into AI to collect social media trends and news and collect relevant data.

[0035] The analysis unit can analyze market trends and fluctuations based on collected data. For example, the analysis unit can analyze market trends and fluctuations using time series analysis and trend analysis. The analysis unit can also use AI to analyze data and predict what kind of impact it will have. For example, if a positive trend occurs regarding a particular product on social media, the analysis unit may predict that demand for that product may increase. In this way, data-driven predictions become possible by analyzing market trends and fluctuations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into AI to analyze market trends and fluctuations.

[0036] The simulation unit can simulate new marketing campaigns. Based on collected data and analysis results, the simulation unit simulates how strategies and measures will respond to market fluctuations. The simulation unit can also perform market simulations using AI. For example, if a new marketing campaign is implemented, the simulation unit can simulate what impact that campaign will have on the market. This allows for the simulation of the effectiveness of a new marketing campaign in advance. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data for a new marketing campaign into AI and simulate its effects.

[0037] The proposal department can propose strengthening promotions. The proposal department analyzes simulation results and proposes the most effective strategies and measures. The proposal department can also use AI to propose optimal strategies and measures. For example, if the proposal department predicts an increase in demand for a particular product, it will propose strengthening the promotion of that product. By proposing strengthened promotions, effective measures can be implemented. 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 simulation results into AI and propose optimal strategies and measures.

[0038] The data collection unit can filter data based on specific keywords or hashtags. For example, the data collection unit can collect data using keywords related to a specific product name or brand name. For example, the data collection unit can collect data using hashtags related to a specific event or campaign. For example, the data collection unit can collect data using keywords related to a specific market segment. This allows for the collection of highly relevant data by filtering data based on specific keywords or hashtags. 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 specific keywords or hashtags into AI and collect relevant data.

[0039] The data collection unit can evaluate the reliability of the data and collect only reliable data. For example, the data collection unit can evaluate the source of the data and prioritize collecting data from reliable sources. For example, the data collection unit can evaluate the content of the data and collect data containing reliable information. For example, the data collection unit can evaluate the timestamp of the data and prioritize collecting the most recent data. This improves the accuracy of the analysis results by collecting only reliable data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the reliability of the data and collect only reliable data.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is in a specific country, the data collection unit can prioritize the collection of data related to that country. For example, if the user is in a specific city, the data collection unit can prioritize the collection of data related to that city. In this way, by considering the user's geographical location, data related to the region can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and prioritize the collection of highly relevant data.

[0041] The data collection unit can analyze a user's social media activity and collect relevant data. For example, the data collection unit can analyze posts from accounts that a user follows and collect relevant data. For example, the data collection unit can analyze posts from groups and communities that a user participates in and collect relevant data. For example, the data collection unit can analyze posts that a user has shared and collect relevant data. This allows for the collection of highly relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI and collect relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with a moderate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 have AI evaluate the importance of the data and adjust the level of detail of the analysis accordingly.

[0043] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a text analysis algorithm to SNS data. For example, the analysis unit can apply a topic modeling algorithm to news data. For example, the analysis unit can apply a time series analysis algorithm to market data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI classify the data categories and apply an appropriate analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of past data. For example, the analysis unit may prioritize the analysis of data with a recent submission date. In this way, by determining the priority of analysis based on the data submission date, the most recent data can be analyzed first. 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 may have AI evaluate the data submission date and determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may postpone the analysis of data with low relevance. For example, the analysis unit may analyze data with moderate relevance with moderate priority. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may have AI evaluate the relevance of the data and adjust the order of analysis.

[0046] The simulation unit can improve the accuracy of the simulation by considering the interrelationships between data. For example, the simulation unit can perform a simulation by considering the interrelationships between SNS data and news data. For example, the simulation unit can perform a simulation by considering the interrelationships between market data and trend data. For example, the simulation unit can perform a simulation by considering the interrelationships between user behavior data and market data. This improves the accuracy of the simulation by considering the interrelationships between data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI evaluate the interrelationships between data to improve the accuracy of the simulation.

[0047] The simulation unit can perform simulations while considering the attribute information of the data submitter. For example, the simulation unit can perform simulations while considering the age group of the submitter. For example, the simulation unit can perform simulations while considering the region information of the submitter. For example, the simulation unit can perform simulations while considering the occupation information of the submitter. This makes it possible to perform simulations with higher accuracy by considering the attribute information of the data submitter. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the submitter's attribute information into AI and perform simulations.

[0048] The simulation unit can perform simulations while considering the geographical distribution of the data. For example, the simulation unit can perform simulations while emphasizing data related to a specific region. For example, the simulation unit can perform simulations while emphasizing data related to a specific country. For example, the simulation unit can perform simulations while emphasizing data related to a specific city. This makes it possible to perform region-specific simulations by considering the geographical distribution of the data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI evaluate the geographical distribution of the data and then perform a simulation.

