System

Abstract

An object of a system according to an embodiment is to efficiently collect and analyze market research data of a target area and generate and sell theoretical market research data.SOLUTION: A system includes a data collection part, an analysis part, a generation part, and a sales part. The data collection unit collects big data and market information of a target area. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates theoretical market research data based on the data analyzed by the analysis unit. The sales unit sells the market research data generated by the generation unit to companies and individuals.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to efficiently collect and analyze market research data in target areas and generate theoretical market research data.

[0005] The system according to the embodiment aims to efficiently collect and analyze market research data in a target area, and generate and sell theoretical market research data. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a generation unit, and a sales unit. The data collection unit collects big data and market information for a target area. The analysis unit analyzes the data collected by the data collection unit. The generation unit generates theoretical market research data based on the data analyzed by the analysis unit. The sales unit sells the market research data generated by the generation unit to companies and individuals. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze market research data in a target area, and generate and sell theoretical market research data. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

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

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

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

[0028] (Example 1) The market research data providing system according to an embodiment of the present invention is a system that theoretically creates market research data required when individuals or companies open new stores or expand their businesses by utilizing big data and market information from a target area, and sells the data to companies and individuals. As a result, the market research data providing system can provide highly reliable market research data when individuals or companies open new stores or expand their businesses.

[0029] A market research data providing system according to an embodiment includes a data collection unit, an analysis unit, a generation unit, and a sales unit. The data collection unit collects big data and market information for a target area. For example, the data collection unit collects demographic data. The data collection unit can also collect age group distribution data. The data collection unit can also collect industry number data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected demographic data. The analysis unit can also analyze the collected age group distribution data. The analysis unit can also analyze the collected industry number data. The generation unit generates theoretical market research data based on the data analyzed by the analysis unit. For example, the generation unit generates market research data based on consumer purchasing trends. The generation unit can also generate market research data based on the status of competitors. The generation unit can also generate market research data based on future market growth forecasts. The sales unit sells the market research data generated by the generation unit to companies and individuals. For example, the sales unit sells the market research data through an online platform. The sales department can also provide customized market research reports. The sales department can also provide market research data on a subscription model. As a result, the market research data providing system according to the embodiment can provide market research data necessary for individuals and companies to open new stores or expand their businesses. For example, companies can develop store opening strategies based on detailed market research data. Individuals can plan business expansion based on the latest market information. Companies and individuals can develop specific strategies based on customized market research reports.

[0030] The data collection unit analyzes traffic data and movement patterns, and can predict consumer activity ranges and purchasing behavior. In the data collection unit, for example, the generation AI collects traffic data for a target area and analyzes consumer movement patterns. For example, it identifies consumer activity ranges based on movement patterns during commute hours and on weekends. The data collection unit also predicts consumer purchasing behavior based on movement pattern data. For example, it analyzes the frequency of visits to specific stores or shopping malls and evaluates purchasing intentions. The data collection unit also integrates traffic data and purchasing data to analyze consumer purchasing behavior in detail. For example, it predicts consumer purchasing trends based on sales data for stores along a specific traffic route. This makes it possible to predict consumer activity ranges and purchasing behavior.

[0031] The data collection unit can analyze weather data and generate market research data that takes into account the impact of weather on consumer behavior. For example, the data collection unit uses a generation AI to collect weather data for a target area and analyze the impact of weather on consumer behavior. For example, it analyzes changes in purchasing behavior during rainy weather. The data collection unit also integrates weather data and purchasing data to evaluate the impact of weather on consumer purchasing willingness. For example, it compares sales data on sunny and rainy days. The data collection unit also predicts consumer purchasing behavior based on the weather data. For example, it predicts purchasing willingness under specific weather conditions and reflects this in marketing strategies. This makes it possible to provide market research data that takes into account the impact of weather on consumer behavior.

[0032] The data collection unit can integrate big data from different industries to provide cross-industry market research data. For example, the data collection unit collects big data from different industries, and the generative AI integrates and analyzes it. For example, data from the fashion and food and beverage industries is combined to analyze consumer lifestyles. The data collection unit also predicts consumer purchasing behavior and trends based on cross-industry data. For example, sales data for specific fashion items and restaurants is integrated to evaluate consumer preferences. The data collection unit also integrates data from different industries to discover new market opportunities. For example, data from the fashion and technology industries is combined to help develop new products. This makes it possible to provide cross-industry market research data.

[0033] The generation unit compares past market data with current data and can predict future market growth. For example, the generation AI collects past market data and current data and performs comparative analysis. For example, it predicts future market growth based on sales data from the past 10 years. The generation unit also integrates past market data with current data to predict future market growth trends. For example, it calculates the growth rate in a specific industry. The generation unit also reports detailed future market growth forecasts based on comparative analysis of market data. For example, it identifies regions and industries where growth is expected. This makes it possible to predict future market growth.

[0034] The generation unit can analyze consumer purchase history data and create individual consumer profiles. For example, the generation AI collects consumer purchase history data and creates individual consumer profiles. For example, it identifies consumer preferences and purchasing patterns based on past purchase history. The generation unit also analyzes the consumer profile in detail based on the purchase history data. For example, it evaluates purchase frequency and average purchase amount and predicts the consumer's purchasing power. The generation unit also proposes target marketing strategies based on the individual consumer profiles. For example, it implements customized promotions for specific consumer groups. This makes it possible to create individual consumer profiles.

