system

Abstract

We provide the system. [Solution] A data collection means that receives user data and market trend data, A text feature extraction method for quantifying the above data, A clustering method that clusters data based on digitized data, A data visualization method for visualizing clustering results, A system that includes this.

Description

【Technical Field】 , , , , , 【0004】 , , , , 【0005】 , , , , , 【0003】 , , 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance. 【Prior Art Documents】 【Patent Documents】 【0003】 【Patent Document 1】 Japanese Patent Application Laid-Open No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 In the modern market, it is important to quickly and accurately grasp customer needs and market trends in product development and improvement, and means for effectively utilizing this information are required. However, it has been difficult with conventional methods to extract relevant information from a huge amount of data and analyze it in an understandable manner. In addition, there is a lack of an efficient methodology for deriving insights applicable to specific design solutions. To solve such problems, a system that consistently performs data collection, analysis, and visualization is necessary. 【Means for Solving the Problems】 【0005】 To address the above challenges, the present invention provides data collection means for receiving user data and market trend data. Furthermore, it includes text feature extraction means for quantifying this data and clustering means for clustering the data based on the quantified data. This allows for a clear understanding of the data's characteristics. In addition, by providing a system that includes data visualization means for visualizing the clustering results, the distribution and characteristics of the data are made easier to understand intuitively, supporting rapid decision-making. These means make it possible to dramatically improve the efficiency of product development adapted to customer needs and market trends. 【0006】 "User data" refers to information about customer feedback, requests, and usage. 【0007】 "Market trend data" refers to information that shows new trends and needs in the market, as well as the situation of competitors. 【0008】 "Data collection means" refers to a function or device for collecting and storing user data and market trend data. 【0009】 "Text feature extraction means" refers to a function or device for analyzing acquired text data and extracting important features from it as numerical values. 【0010】 "Clustering means" refers to a function or device for grouping and classifying data based on similarity. 【0011】 "Data visualization means" refers to a function or device for visually displaying analyzed data or clustering results. 【0012】 A "system" refers to a collection of multiple functions or devices that work together to achieve a specific purpose. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, a tagged processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like. 【0017】 In the following embodiments, a tagged RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, a tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like. 【0019】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0020】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0024】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0025】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0026】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0027】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0028】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0031】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0032】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0033】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0034】 As a configuration for carrying out this invention, the AI ​​design system includes a process of collecting user data and market trend data, quantifying this data, and performing clustering. 【0035】 The server first acquires user data and market trend data from their respective data collection devices and stores them in a database. This data includes customer feedback and information on market trends. Next, the server uses a text feature extraction module to extract important keywords and phrases from the data and digitize them. This digitized data is treated as a vector that reflects the text's features. 【0036】 Next, the server executes a clustering algorithm to group the data based on its quantified representation. Machine learning algorithms such as K-means are used as clustering methods to classify the data into multiple clusters. This allows for the grouping of information with similar characteristics, making it possible to understand data patterns. 【0037】 Finally, the data visualization module is used to visually display the clustered data. This visualization maps the clustering results onto a two-dimensional plane, intuitively showing the cluster structure and data distribution as a graph. This allows users to clearly understand product design trends and customer requirements, and to gain insights for designing new products or improving existing ones. 【0038】 For example, if a user wants design proposals for a new product category, the system collects data such as "intuitive operation" and "innovative design" as customer requirements, and analyzes market trends such as "use of sustainable materials" and "integration of smart technology." Based on this data, the system clusters the optimal design proposals and visualizes them as graphs, generating design suggestions that align with customer needs and market trends. This implementation allows users to efficiently advance the design process and develop products that are more suitable for the market. 【0039】 The following describes the processing flow. 【0040】 Step 1: 【0041】 The server receives user data and market trend data from various data collection points. Specifically, this involves acquiring customer reviews, survey results, online market trend reports, etc., and integrating this data before storing it in a database. 【0042】 Step 2: 【0043】 The server uses a text feature extraction module to extract text features from the received data. Here, the TF-IDF (Term Frequency-Inverse Document Frequency) method is used to quantify characteristic words and phrases from each text data point. This process models the text content as a numerical vector. 【0044】 Step 3: 【0045】 The server performs clustering based on digitized vector data using clustering techniques. Algorithms such as K-means are applied to classify the data into multiple clusters based on similarity. This process clearly forms groups representing different customer needs and market trends. 【0046】 Step 4: 【0047】 The server visualizes the clustered data using a data visualization module. In this step, the dimensionality reduction method TSNE (t-Distributed Stochastic Neighbor Embedding) is used to reduce the high-dimensional cluster data to a two-dimensional space for visualization. The visualized data is plotted as a color-coded point cloud, illustrating the distribution and characteristics of each cluster. 【0048】 Step 5: 【0049】 Users gain strategic insights into product design based on visualized cluster data. Specifically, by reviewing the visualization results and considering areas for product improvement and new design suggestions, product development that aligns with customer needs and market trends is facilitated. 【0050】 (Example 1) 【0051】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0052】 Modern product development requires accurately understanding consumer needs and market trends, and designing products quickly and effectively based on that understanding. However, efficiently collecting user and market trend information, and effectively analyzing and utilizing that data, is difficult. There is a need for effective methods to overcome this challenge and develop products that are better suited to the market. 