[0049] The simulation unit can improve the accuracy of the simulation by referring to relevant literature on the data. For example, the simulation unit can perform simulations by referring to relevant academic papers. For example, the simulation unit can perform simulations by referring to relevant industry reports. For example, the simulation unit can perform simulations by referring to relevant market research data. This improves the accuracy of the simulation by referring to relevant literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI refer to relevant literature to improve the accuracy of the simulation.

[0050] The proposal department can adjust the level of detail in its proposals based on the importance of the strategies and measures. For example, the proposal department can provide detailed proposals for highly important strategies. For example, it can provide simplified proposals for less important strategies. For example, it can provide proposals with a moderate level of detail for strategies of moderate importance. This allows for efficient proposals by adjusting the level of detail in proposals based on the importance of the strategies and measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the importance of strategies and measures and adjust the level of detail in the proposals accordingly.

[0051] The proposal department can apply different proposal algorithms depending on the category of strategy or measure. For example, the proposal department can apply a marketing algorithm to a marketing strategy. For example, the proposal department can apply a sales algorithm to a sales strategy. For example, the proposal department can apply a product development algorithm to a product development strategy. By applying the appropriate proposal algorithm according to the category of strategy or measure, the accuracy of the proposals is improved. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can have AI classify the categories of strategies and measures and apply the appropriate proposal algorithm.

[0052] The proposal department can determine the priority of proposals based on the submission timing of strategies and measures. For example, the proposal department can prioritize proposals for strategies with an upcoming submission deadline. For example, the proposal department can postpone proposals for strategies with a distant submission deadline. For example, the proposal department can give a moderate priority to proposals for strategies with a medium submission deadline. This allows for timely proposals by determining the priority of proposals based on the submission timing of strategies and measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the submission timing of strategies and measures and then determine the priority of proposals.

[0053] The proposal department can adjust the order of proposals based on the relevance of strategies and measures. For example, the proposal department can prioritize proposals for strategies with high relevance. For example, it can postpone proposals for strategies with low relevance. For example, it can give medium priority to proposals for strategies with moderate relevance. In this way, by adjusting the order of proposals based on the relevance of strategies and measures, more relevant proposals can be prioritized. 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 have AI evaluate the relevance of strategies and measures and adjust the order of proposals.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The market simulation system can further consider the user's purchase history when performing simulations. For example, it can collect data on products and services that the user has purchased in the past and use this to predict market trends and fluctuations. If a user frequently purchases products from a particular brand, the system can prioritize the analysis of trends related to that brand. Similarly, if a user frequently purchases products from a specific category, the system can prioritize data related to that category in its simulations. This allows for more personalized simulations by considering the user's purchase history. Purchase history data can be collected from sources such as the user's online shopping history and loyalty card usage history. The data collection unit can analyze the user's purchasing trends based on this data and provide it to the simulation unit. The simulation unit can then predict market trends and fluctuations based on the provided purchase history data and propose optimal strategies and measures.

[0056] The market simulation system can also perform simulations while taking into account the user's geographical location. For example, if the user is in a specific region, the system will prioritize collecting data related to that region and perform simulations accordingly. If the user is in a specific country, the system can prioritize market trends related to that country in its simulations. Similarly, if the user is in a specific city, the system can prioritize data related to that city in its simulations. This allows for region-specific simulations by considering the user's geographical location. Geographical location information can be obtained, for example, from the user's smartphone's GPS data or IP address. The data collection unit can analyze the user's location information based on this data and provide it to the simulation unit. The simulation unit can then predict market trends and fluctuations based on the provided location data and propose optimal strategies and measures.

[0057] The market simulation system can further analyze users' social media activity and collect relevant data. For example, it can analyze posts from accounts that users follow and collect relevant data. It can also analyze posts from groups and communities that users participate in and collect relevant data. Furthermore, it can analyze posts that users have shared and collect relevant data. In this way, by analyzing users' social media activity, highly relevant data can be collected. Based on this data, the collection unit can analyze users' social media activity and provide it to the simulation unit. Based on the provided social media data, the simulation unit can predict market trends and fluctuations and propose optimal strategies and measures.

[0058] The market simulation system can further consider the user's purchase history when making suggestions. For example, it can suggest new products and services related to the user's past purchases based on data from those products and services. If a user frequently purchases products from a particular brand, the system can suggest new products from that brand. Similarly, if a user frequently purchases products from a specific category, the system can suggest new products related to that category. This allows for more personalized suggestions by considering the user's purchase history. Purchase history data is collected from sources such as the user's online shopping history and loyalty card usage history. The data collection unit can analyze the user's purchasing trends based on this data and provide it to the suggestion unit. The suggestion unit can then make optimal suggestions based on the provided purchase history data and provide them to the user.

[0059] The market simulation system can also make suggestions while considering the user's geographical location. For example, if the user is in a specific region, it can suggest products and services related to that region. If the user is in a specific country, it can make suggestions based on market trends related to that country. Also, if the user is in a specific city, it can suggest products and services related to that city. This makes it possible to make region-specific suggestions by considering the user's geographical location. Geographical location information is obtained, for example, from the user's smartphone's GPS data or IP address. The collection unit can analyze the user's location information based on this data and provide it to the suggestion unit. The suggestion unit can make optimal suggestions based on the provided location data and provide them to the user.