[0035] The generation unit can compare market data from different regions and analyze market trends between regions. For example, the generation AI collects market data from different regions and performs comparative analysis. For example, it compares market trends between urban and rural areas and understands the characteristics of each region. The generation unit also analyzes consumer purchasing behavior and trends based on market data between regions. For example, it identifies products and services that are popular in specific regions. The generation unit also reports market trends between regions in detail and proposes marketing strategies for each region. For example, it implements promotions that meet the consumer needs of each region. This makes it possible to analyze market trends between regions.

[0036] The generation unit can integrate market data from different industries and provide market research data that takes into account the interactions between industries. For example, the generation AI collects market data from different industries and performs integrated analysis. For example, it combines data from the food and beverage industry and the retail industry to analyze consumer purchasing behavior. The generation unit also provides market research data that takes into account the interactions between industries. For example, it evaluates consumer lifestyles based on data from the food and beverage industry and the retail industry. The generation unit also integrates data from different industries to discover new market opportunities. For example, it combines data from the food and beverage industry and the entertainment industry to propose new business models. This makes it possible to provide market research data that takes into account the interactions between industries.

[0037] The generation unit can analyze a company's past performance data and provide market research data that takes into account the company's strengths and weaknesses. For example, the generation AI collects a company's past performance data and analyzes its strengths and weaknesses. For example, it evaluates a company's performance based on sales data and customer satisfaction data. The generation unit also provides market research data that takes into account the company's strengths and weaknesses. For example, it makes proposals for marketing strategies that utilize strengths and to compensate for weaknesses. The generation unit also evaluates a company's growth potential based on past performance data. For example, it analyzes past successes and failures and proposes future growth strategies. This makes it possible to provide market research data that takes into account a company's strengths and weaknesses.

[0038] The generation unit can automatically generate customized market research reports according to the specific needs of a company. For example, the generation AI receives prompts from the company's specific needs and automatically generates customized market research reports based on them. For example, it provides a detailed market analysis for entering a new market. The generation unit also creates market research reports focused on specific industries or regions according to the company's needs. For example, it analyzes the situation of competitors and consumer purchasing trends in a specific region. The generation unit also generates customized market research reports in real time to support company decision-making. For example, it instantly updates market research data in response to changes in the company's strategy. This makes it possible to automatically generate customized market research reports.

[0039] The generation unit provides benchmark data from different industries and can evaluate a company's competitiveness. For example, the generation AI collects benchmark data from different industries and evaluates a company's competitiveness. For example, it compares the company's performance based on the performance data of its competitors. The generation unit also evaluates a company's strengths and weaknesses based on the benchmark data and makes suggestions to improve its competitiveness. For example, it identifies areas where the company excels and areas requiring improvement compared to the industry average. The generation unit also integrates benchmark data from different industries and comprehensively evaluates a company's competitiveness. For example, it analyzes a company's market position based on data from multiple industries. This makes it possible to evaluate a company's competitiveness.

[0040] The generation department can provide market research data focused on a company's specific projects. For example, the generation AI collects data on a company's specific projects and provides market research data based on that data. For example, it performs the market analysis required for a new product development project. The generation department also creates customized market research reports according to the needs of the project. For example, it performs a detailed analysis of a specific target market or the situation of competitors. The generation department also updates the market research data in real time according to the progress of the project. For example, it provides the data required for each phase of the project to support decision-making. This makes it possible to provide market research data focused on a specific project.

[0041] The sales department can analyze the user's purchasing history and propose market research data that best suits the user's needs. In the sales department, for example, a generative AI collects and analyzes the user's purchasing history data. For example, the user's needs are identified based on market research data purchased in the past. The sales department also proposes market research data that best suits the user's needs based on the purchasing history data. For example, data related to a specific industry or region is provided preferentially. The sales department also analyzes the user's purchasing history in real time and proposes market research data that meets the needs. For example, new needs can be addressed immediately when they arise. This makes it possible to propose market research data that best suits the user's needs.

[0042] The sales department can analyze user feedback and continuously improve the quality of the data. In the sales department, for example, the generative AI collects and analyzes feedback data from users. For example, it improves the quality based on evaluations of the accuracy and usefulness of the data. The sales department also implements specific measures to continuously improve the quality of the data based on the feedback data. For example, it improves the frequency of data updates and analysis methods. The sales department also analyzes user feedback in real time and immediately improves the quality of the data. For example, it corrects or adds data based on the feedback. This allows the quality of the data to be continuously improved.

[0043] The sales department can provide market research data in different price ranges and offer options according to the user's budget. For example, the generation AI collects market research data in different price ranges and offers options according to the user's budget. For example, it sets the price difference between basic data and detailed data. The sales department also proposes the most suitable market research data according to the user's budget. For example, it prioritizes providing data that offers the greatest value within the budget. The sales department also provides data in different price ranges in real time and presents options according to the user's needs. For example, it provides customization options according to the budget. This makes it possible to offer options according to the user's budget.

[0044] The sales department can provide market research data in different formats (e.g., PDF, Excel, interactive dashboard). For example, the sales department builds a system in which generative AI provides market research data in different formats. For example, it provides reports in PDF format and data sheets in Excel format. The sales department also provides market research data in the form of an interactive dashboard according to user needs. For example, it provides a dashboard with data visualization and filtering functions. The sales department also updates the data provided in different formats in real time to ensure that users always have the latest information. For example, it automatically updates the data on an online platform. This makes it possible to provide market research data in different formats.