【0053】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0054】 In this invention, the server includes information gathering means for acquiring user information and market trend information, text feature extraction means for converting the above information into numerical format, and clustering means for classifying the information based on the numerical information. This enables efficient analysis of the collected information and the provision of insights for product design based on that analysis. 【0055】 "User information" refers to data and opinions about potential and existing customers of a product or service. 【0056】 "Market trend information" refers to information about current market trends, consumer preferences, major competitive activities, etc. 【0057】 "Information gathering methods" refer to processes and systems that aggregate data from diverse sources. 【0058】 "Text feature extraction means" refers to a technology that identifies important elements and features from documents and text data and converts them into numerical data. 【0059】 "Clustering methods" refer to techniques and technologies for grouping data based on similarity. 【0060】 "Information visualization methods" refer to technologies and tools that display complex data and information in an easy-to-understand manner using graphs and diagrams. 【0061】 "Machine learning technology" refers to automated computational methods that find patterns based on data and perform predictions and classifications. 【0062】 "Two-dimensional space" refers to a coordinate system used to visually arrange data on a plane consisting of a horizontal axis and a vertical axis. 【0063】 An "interface" refers to the on-screen display elements and operating environment that allow a user to interact with a computer system or software. 【0064】 This invention is a system that aggregates user information and market trend information, analyzes them, and visualizes them, with the server at its core. The server first acquires user information and market trend information in real time from various data sources via APIs. This information collection process can utilize digital devices, and data scraping techniques are also employed as needed. 【0065】 After collecting the information, the server uses text feature extraction to extract important keywords and phrases and converts them into numerical data. Natural language processing libraries such as NLTK and SpaCy are used here. The resulting numerical data is then used in the next data analysis step. 【0066】 Using clustering techniques, the server classifies data using the K-means method, a machine learning technique. The data is divided into groups with similar characteristics, forming clusters. This process makes it possible to understand patterns and trends in the data. 【0067】 Finally, the resulting clusters are mapped into a two-dimensional space using data visualization tools. Libraries such as Matplotlib and Seaborn are used for this visualization. These tools allow users to visually check the distribution of the data and explore the details through the interface. 【0068】 For example, if a user is requesting a new product design, this system can be used to analyze customer requirements such as "intuitive operation" and "innovative design." A possible prompt might include instructions like, "Please propose a new product design using intuitive operation and sustainable materials." Implementing such a system allows users to streamline the design development process and quickly develop market-ready products. 【0069】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0070】 Step 1: 【0071】 The server collects user information and market trend information via APIs. This information gathering process retrieves data in digital format from online feedback forms and survey data. Input includes raw text and numerical data. The server organizes this data, removing redundancy and noise. The output is stored in a database as a structured dataset. 【0072】 Step 2: 【0073】 The server uses a text feature extraction module to extract important keywords and phrases from stored data and converts them into numerical vectors. Text data is used as input. The server utilizes natural language processing software to extract important features from the text through morphological analysis and TF-IDF calculations. The output is numerical data from which the features have been extracted. 【0074】 Step 3: 【0075】 The server uses the K-means method, a machine learning technique, to cluster digitized data. The input is digitized vectorized feature data. The server groups data with similar characteristics according to the number of clusters and initial settings. The output is a dataset classified into multiple clusters. 【0076】 Step 4: 【0077】 The server uses a visualization library to map the clustering results onto a two-dimensional plane and perform visualization. The clustered dataset is used as input. The server plots each cluster with a different color and shape, generating a graph that allows for easy visualization of the data distribution. The output is a graph that visually represents the structure and patterns of the data. 【0078】 Step 5: 【0079】 Users gain insights into product development based on the generated graphs. Visualized data is presented as input. Users can select specific clusters and view detailed information using mouse operations. The output provides decision-making support information regarding product design and market strategy. 【0080】 (Application Example 1) 【0081】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0082】 In contemporary urban design projects, a challenge is to provide efficient design proposals that reflect the needs of citizens and societal trends. Traditional methods required significant time and effort to collect and analyze vast amounts of information, and limited visual means of understanding these trends made it difficult for designers to provide optimal solutions. 【0083】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0084】 In this invention, the server includes an information gathering means for receiving user information and social trend information, a text feature extraction means for quantifying the above information, a group classification means for grouping information based on the quantified information, an information visualization means for visualizing the grouping results, and a design proposal generation means for generating design proposals related to urban structure. This makes it possible for urban designers and stakeholders to efficiently generate and visually grasp design proposals that reflect the latest social trends. 【0085】 "User information" refers to data collected from individual users, representing their needs and preferences. 【0086】 "Social trend information" refers to data that shows the latest trends and fashions in the market and society as a whole. 【0087】 "Information gathering means" refers to a device or system that has the function of acquiring and integrating user information and social trend information. 【0088】 A "text feature extraction method" is a technology or module for extracting important characteristics and patterns from collected information and quantifying them. 【0089】 A "group classification method" is a method or algorithm for classifying and organizing information based on similarity, using quantified information. 【0090】 "Information visualization methods" are tools and technologies that visually display grouped information, enabling intuitive understanding of the trends and arrangement of that information. 【0091】 A "design proposal generation method" is a process or system for generating design proposals for new urban structures or projects based on visualized information. 