[0060] Market simulation systems can further improve the accuracy of simulations by considering users' purchase history. For example, they can predict relevant market trends based on data of products and services that users have purchased in the past. If a user frequently purchases products from a particular brand, the simulation can focus on trends related to that brand. Similarly, if a user frequently purchases products from a particular category, the simulation can focus on data related to that category. This allows for more accurate simulations by considering users' purchase history. Purchase history data is collected from sources such as users' online shopping history and loyalty card usage history. The data collection unit can analyze user purchasing trends based on this data and provide it to the simulation unit. The simulation unit can then predict market trends and fluctuations based on the provided purchase history data and propose optimal strategies and measures.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects data. For example, the data collection unit collects social media trends and news. The data collection unit can collect topics that are rapidly spreading on social media and the latest news articles. The data collection unit can also collect data in real time using AI. The data collection unit can filter data based on specific keywords or hashtags, for example. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes market trends and fluctuations based on the collected data. For example, the analysis unit can analyze market trends and fluctuations using time series analysis or trend analysis. The analysis unit can also use AI to analyze the data and predict what kind of impact it will have. Step 3: The simulation unit performs market simulations based on the analysis results obtained by the analysis unit. For example, the simulation unit can simulate a new marketing campaign. Based on the collected data and analysis results, the simulation unit simulates how strategies and measures will respond to market fluctuations. The simulation unit can also perform market simulations using AI. Step 4: The proposal department proposes the optimal strategies and measures based on the simulation results obtained by the simulation department. For example, the proposal department might propose strengthening promotions. The proposal department analyzes the simulation results and proposes the most effective strategies and measures. The proposal department can also use AI to propose the optimal strategies and measures.

[0063] (Example of form 2) The market simulation system according to an embodiment of the present invention is a system that, in addition to market simulation using AI, incorporates highly timely data (such as SNS trends and news) to predict how strategies and measures will respond to market fluctuations requiring immediate action. First, the market simulation system collects highly timely data such as SNS trends and news. Next, the AI ​​analyzes the collected data to predict market trends and fluctuations. Furthermore, the AI ​​performs a market simulation to simulate how strategies and measures will respond to market fluctuations. Finally, based on the simulation results, it proposes optimal strategies and measures. This system enables companies to quickly respond to market fluctuations by utilizing highly timely data and implement effective strategies and measures. For example, the market simulation system collects topics that are rapidly spreading on SNS and the latest news articles. This data is collected in real time by AI. Next, the AI ​​analyzes the collected data in the market simulation system. Based on the collected data, the AI ​​analyzes market trends and fluctuations and predicts what kind of impact they will have. For example, if a positive trend regarding a particular product occurs on SNS, it is predicted that demand for that product may increase. Furthermore, the AI ​​performs a market simulation in the market simulation system. Market simulations use collected data and analysis results to simulate how strategies and measures will respond to market fluctuations. For example, if a new marketing campaign is implemented, the simulation will show how that campaign will impact the market. Finally, the market simulation system proposes optimal strategies and measures based on the simulation results. AI analyzes the simulation results and proposes the most effective strategies and measures. For example, if an increase in demand for a particular product is predicted, it will suggest strengthening promotion for that product. This system allows companies to leverage highly timely data to respond quickly to market fluctuations and implement effective strategies and measures.This allows market simulation systems to leverage highly timely data, enabling companies to respond quickly to market fluctuations and implement effective strategies and measures.

[0064] The market simulation system according to this embodiment comprises a data collection unit, an analysis unit, a simulation unit, and a proposal unit. The data collection unit collects data. For example, the data collection unit collects trends and news from social media. The data collection unit can collect topics that are rapidly spreading on social media and the latest news articles. The data collection unit can also collect data in real time using AI. The data collection unit can filter data based on specific keywords or hashtags, for example. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes market trends and fluctuations based on the collected data. For example, the analysis unit can analyze market trends and fluctuations using time series analysis or trend analysis. The analysis unit can also analyze data using AI and predict what kind of impact it will have. The simulation unit performs market simulations based on the analysis results obtained by the analysis unit. For example, the simulation unit can simulate a new marketing campaign. Based on the collected data and analysis results, the simulation unit simulates how strategies and measures will respond to market fluctuations. The simulation unit can also perform market simulations using AI. The proposal department proposes optimal strategies and measures based on the simulation results obtained by the simulation department. For example, the proposal department may propose strengthening promotions. The proposal department analyzes the simulation results and proposes the most effective strategies and measures. The proposal department can also propose optimal strategies and measures using AI. As a result, the market simulation system according to this embodiment can respond quickly to highly immediate market fluctuations by consistently performing everything from data collection and analysis to simulation and proposal.