[0045] The data collection unit can regularly add new data sources to increase the diversity of the data. For example, the generation AI regularly collects new data sources and adds them to the database. For example, it incorporates new market research reports and consumer survey data. The data collection unit also increases the diversity of the data by adding new data sources. For example, it integrates data from different industries and regions to perform comprehensive market analysis. The data collection unit also regularly updates the data sources to provide the latest market information. For example, it adds new data every month to ensure that users always have the latest information. This increases the diversity of the data.

[0046] The data collection unit can compare past data with current data and develop algorithms to maintain data consistency. For example, the data collection unit uses a generative AI to collect past data and current data and perform comparative analysis. For example, it compares past market trends with current data and evaluates consistency. The data collection unit also develops algorithms to maintain data consistency and improve data quality. For example, it checks consistency between different data sources. The data collection unit also integrates past and current data to provide consistent market research data. For example, it identifies long-term market trends and makes future predictions. This makes it possible to develop algorithms to maintain data consistency.

[0047] The data collection unit can integrate data from different regions to provide global market research data. For example, the data collection unit uses generative AI to collect market data from different regions and perform integrated analysis. For example, it can integrate market data from Asia, Europe, and the United States to understand global market trends. The data collection unit also provides global market research data based on market data between regions. For example, it can compare consumer behavior and trends in each region and propose global marketing strategies. The data collection unit also integrates data from different regions to discover new market opportunities. For example, it can propose global business models that take into account the characteristics of each region. This makes it possible to provide global market research data.

[0048] The data collection unit can integrate data from different industries and provide market research data that takes into account the interactions between industries. For example, the data collection unit uses a generative AI to collect market data from different industries and perform integrated analysis. For example, data from the technology industry and healthcare industry is combined to analyze consumer purchasing behavior. The data collection unit also provides market research data that takes into account the interactions between industries. For example, consumer lifestyles are evaluated based on data from the technology industry and healthcare industry. The data collection unit also integrates data from different industries to discover new market opportunities. For example, data from the technology industry and entertainment industry is combined to propose new business models. This makes it possible to provide market research data that takes into account the interactions between industries.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] The data collection unit can collect consumer purchasing history data and analyze consumer purchasing patterns. For example, it can analyze the purchase frequency of specific products or services to understand consumer preferences. The data collection unit can also predict consumer purchasing behavior based on the purchase history data. For example, it can analyze purchasing trends during specific seasons or events and reflect this in marketing strategies. The data collection unit can also evaluate consumer loyalty based on the purchase history data. For example, it can measure the effectiveness of loyalty programs based on the percentage of repeat customers and purchase amounts. This allows for a detailed understanding of consumer purchasing patterns and the development of effective marketing strategies.

[0051] The data collection unit can collect consumer location data and analyze the consumer's range of movement and purchasing behavior. For example, it can analyze the frequency of visits to specific stores or shopping malls to understand consumer behavior patterns. The data collection unit can also predict consumer purchasing behavior based on the location data. For example, it can analyze purchasing trends in specific areas and reflect this in marketing strategies. The data collection unit can also visualize the consumer's range of movement on a map based on the location data. For example, it can display the consumer's movement patterns in color, allowing the consumer's range of movement to be understood at a glance. This allows for a detailed understanding of the consumer's range of movement and purchasing behavior, making it possible to develop effective marketing strategies.

[0052] The data collection unit can analyze weather data and generate market research data that takes into account the impact of weather on consumer behavior. For example, changes in purchasing behavior during rainy weather are analyzed and reflected in marketing strategies. The data collection unit can also integrate weather data and purchasing data to evaluate the impact of weather on consumer purchasing willingness. For example, sales data during sunny and rainy weather can be compared to develop a marketing strategy that takes into account weather conditions. The data collection unit can also predict consumer purchasing behavior based on weather data. For example, purchasing willingness under specific weather conditions can be predicted and reflected in marketing strategies. This makes it possible to provide market research data that takes into account the impact of weather on consumer behavior.

[0053] The data collection unit can integrate big data from different industries to provide cross-industry market research data. For example, data from the fashion and food and beverage industries can be combined to analyze consumer lifestyles. The data collection unit can also predict consumer purchasing behavior and trends based on cross-industry data. For example, sales data for specific fashion items can be integrated with sales data from restaurants to evaluate consumer preferences. The data collection unit can also integrate data from different industries to discover new market opportunities. For example, data from the fashion and technology industries can be combined to help develop new products. This allows the unit to provide cross-industry market research data.

[0054] The generation unit can compare past market data with current data to predict future market growth. For example, it can predict future market growth based on sales data from the past 10 years. The generation unit can also integrate past market data with current data to predict future market growth trends. For example, it can calculate the growth rate in a specific industry. The generation unit can also generate detailed reports on future market growth forecasts based on comparative analysis of market data. For example, it can identify regions and industries that are expected to grow. This makes it possible to predict future market growth.