【0092】 This system efficiently collects and analyzes user information and social trend information to generate urban structure design proposals. The server integrates user information and social trend information using information collection methods. At this stage, data is transmitted from sensing devices and mobile terminals on the network. 【0093】 Next, the server's text feature extraction mechanism analyzes the collected information and quantifies important characteristics. It converts the text data into feature vectors using natural language processing libraries such as Python's NLTK or scikit-learn. Then, it applies an automated learning algorithm such as the K-means method as a group classification mechanism to classify the quantified data based on similarity. 【0094】 In information visualization methods, clustered data is displayed on a two-dimensional plane using visualization tools such as Matplotlib and Plotly. This allows urban planners and stakeholders to visually grasp the current situation and trends. 【0095】 Furthermore, the design proposal generation method generates new urban structure design proposals based on visualized data. This process utilizes a generation AI model to create sophisticated design proposals, which are then provided to stakeholders as concrete proposals. 【0096】 As a concrete example, when designing a new park in the city center, we can obtain user requests such as "design in harmony with nature" and analyze trends in "sustainable energy use" from social trend information. Based on this information, it is possible to generate and visually present park design proposals. 【0097】 Examples of prompts include, "Propose an innovative transportation system design for an urban development project," or "Visualize the features of a next-generation smart park based on customer needs." By entering these prompts, you can obtain useful design proposals. 【0098】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0099】 Step 1: 【0100】 The server uses information gathering tools to collect user information and social trend information via the network. Inputs include feedback data from users' mobile devices and market trend data from online databases. This information is stored in the database in its raw form. 【0101】 Step 2: 【0102】 The server's text feature extraction mechanism analyzes the information stored in the database. The input includes user information and social trend information in text format. The server uses the Python NLTK library to extract important keywords and convert them into numerical feature vectors. At this stage, the information is output in vector format. 【0103】 Step 3: 【0104】 The server clusters the digitized information using a group classification method. The input is the feature vectors generated in step 2. The server classifies these vectors into groups based on similarity using the K-means algorithm. As a result of this process, the information is output as clustered groups. 【0105】 Step 4: 【0106】 The server's information visualization method visualizes the clustering results. The input includes the clustering results generated in step 3. The server uses Matplotlib or Plotly to plot the groups in a two-dimensional space, visually showing the arrangement of the information. At this stage, the visualized data is output. 【0107】 Step 5: 【0108】 The server's design proposal generation mechanism generates design proposals based on visualized data. Inputs include visualization results and prompts for the generating AI model. The server uses the AI ​​model to create new urban design proposals that reflect user needs and social trends, and provides the results as output. 【0109】 In each step, the server's role is multifaceted, ranging from data collection and analysis to visualization and design generation, efficiently supporting urban structure design. 【0110】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0111】 This invention is a system that collects user data and market trend data, quantifies and clusters this data, and further combines it with an emotion engine that recognizes user emotions. The system aims to optimize design proposals while taking the influence of emotions into consideration. 【0112】 The server first acquires user data and market trend data using various data collection methods. This acquired data includes customer reviews, survey results, and social media posts. The data is then passed to a text feature extraction module, where it is digitized. This digitized data is used to capture the key points and characteristics of the information. 【0113】 Next, the server uses a clustering mechanism to perform data clustering based on the digitized data. The clustering algorithm utilizes machine learning to classify the data into multiple clusters based on similarity. This allows for the organization of different customer needs and market trends. 【0114】 In addition, the server uses an emotion engine to analyze the user's emotional data. The emotion engine utilizes natural language processing techniques to calculate emotional values ​​from text data and determine whether the emotional state is positive or negative. This emotional information becomes a crucial factor influencing design proposals. 【0115】 Finally, the server visualizes the clustering and sentiment analysis results using data visualization tools. The visualized results are displayed through an interface in a two-dimensional space. This display provides detailed analysis results useful for design consideration, enabling users to gain concrete insights for improving new and existing products. 【0116】 As a concrete example, when a user is determining the direction of a new product design, initial customer reactions to design proposals are evaluated through sentiment analysis. By incorporating elements that evoke strong positive emotions, development that contributes to improved customer satisfaction becomes possible. Through this implementation, users can refine their product strategy based on emotional information and quickly deliver products that are suitable for the market. 【0117】 The following describes the processing flow. 【0118】 Step 1: 【0119】 The server collects user data and market trend data from various data sources. This data includes online reviews, user surveys, and social media posts. This data is stored in a database that is updated in real time or periodically. 【0120】 Step 2: 【0121】 The server analyzes the collected user data using natural language processing tools and quantifies the text data. Specifically, it uses TF-IDF (Term Frequency-Inverse Document Frequency) and word embedding techniques to extract and quantify important features from each text. This quantification is useful for subsequent analysis and helps to capture the characteristics of the data. 【0122】 Step 3: 【0123】 The server performs clustering using the numerical data generated in the previous step. Clustering uses machine learning techniques such as the K-means algorithm to classify the data into multiple clusters. This process clarifies patterns in customer needs and market trends. 【0124】 Step 4: 【0125】 The server uses an emotion engine to recognize the user's emotions. The emotion engine calculates an emotion score from text data and determines whether the user's emotions are positive or negative. Based on the emotion analysis results, it suggests which design elements are desirable or need improvement. 【0126】 Step 5: 【0127】 The server combines clustering results and sentiment analysis results to visualize the data. The visualization module plots these results in a two-dimensional space and presents them to the user through an interface. The visualized information helps users intuitively understand the overall picture and key elements of the data. 