[0065] The data collection unit collects data. For example, it collects trends and news from social media. Specifically, it can collect topics that are rapidly spreading on social media and the latest news articles. The data collection unit can also collect data in real time using AI. For example, it can filter data based on specific keywords or hashtags. The data collection unit uses social media APIs to automatically collect posts related to specific keywords or hashtags. This allows the data collection unit to quickly grasp the latest trends and topics. The data collection unit also monitors RSS feeds of news sites and retrieves data whenever new articles are published. Furthermore, the data collection unit can use web scraping technology to extract necessary information from specific websites. This allows the data collection unit to collect a wide range of data from diverse sources and understand market trends in real time. The data collection unit centrally manages the collected data and makes it accessible to the analysis and simulation units. For example, the collected data is stored on a cloud server and can be linked with other systems and departments as needed. This allows the data collection unit to collect data efficiently and effectively and improve the overall system performance.

[0066] The analysis unit analyzes the data collected by the data collection unit. Based on the collected data, the analysis unit analyzes market trends and fluctuations. Specifically, the analysis unit can analyze market trends and fluctuations using time series analysis and trend analysis. For example, it can use time series analysis to extract market fluctuation patterns from past data and predict future trends. The analysis unit can also use AI to analyze data and predict what kind of impact it will have. Using machine learning algorithms, the AI ​​can find patterns and correlations from large amounts of data and predict future market trends. For example, it can use natural language processing technology to analyze the content of social media posts and extract consumer sentiment and opinions. This allows the analysis unit to grasp changes in consumer interest and opinions in real time and predict market trends. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data and issue warnings early. This allows the analysis unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0067] The Simulation Department conducts market simulations based on the analysis results obtained by the Analysis Department. Specifically, the Simulation Department can simulate new marketing campaigns. For example, based on collected data and analysis results, the Simulation Department simulates how strategies and measures will respond to market fluctuations. The Simulation Department can also perform market simulations using AI. The AI ​​builds simulation models and predicts the most likely outcome by simulating multiple scenarios. For example, it can simulate fluctuations in sales when a new promotional campaign is implemented, or the market's reaction to the actions of competitors. This allows the Simulation Department to evaluate the effectiveness of strategies and measures in advance and formulate optimal countermeasures. Furthermore, the Simulation Department can continuously revise simulation results based on real-time updated data to respond to the latest situations. For example, if market trends or consumer reactions change rapidly, the Simulation Department immediately incorporates new data and updates the simulation results. This allows the Simulation Department to always provide highly accurate simulations based on the latest information, supporting quick and appropriate responses.

[0068] The Proposal Department proposes optimal strategies and measures based on the simulation results obtained by the Simulation Department. Specifically, the Proposal Department can propose strengthening promotions. For example, it can analyze simulation results and propose the most effective strategies and measures. The Proposal Department can also propose optimal strategies and measures using AI. Based on the simulation results, the AI ​​evaluates multiple strategies and measures and selects the most effective one. For example, it can evaluate the effectiveness of a promotion campaign for a specific target group and propose the most suitable advertising media and message. This allows the Proposal Department to support companies in responding quickly to market fluctuations and implementing effective strategies. Furthermore, the Proposal Department can collect user feedback and continuously improve the accuracy and effectiveness of its proposals. For example, it can monitor the results after the implementation of proposed strategies and measures and revise the proposals based on the feedback. In addition, the Proposal Department can reliably transmit information using multiple communication methods. For example, it can quickly communicate proposals to company representatives via email or dashboards. This allows the Proposal Department to quickly and reliably propose optimal strategies and measures to companies and support them in responding to market fluctuations.

[0069] The data collection unit can collect social media trends and news. For example, it can collect topics that are rapidly spreading on social media and the latest news articles. The data collection unit can also collect data in real time using AI. For example, the data collection unit can filter data based on specific keywords or hashtags. This allows for the use of highly timely data by collecting social media trends and news. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input specific keywords or hashtags into AI to collect social media trends and news and collect relevant data.

[0070] The analysis unit can analyze market trends and fluctuations based on collected data. For example, the analysis unit can analyze market trends and fluctuations using time series analysis and trend analysis. The analysis unit can also use AI to analyze data and predict what kind of impact it will have. For example, if a positive trend occurs regarding a particular product on social media, the analysis unit may predict that demand for that product may increase. In this way, data-driven predictions become possible by analyzing market trends and fluctuations. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input collected data into AI to analyze market trends and fluctuations.

[0071] The simulation unit can simulate new marketing campaigns. Based on collected data and analysis results, the simulation unit simulates how strategies and measures will respond to market fluctuations. The simulation unit can also perform market simulations using AI. For example, if a new marketing campaign is implemented, the simulation unit can simulate what impact that campaign will have on the market. This allows for the simulation of the effectiveness of a new marketing campaign in advance. Some or all of the above-described processes in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input data for a new marketing campaign into AI and simulate its effects.

[0072] The proposal department can propose strengthening promotions. The proposal department analyzes simulation results and proposes the most effective strategies and measures. The proposal department can also use AI to propose optimal strategies and measures. For example, if the proposal department predicts an increase in demand for a particular product, it will propose strengthening the promotion of that product. By proposing strengthened promotions, effective measures can be implemented. 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 simulation results into AI and propose optimal strategies and measures.