[0055] The generation unit can analyze consumer purchase history data and create individual consumer profiles. For example, the consumer's preferences and purchasing patterns can be identified based on past purchase history. The generation unit can also analyze the consumer profile in detail based on the purchase history data. For example, the generation unit can evaluate purchase frequency and average purchase amount and predict the consumer's purchasing power. The generation unit can also propose target marketing strategies based on the individual consumer profiles. For example, the generation unit can implement customized promotions for specific consumer groups. This allows the creation of individual consumer profiles and the development of effective marketing strategies.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The data collection department collects big data and market information for the target area. For example, the data collection department collects demographic data, age distribution data, and number of industries. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected demographic data, age distribution data, and number of industries data. Step 3: The generation unit generates theoretical market research data based on the data analyzed by the analysis unit. For example, the generation unit generates market research data based on consumer purchasing trends, the status of competitors, and future market growth forecasts. Step 4: The sales department sells the market research data generated by the generation department to companies and individuals. For example, the sales department sells the market research data through an online platform and provides customized market research reports or market research data on a subscription model.

[0058] (Example 2) The market research data providing system according to an embodiment of the present invention is a system that theoretically creates market research data required when individuals or companies open new stores or expand their businesses by utilizing big data and market information from a target area, and sells the data to companies and individuals. As a result, the market research data providing system can provide highly reliable market research data when individuals or companies open new stores or expand their businesses.

[0059] A market research data providing system according to an embodiment includes a data collection unit, an analysis unit, a generation unit, and a sales unit. The data collection unit collects big data and market information for a target area. For example, the data collection unit collects demographic data. The data collection unit can also collect age group distribution data. The data collection unit can also collect industry number data. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected demographic data. The analysis unit can also analyze the collected age group distribution data. The analysis unit can also analyze the collected industry number data. The generation unit generates theoretical market research data based on the data analyzed by the analysis unit. For example, the generation unit generates market research data based on consumer purchasing trends. The generation unit can also generate market research data based on the status of competitors. The generation unit can also generate market research data based on future market growth forecasts. The sales unit sells the market research data generated by the generation unit to companies and individuals. For example, the sales unit sells the market research data through an online platform. The sales department can also provide customized market research reports. The sales department can also provide market research data on a subscription model. As a result, the market research data providing system according to the embodiment can provide market research data necessary for individuals and companies to open new stores or expand their businesses. For example, companies can develop store opening strategies based on detailed market research data. Individuals can plan business expansion based on the latest market information. Companies and individuals can develop specific strategies based on customized market research reports.

[0060] The data collection unit can analyze social media data and generate market research data that reflects real-time consumer sentiment and trends. For example, the data collection unit uses a generation AI to collect social media data from a target area and analyze consumer posts. For example, it analyzes sentiment toward a specific product or service and identifies products with many positive responses. The data collection unit also analyzes hashtags and keywords on social media to understand consumer interests and trends. For example, it analyzes responses to specific events or campaigns and reflects this in marketing strategies. The data collection unit also evaluates consumer purchasing intent and brand loyalty based on social media data. For example, it predicts consumer purchasing intent based on positive comments and the number of shares. This makes it possible to provide market research data that reflects real-time consumer sentiment and trends.

[0061] The data collection unit analyzes traffic data and movement patterns, and can predict consumer activity ranges and purchasing behavior. In the data collection unit, for example, the generation AI collects traffic data for a target area and analyzes consumer movement patterns. For example, it identifies consumer activity ranges based on movement patterns during commute hours and on weekends. The data collection unit also predicts consumer purchasing behavior based on movement pattern data. For example, it analyzes the frequency of visits to specific stores or shopping malls and evaluates purchasing intentions. The data collection unit also integrates traffic data and purchasing data to analyze consumer purchasing behavior in detail. For example, it predicts consumer purchasing trends based on sales data for stores along a specific traffic route. This makes it possible to predict consumer activity ranges and purchasing behavior.

[0062] The data collection unit can use the emotion estimation function to collect consumer emotion data and identify areas with a high level of positive emotion. For example, the data collection unit uses the emotion estimation function to analyze consumers' social media posts and reviews to collect emotion data. For example, it identifies areas with a high level of positive comments. The data collection unit also visualizes areas with a high level of positive emotion on a map based on the consumer emotion data. For example, it can display emotion scores in different colors, making it possible to identify positive areas at a glance. The data collection unit also analyzes the characteristics of areas with a high level of positive emotion based on the emotion data. For example, it evaluates whether a specific event or store is causing positive emotion. This makes it possible to identify areas with a high level of positive emotion.

[0063] The data collection unit can analyze weather data and generate market research data that takes into account the impact of weather on consumer behavior. For example, the data collection unit uses a generation AI to collect weather data for a target area and analyze the impact of weather on consumer behavior. For example, it analyzes changes in purchasing behavior during rainy weather. The data collection unit also integrates weather data and purchasing data to evaluate the impact of weather on consumer purchasing willingness. For example, it compares sales data on sunny and rainy days. The data collection unit also predicts consumer purchasing behavior based on the weather data. For example, it predicts purchasing willingness under specific weather conditions and reflects this in marketing strategies. This makes it possible to provide market research data that takes into account the impact of weather on consumer behavior.

[0064] The data collection unit can integrate big data from different industries to provide cross-industry market research data. For example, the data collection unit collects big data from different industries, and the generative AI integrates and analyzes it. For example, data from the fashion and food and beverage industries is combined to analyze consumer lifestyles. The data collection unit also predicts consumer purchasing behavior and trends based on cross-industry data. For example, sales data for specific fashion items and restaurants is integrated to evaluate consumer preferences. The data collection unit also integrates data from different industries to discover new market opportunities. For example, data from the fashion and technology industries is combined to help develop new products. This makes it possible to provide cross-industry market research data.