【0128】 Step 6: 【0129】 Users refer to visualized information and proceed through a process of determining the direction of product design. Users can highlight positive elements indicated by sentiment analysis and make revisions based on negative feedback. This provides valuable insights for design improvements and new product planning. 【0130】 (Example 2) 【0131】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0132】 Conventional data analysis systems have the problem of difficulty in optimizing design proposals that accurately reflect user sentiment and market trends. Furthermore, there are challenges in efficiently obtaining the insights users need through data classification and visualization. 【0133】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0134】 In this invention, the server includes an information gathering means for receiving user information and market trend information, a feature extraction means for quantifying the above information, a classification means for classifying the information based on the quantified information, and an emotion recognition means for recognizing the user's emotional state. This makes it possible to optimize effective design proposals based on the user's emotions and market trends. 【0135】 "User information" refers to data that includes the preferences, behavioral patterns, and emotional states of an individual or group. 【0136】 "Market trend information" refers to data on consumer purchasing behavior and market trends in a specific time and region. 【0137】 "Information gathering means" refers to the technologies and methods used to acquire user information and market trend information. 【0138】 "Feature extraction methods" refer to technologies that perform the process of identifying useful characteristics and patterns from raw data and quantifying them. 【0139】 "Classification method" refers to a technique that performs the process of grouping data based on similar characteristics. 【0140】 "Emotion recognition means" refers to technology that identifies and efficiently determines a user's emotional state from text and other data. 【0141】 "Visualization methods" refer to technologies that visually represent data analysis and classification results, enabling users to intuitively understand the information. 【0142】 This invention is a system that optimizes design proposals based on user information and market trend information. The server first acquires user information and market trend information using information gathering means. In this process, web scraping technology and APIs are used to collect data such as customer reviews, social media posts, and survey results. 【0143】 Next, the server quantifies the information collected through the feature extraction means. This step involves text feature extraction using natural language processing techniques, and vectorizes the text using algorithms such as TF-IDF and Word2Vec. 【0144】 Next, the server groups the quantified information using classification methods. By applying machine learning algorithms such as K-means and hierarchical clustering to classify the information into multiple groups, it grasps the diverse needs of users and market trends. 【0145】 Furthermore, the server analyzes the user's emotional state using emotion recognition technology. From the collected text data, a positive or negative emotion score is derived using natural language processing techniques. This information has a significant impact when making design proposals. 【0146】 Finally, the server visualizes the analysis results using visualization tools. The data is visualized in a two-dimensional space and provided as an interface via the user's terminal. This generated visualization becomes a powerful support tool for users when considering their designs. 【0147】 As a concrete example, when a user is considering the design of a new product, they can receive suggestions from a generating AI model that incorporate elements that evoke positive emotions. An example of a prompt would be, "Please tell me the design elements that will get positive feedback from customers." 【0148】 This allows users to quickly receive compelling design suggestions based on emotional information and market trends, enabling them to effectively refine their product strategies. 【0149】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0150】 Step 1: 【0151】 The server collects user information and market trend information as input data using information gathering methods. In this process, it uses web scraping technology and APIs to obtain information from customer reviews, social media posts, etc. The output is raw data to be used in subsequent processing steps. Specifically, for example, it aggregates 1000 social media posts obtained via API. 【0152】 Step 2: 【0153】 The server takes raw data collected using feature extraction methods as input and converts the text data into numerical data. This involves applying TF-IDF or Word2Vec to convert the text data into numerical vectors. The output of this step is a numerical dataset. Specifically, the review "The product quality is high" is converted into a numerical vector. 【0154】 Step 3: 【0155】 The server utilizes classification methods to perform clustering on a quantified dataset as input. It groups the data using machine learning algorithms such as K-means, and the output is multiple data clusters. This allows for the organization of different customer needs and market trends. For example, data can be segmented into groups such as women in their 20s and men in their 30s. 【0156】 Step 4: 【0157】 The server uses emotion recognition technology to perform sentiment analysis on each clustered data cluster as input. Using natural language processing techniques, it outputs positive or negative sentiment scores from the text. Specifically, emotions like "happy" are represented by high scores, providing data useful for product improvement. 【0158】 Step 5: 【0159】 The server uses visualization tools to visualize the results of sentiment analysis and clustering as input. The data is plotted in a two-dimensional space and output as a diagram to the user's terminal. This visualization is used to consider design proposals. Specifically, the clustering results using PCA are displayed as a color-coded chart. 【0160】 Step 6: 【0161】 The user receives design suggestions using an AI model that generates data based on visualized data. The prompt is in the format of "Please tell me the design elements that will get positive feedback from customers," and the AI ​​model outputs suggested design improvements. This allows the user to confirm the direction of specific design changes. 【0162】 (Application Example 2) 【0163】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0164】 In today's advertising industry, providing optimal advertising tailored to the emotions of individual users is challenging. Furthermore, there is a need for methods to efficiently collect vast amounts of customer and market trend information, analyze emotions based on this data, and optimize advertising content. This invention aims to solve this problem. 【0165】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0166】 In this invention, the server includes an information acquisition means for receiving customer information and market trend information, an information feature analysis means for quantifying the above information, and a classification means for classifying the information based on the quantified information. This makes it possible to provide optimal advertisements that respond to the user's emotions. 【0167】 "Customer information" refers to information about users' attributes and activity history that is obtained in order to optimize advertising. 【0168】 "Market trend information" refers to information that shows consumer behavior and trends in the current market. 