[0073] The data collection unit can estimate the user's emotions and adjust the types of data collected based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting positive trends and news. For example, if the user is anxious, the data collection unit may prioritize collecting risk-related data. For example, if the user is relaxed, the data collection unit may collect general market trends. This allows for the collection of more relevant data by adjusting the types of data collected according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into AI and adjust the types of data collected.

[0074] The data collection unit can filter data based on specific keywords or hashtags. For example, the data collection unit can collect data using keywords related to a specific product name or brand name. For example, the data collection unit can collect data using hashtags related to a specific event or campaign. For example, the data collection unit can collect data using keywords related to a specific market segment. This allows for the collection of highly relevant data by filtering data based on specific keywords or hashtags. 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 specific keywords or hashtags into AI and collect relevant data.

[0075] The data collection unit can evaluate the reliability of the data and collect only reliable data. For example, the data collection unit can evaluate the source of the data and prioritize collecting data from reliable sources. For example, the data collection unit can evaluate the content of the data and collect data containing reliable information. For example, the data collection unit can evaluate the timestamp of the data and prioritize collecting the most recent data. This improves the accuracy of the analysis results by collecting only reliable data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can have AI evaluate the reliability of the data and collect only reliable data.

[0076] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit may prioritize collecting positive trends and news. For example, if the user is anxious, the data collection unit may prioritize collecting risk-related data. For example, if the user is relaxed, the data collection unit may prioritize collecting general market trends. This allows for the priority collection of more important data by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into AI to determine the priority of the data to collect.

[0077] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. For example, if the user is in a specific country, the data collection unit can prioritize the collection of data related to that country. For example, if the user is in a specific city, the data collection unit can prioritize the collection of data related to that city. In this way, by considering the user's geographical location, data related to the region can be prioritized. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and prioritize the collection of highly relevant data.

[0078] The data collection unit can analyze a user's social media activity and collect relevant data. For example, the data collection unit can analyze posts from accounts that a user follows and collect relevant data. For example, the data collection unit can analyze posts from groups and communities that a user participates in and collect relevant data. For example, the data collection unit can analyze posts that a user has shared and collect relevant data. This allows for the collection of highly relevant data by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into AI and collect relevant data.

[0079] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is excited, the analysis unit may prioritize positive data in the analysis. For example, if the user is anxious, the analysis unit may prioritize risk-related data in the analysis. For example, if the user is relaxed, the analysis unit may prioritize general market trends in the analysis. This allows for more appropriate analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and adjust the analysis method.

[0080] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can perform an analysis with a moderate level of detail on data with moderate importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. 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 have AI evaluate the importance of the data and adjust the level of detail of the analysis accordingly.

[0081] The analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit can apply a text analysis algorithm to SNS data. For example, the analysis unit can apply a topic modeling algorithm to news data. For example, the analysis unit can apply a time series analysis algorithm to market data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have AI classify the data categories and apply an appropriate analysis algorithm.

[0082] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated user emotions. For example, if the user is excited, the analysis unit may prioritize the analysis of positive data. For example, if the user is feeling anxious, the analysis unit may prioritize the analysis of risk-related data. For example, if the user is relaxed, the analysis unit may prioritize the analysis of general market trends. This allows for the priority of analysis of important data by determining the priority of analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into AI and determine the priority of analysis.

[0083] The analysis unit can determine the priority of analysis based on the data submission date. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may postpone the analysis of past data. For example, the analysis unit may prioritize the analysis of data with a recent submission date. In this way, by determining the priority of analysis based on the data submission date, the most recent data can be analyzed first. 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 may have AI evaluate the data submission date and determine the priority of analysis.

[0084] The analysis unit can adjust the order of analysis based on the relevance of the data. For example, the analysis unit may prioritize the analysis of data with high relevance. For example, the analysis unit may postpone the analysis of data with low relevance. For example, the analysis unit may analyze data with moderate relevance with moderate priority. In this way, by adjusting the order of analysis based on the relevance of the data, highly relevant data can be analyzed preferentially. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit may have AI evaluate the relevance of the data and adjust the order of analysis.

[0085] The simulation unit can estimate the user's emotions and adjust the simulation criteria based on the estimated user emotions. For example, if the user is excited, the simulation unit will prioritize positive scenarios in the simulation. For example, if the user is anxious, the simulation unit can prioritize risk scenarios in the simulation. For example, if the user is relaxed, the simulation unit can prioritize general scenarios in the simulation. By adjusting the simulation criteria according to the user's emotions, a more appropriate simulation becomes possible. 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 simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into AI and adjust the simulation criteria.

[0086] The simulation unit can improve the accuracy of the simulation by considering the interrelationships between data. For example, the simulation unit can perform a simulation by considering the interrelationships between SNS data and news data. For example, the simulation unit can perform a simulation by considering the interrelationships between market data and trend data. For example, the simulation unit can perform a simulation by considering the interrelationships between user behavior data and market data. This improves the accuracy of the simulation by considering the interrelationships between data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI evaluate the interrelationships between data to improve the accuracy of the simulation.