[0065] The data collection unit can use the emotion estimation function to collect consumer emotion data in real time and provide market research data according to emotional fluctuations. The data collection unit, for example, uses the emotion estimation function to collect real-time consumer emotion data. For example, it analyzes social media posts and reviews to understand emotional fluctuations. The data collection unit also updates the market research data based on the emotion data collected in real time. For example, it adjusts marketing strategies according to emotional fluctuations. The data collection unit also visualizes the emotion data in real time, allowing consumers to understand emotional fluctuations at a glance. For example, it displays emotion scores on a graph or map. This makes it possible to provide market research data according to emotional fluctuations.

[0066] The generation unit compares past market data with current data and can predict future market growth. For example, the generation AI collects past market data and current data and performs comparative analysis. For example, it predicts future market growth based on sales data from the past 10 years. The generation unit also integrates past market data with current data to predict future market growth trends. For example, it calculates the growth rate in a specific industry. The generation unit also reports detailed future market growth forecasts based on comparative analysis of market data. For example, it identifies regions and industries where growth is expected. This makes it possible to predict future market growth.

[0067] The generation unit can analyze consumer purchase history data and create individual consumer profiles. For example, the generation AI collects consumer purchase history data and creates individual consumer profiles. For example, it identifies consumer preferences and purchasing patterns based on past purchase history. The generation unit also analyzes the consumer profile in detail based on the purchase history data. For example, it evaluates purchase frequency and average purchase amount and predicts the consumer's purchasing power. The generation unit also proposes target marketing strategies based on the individual consumer profiles. For example, it implements customized promotions for specific consumer groups. This makes it possible to create individual consumer profiles.

[0068] The generation unit can use the emotion estimation function to make an emotionally positive market prediction based on consumer emotion data. For example, the generation unit uses the emotion estimation function to collect consumer emotion data and predict markets with a high level of positive emotion. For example, it identifies products and services with a high positive emotion score. The generation unit also makes an emotionally positive market prediction based on the emotion data. For example, it identifies regions and industries with a high level of positive emotion and predicts markets that are expected to grow. The generation unit also analyzes the characteristics of markets with a high level of positive emotion based on the emotion estimation data. For example, it evaluates whether a specific event or campaign is causing positive emotion. This makes it possible to make an emotionally positive market prediction.

[0069] The generation unit can compare market data from different regions and analyze market trends between regions. For example, the generation AI collects market data from different regions and performs comparative analysis. For example, it compares market trends between urban and rural areas and understands the characteristics of each region. The generation unit also analyzes consumer purchasing behavior and trends based on market data between regions. For example, it identifies products and services that are popular in specific regions. The generation unit also reports market trends between regions in detail and proposes marketing strategies for each region. For example, it implements promotions that meet the consumer needs of each region. This makes it possible to analyze market trends between regions.

[0070] The generation unit can integrate market data from different industries and provide market research data that takes into account the interactions between industries. For example, the generation AI collects market data from different industries and performs integrated analysis. For example, it combines data from the food and beverage industry and the retail industry to analyze consumer purchasing behavior. The generation unit also provides market research data that takes into account the interactions between industries. For example, it evaluates consumer lifestyles based on data from the food and beverage industry and the retail industry. The generation unit also integrates data from different industries to discover new market opportunities. For example, it combines data from the food and beverage industry and the entertainment industry to propose new business models. This makes it possible to provide market research data that takes into account the interactions between industries.

[0071] The generation unit can use the emotion estimation function to make an emotionally positive market prediction based on consumer emotion data. For example, the generation unit uses the emotion estimation function to collect consumer emotion data and predict markets with a high level of positive emotion. For example, it identifies products and services with a high positive emotion score. The generation unit also makes an emotionally positive market prediction based on the emotion data. For example, it identifies regions and industries with a high level of positive emotion and predicts markets that are expected to grow. The generation unit also analyzes the characteristics of markets with a high level of positive emotion based on the emotion estimation data. For example, it evaluates whether a specific event or campaign is causing positive emotion. This makes it possible to make an emotionally positive market prediction.

[0072] The generation unit can analyze a company's past performance data and provide market research data that takes into account the company's strengths and weaknesses. For example, the generation AI collects a company's past performance data and analyzes its strengths and weaknesses. For example, it evaluates a company's performance based on sales data and customer satisfaction data. The generation unit also provides market research data that takes into account the company's strengths and weaknesses. For example, it makes proposals for marketing strategies that utilize strengths and to compensate for weaknesses. The generation unit also evaluates a company's growth potential based on past performance data. For example, it analyzes past successes and failures and proposes future growth strategies. This makes it possible to provide market research data that takes into account a company's strengths and weaknesses.

[0073] The generation unit can automatically generate customized market research reports according to the specific needs of a company. For example, the generation AI receives prompts from the company's specific needs and automatically generates customized market research reports based on them. For example, it provides a detailed market analysis for entering a new market. The generation unit also creates market research reports focused on specific industries or regions according to the company's needs. For example, it analyzes the situation of competitors and consumer purchasing trends in a specific region. The generation unit also generates customized market research reports in real time to support company decision-making. For example, it instantly updates market research data in response to changes in the company's strategy. This makes it possible to automatically generate customized market research reports.