【0169】 "Information acquisition means" refers to methods or devices for collecting customer information and market trend information. 【0170】 "Information feature analysis means" refers to a method or apparatus for performing the process of quantifying collected information. 【0171】 A "classification tool" is a method or apparatus for analyzing digitized information and classifying it into specific categories or clusters. 【0172】 "Information visualization means" refers to a method or device for visually displaying classified information. 【0173】 "Emotional analysis advertising optimization means" refers to a method or apparatus for analyzing a user's emotions and adjusting advertising content based on the results. 【0174】 In an embodiment of this invention, a server first receives customer information and market trend information. This information may be collected from terminals such as smartphones and personal computers. The collected information is then organized by an information acquisition means. 【0175】 Next, an information feature analysis tool quantifies this information. The quantified data is then analyzed using a natural language processing library (e.g., spaCy or Hugging Face Transformers). This detects the important features of each data point and prepares them for use by subsequent classification tools. 【0176】 The classification method uses machine learning techniques (e.g., K-means, DBSCAN) to classify this quantified information into multiple categories. This helps to organize different user groups and market trends. 【0177】 Furthermore, the sentiment analysis ad optimization method uses sentiment analysis APIs (e.g., IBM Watson®, Google® Cloud Natural Language) to analyze the user's emotions. Based on this analysis, ad content is optimized in real time to provide the user with the most relevant ads. The ads are displayed through the interface of smartphones and smart glasses. 【0178】 For example, if a user frequently posts positive comments about a particular topic on social media, ads for products related to that topic will be displayed preferentially. 【0179】 An example of a prompt message from this system is: "Perform a sentiment analysis on topics the user has recently been interested in, and generate three relevant ad ideas." 【0180】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0181】 Step 1: 【0182】 The server receives customer information and market trend information from the terminal. This information includes user attribute data, social media posting history, and online purchase history. The server organizes this information using information acquisition methods and prepares it for subsequent processing. The input is raw data, and the output is organized customer information. 【0183】 Step 2: 【0184】 The server quantifies the information it receives using information feature analysis tools. It uses a natural language processing library to analyze text data and extract numerical features. This process converts text features such as word frequency and sentiment scores into numerical data. The input is organized customer information, and the output is quantified data. 【0185】 Step 3: 【0186】 The server uses a classification method to quantify data and then categorizes it using machine learning algorithms. It employs the K-means method to group data with similar characteristics. This allows for categorization tailored to different customer segments and market trends. The input is quantified data, and the output is categorized information. 【0187】 Step 4: 【0188】 The server analyzes the sentiment of categorized information using sentiment analysis advertising optimization methods. It calls a sentiment analysis API to understand the user's emotional state. The input is categorized information, and the output is the analysis results, including sentiment scores. 【0189】 Step 5: 【0190】 The server generates ad content based on sentiment scores and displays it on the device. It utilizes a generative AI model to create ad suggestions most relevant to the sentiment score. Prompts are used to support the ad generation process. Ultimately, the ad is displayed on the user's smartphone or smart glasses. The input is the analysis results, including sentiment scores, and the output is the optimized ad content. 【0191】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0192】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0193】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0194】 [Second Embodiment] 【0195】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0196】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0197】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0198】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0199】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0200】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0201】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0202】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0203】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0204】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0205】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0206】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0207】 As a configuration for carrying out this invention, the AI ​​design system includes a process of collecting user data and market trend data, quantifying this data, and performing clustering. 【0208】 The server first acquires user data and market trend data from their respective data collection devices and stores them in a database. This data includes customer feedback and information on market trends. Next, the server uses a text feature extraction module to extract important keywords and phrases from the data and digitize them. This digitized data is treated as a vector that reflects the text's features. 【0209】 Next, the server executes a clustering algorithm to group the data based on its quantified representation. Machine learning algorithms such as K-means are used as clustering methods to classify the data into multiple clusters. This allows for the grouping of information with similar characteristics, making it possible to understand data patterns. 【0210】 Finally, the data visualization module is used to visually display the clustered data. This visualization maps the clustering results onto a two-dimensional plane, intuitively showing the cluster structure and data distribution as a graph. This allows users to clearly understand product design trends and customer requirements, and to gain insights for designing new products or improving existing ones. 【0211】 For example, if a user wants design proposals for a new product category, the system collects data such as "intuitive operation" and "innovative design" as customer requirements, and analyzes market trends such as "use of sustainable materials" and "integration of smart technology." Based on this data, the system clusters the optimal design proposals and visualizes them as graphs, generating design suggestions that align with customer needs and market trends. This implementation allows users to efficiently advance the design process and develop products that are more suitable for the market. 【0212】 The following describes the processing flow. 【0213】 Step 1: 【0214】 The server receives user data and market trend data from various data collection points. Specifically, this involves acquiring customer reviews, survey results, online market trend reports, etc., and integrating this data before storing it in a database. 【0215】 Step 2: 【0216】 The server uses a text feature extraction module to extract text features from the received data. Here, the TF-IDF (Term Frequency-Inverse Document Frequency) method is used to quantify characteristic words and phrases from each text data point. This process models the text content as a numerical vector. 