[0087] The simulation unit can perform simulations while considering the attribute information of the data submitter. For example, the simulation unit can perform simulations while considering the age group of the submitter. For example, the simulation unit can perform simulations while considering the region information of the submitter. For example, the simulation unit can perform simulations while considering the occupation information of the submitter. This makes it possible to perform simulations with higher accuracy by considering the attribute information of the data submitter. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without using AI. For example, the simulation unit can input the submitter's attribute information into AI and perform simulations.

[0088] The simulation unit can estimate the user's emotions and adjust the order in which the simulation results are displayed based on the estimated user emotions. For example, if the user is excited, the simulation unit can prioritize displaying positive results. For example, if the user is feeling anxious, the simulation unit can prioritize displaying risk-related results. For example, if the user is relaxed, the simulation unit can prioritize displaying general results. This allows for the provision of more appropriate information by adjusting the display order of simulation results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can input user emotion data into AI and adjust the display order of the simulation results.

[0089] The simulation unit can perform simulations while considering the geographical distribution of the data. For example, the simulation unit can perform simulations while emphasizing data related to a specific region. For example, the simulation unit can perform simulations while emphasizing data related to a specific country. For example, the simulation unit can perform simulations while emphasizing data related to a specific city. This makes it possible to perform region-specific simulations by considering the geographical distribution of the data. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI evaluate the geographical distribution of the data and then perform a simulation.

[0090] The simulation unit can improve the accuracy of the simulation by referring to relevant literature on the data. For example, the simulation unit can perform simulations by referring to relevant academic papers. For example, the simulation unit can perform simulations by referring to relevant industry reports. For example, the simulation unit can perform simulations by referring to relevant market research data. This improves the accuracy of the simulation by referring to relevant literature. Some or all of the above processing in the simulation unit may be performed using AI, for example, or without AI. For example, the simulation unit can have AI refer to relevant literature to improve the accuracy of the simulation.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on those emotions. For example, if the user is excited, the suggestion unit can make suggestions using positive language. If the user is feeling anxious, the suggestion unit can make suggestions that mitigate risks. If the user is relaxed, the suggestion unit can make suggestions using general language. By adjusting the way suggestions are expressed according to the user's emotions, more effective suggestions become possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI, or not using AI. For example, the suggestion unit can input user emotion data into AI and adjust the way suggestions are expressed.

[0092] The proposal department can adjust the level of detail in its proposals based on the importance of the strategies and measures. For example, the proposal department can provide detailed proposals for highly important strategies. For example, it can provide simplified proposals for less important strategies. For example, it can provide proposals with a moderate level of detail for strategies of moderate importance. This allows for efficient proposals by adjusting the level of detail in proposals based on the importance of the strategies and measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the importance of strategies and measures and adjust the level of detail in the proposals accordingly.

[0093] The proposal department can apply different proposal algorithms depending on the category of strategy or measure. For example, the proposal department can apply a marketing algorithm to a marketing strategy. For example, the proposal department can apply a sales algorithm to a sales strategy. For example, the proposal department can apply a product development algorithm to a product development strategy. By applying the appropriate proposal algorithm according to the category of strategy or measure, the accuracy of the proposals is improved. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can have AI classify the categories of strategies and measures and apply the appropriate proposal algorithm.

[0094] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is excited, the suggestion unit will prioritize positive suggestions. For example, if the user is anxious, the suggestion unit will prioritize suggestions that mitigate risk. For example, if the user is relaxed, the suggestion unit will prioritize general suggestions. In this way, by determining the priority of suggestions according to the user's emotions, more important suggestions can be prioritized. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input user emotion data into AI to determine the priority of suggestions.

[0095] The proposal department can determine the priority of proposals based on the submission timing of strategies and measures. For example, the proposal department can prioritize proposals for strategies with an upcoming submission deadline. For example, the proposal department can postpone proposals for strategies with a distant submission deadline. For example, the proposal department can give a moderate priority to proposals for strategies with a medium submission deadline. This allows for timely proposals by determining the priority of proposals based on the submission timing of strategies and measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can have AI evaluate the submission timing of strategies and measures and then determine the priority of proposals.

[0096] The proposal department can adjust the order of proposals based on the relevance of strategies and measures. For example, the proposal department can prioritize proposals for strategies with high relevance. For example, it can postpone proposals for strategies with low relevance. For example, it can give medium priority to proposals for strategies with moderate relevance. In this way, by adjusting the order of proposals based on the relevance of strategies and measures, more relevant proposals can be prioritized. 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 have AI evaluate the relevance of strategies and measures and adjust the order of proposals.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The market simulation system can further consider the user's purchase history when performing simulations. For example, it can collect data on products and services that the user has purchased in the past and use this to predict market trends and fluctuations. If a user frequently purchases products from a particular brand, the system can prioritize the analysis of trends related to that brand. Similarly, if a user frequently purchases products from a specific category, the system can prioritize data related to that category in its simulations. This allows for more personalized simulations by considering the user's purchase history. Purchase history data can be collected from sources such as the user's online shopping history and loyalty card usage history. The data collection unit can analyze the user's purchasing trends based on this data and provide it to the simulation unit. The simulation unit can then predict market trends and fluctuations based on the provided purchase history data and propose optimal strategies and measures.