[0074] The generation unit can use the emotion estimation function to provide market research data that increases employee motivation based on the emotion data of the company's employees. The generation unit, for example, uses the emotion estimation function to collect emotion data of the company's employees and provide market research data for increasing employee motivation. For example, it identifies work environments with a high proportion of positive emotions. The generation unit also makes specific suggestions for increasing motivation based on the employee emotion data. For example, it identifies areas for improvement in the work environment based on the employee emotion scores. The generation unit also creates a market research report for increasing employee motivation based on the emotion data. For example, it visualizes employee emotion fluctuations and proposes measures to improve motivation. This makes it possible to provide market research data that increases employee motivation.

[0075] The generation unit provides benchmark data from different industries and can evaluate a company's competitiveness. For example, the generation AI collects benchmark data from different industries and evaluates a company's competitiveness. For example, it compares the company's performance based on the performance data of its competitors. The generation unit also evaluates a company's strengths and weaknesses based on the benchmark data and makes suggestions to improve its competitiveness. For example, it identifies areas where the company excels and areas requiring improvement compared to the industry average. The generation unit also integrates benchmark data from different industries and comprehensively evaluates a company's competitiveness. For example, it analyzes a company's market position based on data from multiple industries. This makes it possible to evaluate a company's competitiveness.

[0076] The generation department can provide market research data focused on a company's specific projects. For example, the generation AI collects data on a company's specific projects and provides market research data based on that data. For example, it performs the market analysis required for a new product development project. The generation department also creates customized market research reports according to the needs of the project. For example, it performs a detailed analysis of a specific target market or the situation of competitors. The generation department also updates the market research data in real time according to the progress of the project. For example, it provides the data required for each phase of the project to support decision-making. This makes it possible to provide market research data focused on a specific project.

[0077] The generation unit can use the emotion estimation function to provide market research data that increases employee motivation based on the emotion data of the company's employees. The generation unit, for example, uses the emotion estimation function to collect emotion data of the company's employees and provide market research data for increasing employee motivation. For example, it identifies work environments with a high proportion of positive emotions. The generation unit also makes specific suggestions for increasing motivation based on the employee emotion data. For example, it identifies areas for improvement in the work environment based on the employee emotion scores. The generation unit also creates a market research report for increasing employee motivation based on the emotion data. For example, it visualizes employee emotion fluctuations and proposes measures to improve motivation. This makes it possible to provide market research data that increases employee motivation.

[0078] The sales department can analyze the user's purchasing history and propose market research data that best suits the user's needs. In the sales department, for example, a generative AI collects and analyzes the user's purchasing history data. For example, the user's needs are identified based on market research data purchased in the past. The sales department also proposes market research data that best suits the user's needs based on the purchasing history data. For example, data related to a specific industry or region is provided preferentially. The sales department also analyzes the user's purchasing history in real time and proposes market research data that meets the needs. For example, new needs can be addressed immediately when they arise. This makes it possible to propose market research data that best suits the user's needs.

[0079] The sales department can analyze user feedback and continuously improve the quality of the data. In the sales department, for example, the generative AI collects and analyzes feedback data from users. For example, it improves the quality based on evaluations of the accuracy and usefulness of the data. The sales department also implements specific measures to continuously improve the quality of the data based on the feedback data. For example, it improves the frequency of data updates and analysis methods. The sales department also analyzes user feedback in real time and immediately improves the quality of the data. For example, it corrects or adds data based on the feedback. This allows the quality of the data to be continuously improved.

[0080] The sales department can use the emotion estimation function to suggest market research data that is of most interest to the user based on the user's emotion data. The sales department, for example, uses the emotion estimation function to collect and analyze the user's emotion data. For example, data related to themes with strong positive emotions is provided preferentially. The sales department also suggests market research data that is of most interest to the user based on the user's emotion data. For example, data related to themes with high emotion scores is provided. The sales department also analyzes the emotion data in real time to suggest market research data that is in line with the user's interests. For example, the content of the suggested data is adjusted according to the user's emotional fluctuations. This makes it possible to suggest market research data that is of most interest to the user.

[0081] The sales department can provide market research data in different price ranges and offer options according to the user's budget. For example, the generation AI collects market research data in different price ranges and offers options according to the user's budget. For example, it sets the price difference between basic data and detailed data. The sales department also proposes the most suitable market research data according to the user's budget. For example, it prioritizes providing data that offers the greatest value within the budget. The sales department also provides data in different price ranges in real time and presents options according to the user's needs. For example, it provides customization options according to the budget. This makes it possible to offer options according to the user's budget.

[0082] The sales department can provide market research data in different formats (e.g., PDF, Excel, interactive dashboard). For example, the sales department builds a system in which generative AI provides market research data in different formats. For example, it provides reports in PDF format and data sheets in Excel format. The sales department also provides market research data in the form of an interactive dashboard according to user needs. For example, it provides a dashboard with data visualization and filtering functions. The sales department also updates the data provided in different formats in real time to ensure that users always have the latest information. For example, it automatically updates the data on an online platform. This makes it possible to provide market research data in different formats.

[0083] The sales department can use the emotion estimation function to suggest market research data that is of most interest to the user based on the user's emotion data. The sales department, for example, uses the emotion estimation function to collect and analyze the user's emotion data. For example, data related to themes with strong positive emotions is provided preferentially. The sales department also suggests market research data that is of most interest to the user based on the user's emotion data. For example, data related to themes with high emotion scores is provided. The sales department also analyzes the emotion data in real time to suggest market research data that is in line with the user's interests. For example, the content of the suggested data is adjusted according to the user's emotional fluctuations. This makes it possible to suggest market research data that is of most interest to the user.