【0217】 Step 3: 【0218】 The server performs clustering based on digitized vector data using clustering techniques. Algorithms such as K-means are applied to classify the data into multiple clusters based on similarity. This process clearly forms groups representing different customer needs and market trends. 【0219】 Step 4: 【0220】 The server visualizes the clustered data using a data visualization module. In this step, the dimensionality reduction method TSNE (t-Distributed Stochastic Neighbor Embedding) is used to reduce the high-dimensional cluster data to a two-dimensional space for visualization. The visualized data is plotted as a color-coded point cloud, illustrating the distribution and characteristics of each cluster. 【0221】 Step 5: 【0222】 Users gain strategic insights into product design based on visualized cluster data. Specifically, by reviewing the visualization results and considering areas for product improvement and new design suggestions, product development that aligns with customer needs and market trends is facilitated. 【0223】 (Example 1) 【0224】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0225】 Modern product development requires accurately understanding consumer needs and market trends, and designing products quickly and effectively based on that understanding. However, efficiently collecting user and market trend information, and effectively analyzing and utilizing that data, is difficult. There is a need for effective methods to overcome this challenge and develop products that are better suited to the market. 【0226】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0227】 In this invention, the server includes information gathering means for acquiring user information and market trend information, text feature extraction means for converting the above information into numerical format, and clustering means for classifying the information based on the numerical information. This enables efficient analysis of the collected information and the provision of insights for product design based on that analysis. 【0228】 "User information" refers to data and opinions about potential and existing customers of a product or service. 【0229】 "Market trend information" refers to information about current market trends, consumer preferences, major competitive activities, etc. 【0230】 "Information gathering methods" refer to processes and systems that aggregate data from diverse sources. 【0231】 "Text feature extraction means" refers to a technology that identifies important elements and features from documents and text data and converts them into numerical data. 【0232】 "Clustering methods" refer to techniques and technologies for grouping data based on similarity. 【0233】 "Information visualization methods" refer to technologies and tools that display complex data and information in an easy-to-understand manner using graphs and diagrams. 【0234】 "Machine learning technology" refers to automated computational methods that find patterns based on data and perform predictions and classifications. 【0235】 "Two-dimensional space" refers to a coordinate system used to visually arrange data on a plane consisting of a horizontal axis and a vertical axis. 【0236】 An "interface" refers to the on-screen display elements and operating environment that allow a user to interact with a computer system or software. 【0237】 This invention is a system that aggregates user information and market trend information, analyzes them, and visualizes them, with the server at its core. The server first acquires user information and market trend information in real time from various data sources via APIs. This information collection process can utilize digital devices, and data scraping techniques are also employed as needed. 【0238】 After collecting the information, the server uses text feature extraction to extract important keywords and phrases and converts them into numerical data. Natural language processing libraries such as NLTK and SpaCy are used here. The resulting numerical data is then used in the next data analysis step. 【0239】 Using clustering techniques, the server classifies data using the K-means method, a machine learning technique. The data is divided into groups with similar characteristics, forming clusters. This process makes it possible to understand patterns and trends in the data. 【0240】 Finally, the resulting clusters are mapped into a two-dimensional space using data visualization tools. Libraries such as Matplotlib and Seaborn are used for this visualization. These tools allow users to visually check the distribution of the data and explore the details through the interface. 【0241】 For example, if a user is requesting a new product design, this system can be used to analyze customer requirements such as "intuitive operation" and "innovative design." A possible prompt might include instructions like, "Please propose a new product design using intuitive operation and sustainable materials." Implementing such a system allows users to streamline the design development process and quickly develop market-ready products. 【0242】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0243】 Step 1: 【0244】 The server collects user information and market trend information via APIs. This information gathering process retrieves data in digital format from online feedback forms and survey data. Input includes raw text and numerical data. The server organizes this data, removing redundancy and noise. The output is stored in a database as a structured dataset. 【0245】 Step 2: 【0246】 The server uses a text feature extraction module to extract important keywords and phrases from stored data and converts them into numerical vectors. Text data is used as input. The server utilizes natural language processing software to extract important features from the text through morphological analysis and TF-IDF calculations. The output is numerical data from which the features have been extracted. 【0247】 Step 3: 【0248】 The server uses the K-means method, a machine learning technique, to cluster digitized data. The input is digitized vectorized feature data. The server groups data with similar characteristics according to the number of clusters and initial settings. The output is a dataset classified into multiple clusters. 【0249】 Step 4: 【0250】 The server uses a visualization library to map the clustering results onto a two-dimensional plane and perform visualization. The clustered dataset is used as input. The server plots each cluster with a different color and shape, generating a graph that allows for easy visualization of the data distribution. The output is a graph that visually represents the structure and patterns of the data. 【0251】 Step 5: 【0252】 Users gain insights into product development based on the generated graphs. Visualized data is presented as input. Users can select specific clusters and view detailed information using mouse operations. The output provides decision-making support information regarding product design and market strategy. 【0253】 (Application Example 1) 【0254】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0255】 In contemporary urban design projects, a challenge is to provide efficient design proposals that reflect the needs of citizens and societal trends. Traditional methods required significant time and effort to collect and analyze vast amounts of information, and limited visual means of understanding these trends made it difficult for designers to provide optimal solutions. 