[0099] The market simulation system can further estimate user emotions and customize the simulation results based on those emotions. For example, if a user is excited, the simulation results can highlight positive scenarios. If a user is anxious, measures to mitigate risks can be prioritized. If a user is relaxed, the simulation results can emphasize general market trends. By customizing the simulation results according to the user's emotions, more appropriate information can be provided. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice data. The analysis unit can estimate the user's emotions based on this data and provide it to the simulation unit. The simulation unit can then customize the simulation results based on the provided emotion data and provide them to the proposal unit.

[0100] The market simulation system can also perform simulations while taking into account the user's geographical location. For example, if the user is in a specific region, the system will prioritize collecting data related to that region and perform simulations accordingly. If the user is in a specific country, the system can prioritize market trends related to that country in its simulations. Similarly, if the user is in a specific city, the system can prioritize data related to that city in its simulations. This allows for region-specific simulations by considering the user's geographical location. Geographical location information can be obtained, for example, from the user's smartphone's GPS data or IP address. The data collection unit can analyze the user's location information based on this data and provide it to the simulation unit. The simulation unit can then predict market trends and fluctuations based on the provided location data and propose optimal strategies and measures.

[0101] The market simulation system can further analyze users' social media activity and collect relevant data. For example, it can analyze posts from accounts that users follow and collect relevant data. It can also analyze posts from groups and communities that users participate in and collect relevant data. Furthermore, it can analyze posts that users have shared and collect relevant data. In this way, by analyzing users' social media activity, highly relevant data can be collected. Based on this data, the collection unit can analyze users' social media activity and provide it to the simulation unit. Based on the provided social media data, the simulation unit can predict market trends and fluctuations and propose optimal strategies and measures.

[0102] The market simulation system can further estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is excited, it can present suggestions using positive language. If the user is anxious, it can present suggestions that mitigate risk. If the user is relaxed, it can present suggestions using general language. By adjusting the presentation of suggestions according to the user's emotions, more effective suggestions become possible. Emotion estimation is performed, for example, by analyzing the user's facial expressions and voice data. The analysis unit can estimate the user's emotions based on this data and provide it to the suggestion unit. The suggestion unit can then adjust the presentation of suggestions based on the provided emotion data and present them to the user.

[0103] The market simulation system can further consider the user's purchase history when making suggestions. For example, it can suggest new products and services related to the user's past purchases based on data from those products and services. If a user frequently purchases products from a particular brand, the system can suggest new products from that brand. Similarly, if a user frequently purchases products from a specific category, the system can suggest new products related to that category. This allows for more personalized suggestions by considering the user's purchase history. Purchase history data is collected from sources such as the user's online shopping history and loyalty card usage history. The data collection unit can analyze the user's purchasing trends based on this data and provide it to the suggestion unit. The suggestion unit can then make optimal suggestions based on the provided purchase history data and provide them to the user.

[0104] The market simulation system can further estimate user emotions and prioritize suggestions based on those emotions. For example, if a user is excited, positive suggestions will be prioritized. If a user is anxious, suggestions that mitigate risk will be prioritized. If a user is relaxed, general suggestions will be prioritized. This allows for prioritizing more important suggestions based on user emotions. Emotion estimation is performed, for example, by analyzing user facial expressions and voice data. The analysis unit can estimate user emotions based on this data and provide it to the suggestion unit. The suggestion unit can then determine the priority of suggestions based on the provided emotion data and provide them to the user.

[0105] The market simulation system can also make suggestions while considering the user's geographical location. For example, if the user is in a specific region, it can suggest products and services related to that region. If the user is in a specific country, it can make suggestions based on market trends related to that country. Also, if the user is in a specific city, it can suggest products and services related to that city. This makes it possible to make region-specific suggestions by considering the user's geographical location. Geographical location information is obtained, for example, from the user's smartphone's GPS data or IP address. The collection unit can analyze the user's location information based on this data and provide it to the suggestion unit. The suggestion unit can make optimal suggestions based on the provided location data and provide them to the user.

[0106] The market simulation system can further estimate user emotions and adjust the simulation criteria based on those estimated emotions. For example, if a user is excited, the simulation can prioritize positive scenarios. If a user is anxious, the simulation can prioritize risk scenarios. If a user is relaxed, the simulation can prioritize general scenarios. By adjusting the simulation criteria according to user emotions, a more appropriate simulation becomes possible. Emotion estimation is performed, for example, by analyzing user facial expressions and voice data. The analysis unit can estimate user emotions based on this data and provide it to the simulation unit. The simulation unit can then adjust the simulation criteria based on the provided emotion data to perform an optimal simulation.