[0084] The data collection unit can regularly add new data sources to increase the diversity of the data. For example, the generation AI regularly collects new data sources and adds them to the database. For example, it incorporates new market research reports and consumer survey data. The data collection unit also increases the diversity of the data by adding new data sources. For example, it integrates data from different industries and regions to perform comprehensive market analysis. The data collection unit also regularly updates the data sources to provide the latest market information. For example, it adds new data every month to ensure that users always have the latest information. This increases the diversity of the data.

[0085] The data collection unit can compare past data with current data and develop algorithms to maintain data consistency. For example, the data collection unit uses a generative AI to collect past data and current data and perform comparative analysis. For example, it compares past market trends with current data and evaluates consistency. The data collection unit also develops algorithms to maintain data consistency and improve data quality. For example, it checks consistency between different data sources. The data collection unit also integrates past and current data to provide consistent market research data. For example, it identifies long-term market trends and makes future predictions. This makes it possible to develop algorithms to maintain data consistency.

[0086] The data collection unit can continuously collect consumer emotion data using the emotion estimation function and provide market research data according to emotional fluctuations. The data collection unit, for example, uses the emotion estimation function to continuously collect consumer emotion data. For example, it periodically analyzes social media posts and reviews to understand emotional fluctuations. The data collection unit also updates the market research data based on the continuously collected emotion data. For example, it adjusts marketing strategies according to emotional fluctuations. The data collection unit also continuously visualizes the emotion data so that consumer emotion fluctuations can be understood at a glance. For example, it displays emotion scores on a graph or map and updates them in real time. This makes it possible to provide market research data according to emotional fluctuations.

[0087] The data collection unit can integrate data from different regions to provide global market research data. For example, the data collection unit uses generative AI to collect market data from different regions and perform integrated analysis. For example, it can integrate market data from Asia, Europe, and the United States to understand global market trends. The data collection unit also provides global market research data based on market data between regions. For example, it can compare consumer behavior and trends in each region and propose global marketing strategies. The data collection unit also integrates data from different regions to discover new market opportunities. For example, it can propose global business models that take into account the characteristics of each region. This makes it possible to provide global market research data.

[0088] The data collection unit can integrate data from different industries and provide market research data that takes into account the interactions between industries. For example, the data collection unit uses a generative AI to collect market data from different industries and perform integrated analysis. For example, data from the technology industry and healthcare industry is combined to analyze consumer purchasing behavior. The data collection unit also provides market research data that takes into account the interactions between industries. For example, consumer lifestyles are evaluated based on data from the technology industry and healthcare industry. The data collection unit also integrates data from different industries to discover new market opportunities. For example, data from the technology industry and entertainment industry is combined to propose new business models. This makes it possible to provide market research data that takes into account the interactions between industries.

[0089] The data collection unit can continuously collect consumer emotion data using the emotion estimation function and provide market research data according to emotional fluctuations. The data collection unit, for example, uses the emotion estimation function to continuously collect consumer emotion data. For example, it periodically analyzes social media posts and reviews to understand emotional fluctuations. The data collection unit also updates the market research data based on the continuously collected emotion data. For example, it adjusts marketing strategies according to emotional fluctuations. The data collection unit also continuously visualizes the emotion data so that consumer emotion fluctuations can be understood at a glance. For example, it displays emotion scores on a graph or map and updates them in real time. This makes it possible to provide market research data according to emotional fluctuations.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The data collection unit can collect consumer purchasing history data and analyze consumer purchasing patterns. For example, it can analyze the purchase frequency of specific products or services to understand consumer preferences. The data collection unit can also predict consumer purchasing behavior based on the purchase history data. For example, it can analyze purchasing trends during specific seasons or events and reflect this in marketing strategies. The data collection unit can also evaluate consumer loyalty based on the purchase history data. For example, it can measure the effectiveness of loyalty programs based on the percentage of repeat customers and purchase amounts. This allows for a detailed understanding of consumer purchasing patterns and the development of effective marketing strategies.

[0092] The data collection unit can use the emotion estimation function to collect consumer emotion data and propose marketing strategies based on consumer emotions. For example, it can identify products or services that have a high percentage of positive emotions and promote them based on those. The data collection unit can also grasp fluctuations in consumer emotions in real time based on the emotion data. For example, it can analyze emotional fluctuations regarding specific events or campaigns and adjust marketing strategies. The data collection unit can also provide customized marketing messages based on consumer emotions based on the emotion data. For example, it can send messages that evoke positive emotions to increase consumer purchasing intent. This makes it possible to develop effective marketing strategies based on consumer emotions.

[0093] The data collection unit can collect consumer location data and analyze the consumer's range of movement and purchasing behavior. For example, it can analyze the frequency of visits to specific stores or shopping malls to understand consumer behavior patterns. The data collection unit can also predict consumer purchasing behavior based on the location data. For example, it can analyze purchasing trends in specific areas and reflect this in marketing strategies. The data collection unit can also visualize the consumer's range of movement on a map based on the location data. For example, it can display the consumer's movement patterns in color, allowing the consumer's range of movement to be understood at a glance. This allows for a detailed understanding of the consumer's range of movement and purchasing behavior, making it possible to develop effective marketing strategies.