【0256】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0257】 In this invention, the server includes an information gathering means for receiving user information and social trend information, a text feature extraction means for quantifying the above information, a group classification means for grouping information based on the quantified information, an information visualization means for visualizing the grouping results, and a design proposal generation means for generating design proposals related to urban structure. This makes it possible for urban designers and stakeholders to efficiently generate and visually grasp design proposals that reflect the latest social trends. 【0258】 "User information" refers to data collected from individual users, representing their needs and preferences. 【0259】 "Social trend information" refers to data that shows the latest trends and fashions in the market and society as a whole. 【0260】 "Information gathering means" refers to a device or system that has the function of acquiring and integrating user information and social trend information. 【0261】 A "text feature extraction method" is a technology or module for extracting important characteristics and patterns from collected information and quantifying them. 【0262】 A "group classification method" is a method or algorithm for classifying and organizing information based on similarity, using quantified information. 【0263】 "Information visualization methods" are tools and technologies that visually display grouped information, enabling an intuitive understanding of the trends and arrangement of that information. 【0264】 A "design proposal generation method" is a process or system for generating design proposals for new urban structures or projects based on visualized information. 【0265】 This system efficiently collects and analyzes user information and social trend information to generate urban structure design proposals. The server integrates user information and social trend information using information collection methods. At this stage, data is transmitted from sensing devices and mobile terminals on the network. 【0266】 Next, the server's text feature extraction mechanism analyzes the collected information and quantifies important characteristics. It converts the text data into feature vectors using natural language processing libraries such as Python's NLTK or scikit-learn. Then, it applies an automated learning algorithm such as the K-means method as a group classification mechanism to classify the quantified data based on similarity. 【0267】 In information visualization methods, clustered data is displayed on a two-dimensional plane using visualization tools such as Matplotlib and Plotly. This allows urban planners and stakeholders to visually grasp the current situation and trends. 【0268】 Furthermore, the design proposal generation method generates new urban structure design proposals based on visualized data. This process utilizes a generation AI model to create sophisticated design proposals, which are then provided to stakeholders as concrete proposals. 【0269】 As a concrete example, when designing a new park in the city center, we can obtain user requests such as "design in harmony with nature" and analyze trends in "sustainable energy use" from social trend information. Based on this information, it is possible to generate and visually present park design proposals. 【0270】 Examples of prompts include "Propose an innovative transportation system design for an urban development project" or "Visualize the features of a next-generation smart park based on customer needs." By entering these prompts, you can obtain useful design proposals. 【0271】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0272】 Step 1: 【0273】 The server uses information gathering tools to collect user information and social trend information via the network. Inputs include feedback data from users' mobile devices and market trend data from online databases. This information is stored in the database in its raw form. 【0274】 Step 2: 【0275】 The server's text feature extraction mechanism analyzes the information stored in the database. The input includes user information and social trend information in text format. The server uses the Python NLTK library to extract important keywords and convert them into numerical feature vectors. At this stage, the information is output in vector format. 【0276】 Step 3: 【0277】 The server clusters the digitized information using a group classification method. The input is the feature vectors generated in step 2. The server classifies these vectors into groups based on similarity using the K-means algorithm. As a result of this process, the information is output as clustered groups. 【0278】 Step 4: 【0279】 The server's information visualization method visualizes the clustering results. The input includes the clustering results generated in step 3. The server uses Matplotlib or Plotly to plot the groups in a two-dimensional space, visually showing the arrangement of the information. At this stage, the visualized data is output. 【0280】 Step 5: 【0281】 The server's design proposal generation means generates a design proposal based on the visualized data. The inputs include the visualization result and the prompt text in the generation AI model. The server uses the AI model to create a new urban design proposal that reflects user needs and social trends, and provides the result as an output. 【0282】 In each step, the role of the server covers a wide range from data collection to analysis, visualization, and design generation, and efficiently supports urban structure design. 【0283】 Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion. 【0284】 This invention is a system that collects user data and market trend data, digitizes and clusters these data, and further combines an emotion engine that recognizes the user's emotion. The system aims to optimize design proposals while considering the influence of emotions. 【0285】 The server first acquires user data and market trend data using various data collection means. The acquired data includes customer reviews, questionnaire results, SNS posts, etc. Then, it is passed to the text feature extraction module, and the data is digitized. This digitized data is used to capture the key points and features of the information. 【0286】 Next, the server performs data clustering based on the digitized data using clustering means. As the clustering algorithm, machine learning is utilized to classify the data into multiple clusters based on similarity. This enables the organization of different customer needs and market trends. 【0287】 In addition, the server uses an emotion engine to analyze the user's emotional data. The emotion engine utilizes natural language processing techniques to calculate emotional values ​​from text data and determine whether the emotional state is positive or negative. This emotional information becomes a crucial factor influencing design proposals. 【0288】 Finally, the server visualizes the clustering and sentiment analysis results using data visualization tools. The visualized results are displayed through an interface in a two-dimensional space. This display provides detailed analysis results useful for design consideration, enabling users to gain concrete insights for improving new and existing products. 【0289】 As a concrete example, when a user is determining the direction of a new product design, initial customer reactions to design proposals are evaluated through sentiment analysis. By incorporating elements that evoke strong positive emotions, development that contributes to improved customer satisfaction becomes possible. Through this implementation, users can refine their product strategy based on emotional information and quickly deliver products that are suitable for the market. 【0290】 The following describes the processing flow. 【0291】 Step 1: 【0292】 The server collects user data and market trend data from various data sources. This data includes online reviews, user surveys, and social media posts. This data is stored in a database that is updated in real time or periodically. 