[0107] Market simulation systems can further improve the accuracy of simulations by considering users' purchase history. For example, they can predict relevant market trends based on data of products and services that users have purchased in the past. If a user frequently purchases products from a particular brand, the simulation can focus on trends related to that brand. Similarly, if a user frequently purchases products from a particular category, the simulation can focus on data related to that category. This allows for more accurate simulations by considering users' purchase history. Purchase history data is collected from sources such as users' online shopping history and loyalty card usage history. The data collection unit can analyze user purchasing trends based on this data and provide it to the simulation unit. The simulation unit can then predict market trends and fluctuations based on the provided purchase history data and propose optimal strategies and measures.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The data collection unit collects data. For example, the data collection unit collects social media trends and news. The data collection unit can collect topics that are rapidly spreading on social media and the latest news articles. The data collection unit can also collect data in real time using AI. The data collection unit can filter data based on specific keywords or hashtags, for example. Step 2: The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes market trends and fluctuations based on the collected data. For example, the analysis unit can analyze market trends and fluctuations using time series analysis or trend analysis. The analysis unit can also use AI to analyze the data and predict what kind of impact it will have. Step 3: The simulation unit performs market simulations based on the analysis results obtained by the analysis unit. For example, the simulation unit can simulate a new marketing campaign. Based on the collected data and analysis results, the simulation unit simulates how strategies and measures will respond to market fluctuations. The simulation unit can also perform market simulations using AI. Step 4: The proposal department proposes the optimal strategies and measures based on the simulation results obtained by the simulation department. For example, the proposal department might propose strengthening promotions. The proposal department analyzes the simulation results and proposes the most effective strategies and measures. The proposal department can also use AI to propose the optimal strategies and measures.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects SNS trends and news using the camera 42 and communication I / F 44 of the smart device 14, and filters the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes market trends and fluctuations based on the collected data. The simulation unit is implemented in the specific processing unit 290 of the data processing unit 12, and performs market simulations based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes optimal strategies and measures based on the simulation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects SNS trends and news using the camera 42 and communication I / F 44 of the smart glasses 214, and filters the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes market trends and fluctuations based on the collected data. The simulation unit is implemented in the specific processing unit 290 of the data processing unit 12, and performs market simulations based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes optimal strategies and measures based on the simulation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects SNS trends and news using the camera 42 and communication I / F 44 of the headset terminal 314, and filters the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes market trends and fluctuations based on the collected data. The simulation unit is implemented in the specific processing unit 290 of the data processing unit 12, and performs market simulations based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes optimal strategies and measures based on the simulation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the data collection unit, analysis unit, simulation unit, and proposal unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects SNS trends and news using the camera 42 and communication I / F 44 of the robot 414, and filters the data using the control unit 46A. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, and analyzes market trends and fluctuations based on the collected data. The simulation unit is implemented in the specific processing unit 290 of the data processing unit 12, and performs market simulations based on the analysis results. The proposal unit is implemented in the specific processing unit 290 of the data processing unit 12, and proposes optimal strategies and measures based on the simulation results. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A simulation unit that performs market simulations based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that proposes optimal strategies and measures based on the simulation results obtained by the simulation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect social media trends and news. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, market trends and fluctuations are analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned simulation unit, Simulate a new marketing campaign. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, I suggest strengthening our promotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates the user's emotions and adjusts the types of data collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Filter data based on specific keywords or hashtags. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Evaluate the reliability of the data and collect only the most reliable data. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is Prioritize 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 11) The aforementioned collection unit is Analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Apply different analysis algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Prioritize analysis based on data submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned simulation unit, It estimates the user's emotions and adjusts the simulation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned simulation unit, Improving the accuracy of simulations by considering the interrelationships between data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned simulation unit, The simulation will be performed taking into account the attribute information of the data submitters. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned simulation unit, It estimates the user's emotions and adjusts the order in which the simulation results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned simulation unit, Perform the simulation while considering the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned simulation unit, Referencing relevant data literature improves the accuracy of simulations. The system described in Appendix 1, characterized by the features described herein. (Note 24) 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 25) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the importance of the strategies and measures. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, Apply different proposal algorithms depending on the category of strategy or measure. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, Prioritize proposals based on the timing of strategy and policy submissions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, Adjust the order of proposals based on the relevance of strategies and measures. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A simulation unit that performs market simulations based on the analysis results obtained by the aforementioned analysis unit, The system includes a proposal unit that proposes optimal strategies and measures based on the simulation results obtained by the simulation unit. A system characterized by the following features.

2. The aforementioned collection unit is Collecting trends and news from social media. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, market trends and fluctuations are analyzed. The system according to feature 1.

4. The aforementioned simulation unit, Simulate a new marketing campaign. The system according to feature 1.

5. The aforementioned proposal section is, I suggest strengthening our promotions. The system according to feature 1.

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

7. The aforementioned collection unit is Filter data based on specific keywords or hashtags. The system according to feature 1.

8. The aforementioned collection unit is Evaluate the reliability of the data and collect only the most reliable data. The system according to feature 1.

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