[0094] The data collection unit can use the emotion estimation function to collect consumer emotion data and identify areas with a high level of positive emotion. For example, it can analyze social media posts and reviews to identify areas with a high number of positive comments. The data collection unit can also visualize areas with a high level of positive emotion on a map based on the emotion data. For example, it can display emotion scores in different colors, allowing positive areas to be identified at a glance. The data collection unit can also analyze the characteristics of areas with a high level of positive emotion based on the emotion data. For example, it can evaluate whether a specific event or store is causing positive emotion. This allows it to identify areas with a high level of positive emotion and develop effective marketing strategies.

[0095] The data collection unit can analyze weather data and generate market research data that takes into account the impact of weather on consumer behavior. For example, changes in purchasing behavior during rainy weather are analyzed and reflected in marketing strategies. The data collection unit can also integrate weather data and purchasing data to evaluate the impact of weather on consumer purchasing willingness. For example, sales data during sunny and rainy weather can be compared to develop a marketing strategy that takes into account weather conditions. The data collection unit can also predict consumer purchasing behavior based on weather data. For example, purchasing willingness under specific weather conditions can be predicted and reflected in marketing strategies. This makes it possible to provide market research data that takes into account the impact of weather on consumer behavior.

[0096] The data collection unit can integrate big data from different industries to provide cross-industry market research data. For example, data from the fashion and food and beverage industries can be combined to analyze consumer lifestyles. The data collection unit can also predict consumer purchasing behavior and trends based on cross-industry data. For example, sales data for specific fashion items can be integrated with sales data from restaurants to evaluate consumer preferences. The data collection unit can also integrate data from different industries to discover new market opportunities. For example, data from the fashion and technology industries can be combined to help develop new products. This allows the unit to provide cross-industry market research data.

[0097] The data collection unit can use the emotion estimation function to collect consumer emotion data in real time and provide market research data according to emotional fluctuations. For example, it can analyze social media posts and reviews to understand emotional fluctuations. The data collection unit can also update the market research data based on the emotion data collected in real time. For example, it can adjust marketing strategies according to emotional fluctuations. The data collection unit can also visualize the emotion data in real time, allowing consumers to understand emotional fluctuations at a glance. For example, it can display emotion scores on a graph or map. This makes it possible to provide market research data according to emotional fluctuations.

[0098] The generation unit can compare past market data with current data to predict future market growth. For example, it can predict future market growth based on sales data from the past 10 years. The generation unit can also integrate past market data with current data to predict future market growth trends. For example, it can calculate the growth rate in a specific industry. The generation unit can also generate detailed reports on future market growth forecasts based on comparative analysis of market data. For example, it can identify regions and industries that are expected to grow. This makes it possible to predict future market growth.

[0099] The generation unit can analyze consumer purchase history data and create individual consumer profiles. For example, the consumer's preferences and purchasing patterns can be identified based on past purchase history. The generation unit can also analyze the consumer profile in detail based on the purchase history data. For example, the generation unit can evaluate purchase frequency and average purchase amount and predict the consumer's purchasing power. The generation unit can also propose target marketing strategies based on the individual consumer profiles. For example, the generation unit can implement customized promotions for specific consumer groups. This allows the creation of individual consumer profiles and the development of effective marketing strategies.

[0100] The generation unit can use the emotion estimation function to make emotionally positive market predictions based on consumer emotion data. For example, it can identify products and services with high positive emotion scores. The generation unit can also make emotionally positive market predictions based on emotion data. For example, it can identify regions and industries with a high level of positive emotion and predict markets that are expected to grow. The generation unit can also analyze the characteristics of markets with a high level of positive emotion based on emotion estimation data. For example, it can evaluate whether a specific event or campaign is causing positive emotion. This makes it possible to make emotionally positive market predictions and develop effective marketing strategies.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The data collection department collects big data and market information for the target area. For example, the data collection department collects demographic data, age distribution data, and number of industries. Step 2: The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected demographic data, age distribution data, and number of industries data. Step 3: The generation unit generates theoretical market research data based on the data analyzed by the analysis unit. For example, the generation unit generates market research data based on consumer purchasing trends, the status of competitors, and future market growth forecasts. Step 4: The sales department sells the market research data generated by the generation department to companies and individuals. For example, the sales department sells the market research data through an online platform and provides customized market research reports or market research data on a subscription model.

[0103] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 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.

[0108] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data collection department that collects big data and market information for the target area; an analysis unit that analyzes the data collected by the data collection unit; a generation unit that generates theoretical market research data based on the data analyzed by the analysis unit; a sales department that sells the market research data generated by the generation department to companies and individuals. A system characterized by:

2. The data collection unit Analyze social media data to generate market research data that reflects real-time consumer sentiment and trends 2. The system of claim 1.

3. The data collection unit Analyzing traffic data and movement patterns to predict consumer movement ranges and purchasing behavior 2. The system of claim 1.

4. The data collection unit Collect consumer sentiment data and identify neighborhoods with high levels of positive sentiment 2. The system of claim 1.

5. The data collection unit Analyze weather data and generate market research data that takes into account the impact of weather on consumer behavior.

2. The system of claim 1.

6. The data collection unit Integrate the big data from different industries to provide cross-industry market research data 2. The system of claim 1.

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