【0293】 Step 2: 【0294】 The server analyzes the collected user data using natural language processing tools and quantifies the text data. Specifically, it uses TF-IDF (Term Frequency-Inverse Document Frequency) and word embedding techniques to extract and quantify important features from each text. This quantification is useful for subsequent analysis and helps to capture the characteristics of the data. 【0295】 Step 3: 【0296】 The server performs clustering using the numerical data generated in the previous step. Clustering uses machine learning techniques such as the K-means algorithm to classify the data into multiple clusters. This process clarifies patterns in customer needs and market trends. 【0297】 Step 4: 【0298】 The server uses an emotion engine to recognize the user's emotions. The emotion engine calculates an emotion score from text data and determines whether the user's emotions are positive or negative. Based on the emotion analysis results, it suggests which design elements are desirable or need improvement. 【0299】 Step 5: 【0300】 The server combines clustering results and sentiment analysis results to visualize the data. The visualization module plots these results in a two-dimensional space and presents them to the user through an interface. The visualized information helps users intuitively understand the overall picture and key elements of the data. 【0301】 Step 6: 【0302】 The user refers to the visualized information and proceeds to the process of determining the direction of the product design. The user can emphasize the positive elements indicated by the sentiment analysis and make corrections based on the negative feedback, thereby obtaining insights useful for improving the design and planning new products. 【0303】 (Example 2) 【0304】 Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as a "server", and the smart glasses 214 are referred to as a "terminal". 【0305】 In a conventional data analysis system, there is a problem that it is difficult to optimize a design proposal that accurately reflects the user's emotions and market trends. Also, in data classification and visualization, there is an issue that the user cannot efficiently obtain the insights required. 【0306】 The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means. 【0307】 In this invention, the server includes an information collection means for receiving user information and market trend information, a feature extraction means for digitizing the above information, a classification means for classifying information based on the digitized information, and a sentiment recognition means for recognizing the user's emotional state. This enables the optimization of an effective design proposal based on the user's emotions and market trends. 【0308】 "User information" refers to data including the preferences, behavior patterns, and emotional states of individuals or groups. 【0309】 "Market trend information" refers to data regarding consumers' purchasing behaviors and market trends in a specific time or region. 【0310】 "Information collection means" refers to the technologies and methods for acquiring user information and market trend information. 【0311】 "Feature extraction methods" refer to technologies that identify useful characteristics and patterns from raw data and perform the process of quantifying them. 【0312】 "Classification method" refers to a technique that performs the process of grouping data based on similar characteristics. 【0313】 "Emotion recognition means" refers to technology that identifies and efficiently determines a user's emotional state from text and other data. 【0314】 "Visualization methods" refer to technologies that visually represent data analysis and classification results, enabling users to intuitively understand the information. 【0315】 This invention is a system that optimizes design proposals based on user information and market trend information. The server first acquires user information and market trend information using information gathering means. In this process, web scraping technology and APIs are used to collect data such as customer reviews, social media posts, and survey results. 【0316】 Next, the server quantifies the information collected through the feature extraction means. This step involves text feature extraction using natural language processing techniques, and vectorizes the text using algorithms such as TF-IDF and Word2Vec. 【0317】 Next, the server groups the quantified information using classification methods. By applying machine learning algorithms such as K-means and hierarchical clustering to classify the information into multiple groups, it grasps the diverse needs of users and market trends. 【0318】 Furthermore, the server analyzes the user's emotional state using emotion recognition technology. From the collected text data, a positive or negative emotion score is derived using natural language processing techniques. This information has a significant impact when making design proposals. 【0319】 Finally, the server visualizes the analysis results using visualization tools. The data is visualized in a two-dimensional space and provided as an interface via the user's terminal. This generated visualization becomes a powerful support tool for users when considering their designs. 【0320】 As a concrete example, when a user is considering the design of a new product, they can receive suggestions from a generating AI model that incorporate elements that evoke positive emotions. An example of a prompt would be, "Please tell me the design elements that will get positive feedback from customers." 【0321】 This allows users to quickly receive compelling design suggestions based on emotional information and market trends, enabling them to effectively refine their product strategies. 【0322】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0323】 Step 1: 【0324】 The server collects user information and market trend information as input data using information gathering methods. In this process, it uses web scraping technology and APIs to obtain information from customer reviews, social media posts, etc. The output is raw data to be used in subsequent processing steps. Specifically, for example, it aggregates 1000 social media posts obtained via API. 【0325】 Step 2: 【0326】 The server takes raw data collected using feature extraction methods as input and converts the text data into numerical data. This involves applying TF-IDF or Word2Vec to convert the text data into numerical vectors. The output of this step is a numerical dataset. Specifically, the review "The product quality is high" is converted into a numerical vector. 【0327】 Step 3: 【0328】 The server utilizes classification methods to perform clustering on a quantified dataset as input. It groups the data using machine learning algorithms such as K-means, and the output is multiple data clusters. This allows for the organization of different customer needs and market trends. For example, data can be segmented into groups such as women in their 20s and men in their 30s. 【0329】 Step 4: 【0330】 The server uses emotion recognition technology to perform sentiment analysis on each clustered data cluster as input. Using natural language processing techniques, it outputs positive or negative sentiment scores from the text. Specifically, emotions like "happy" are represented by high scores, providing data useful for product improvement. 【0331】 Step 5: 【0332】 The server uses visualization tools to visualize the results of sentiment analysis and clustering as input. The data is plotted in a two-dimensional space and output as a diagram to the user's terminal. This visualization is used to consider design proposals. Specifically, the clustering results using PCA are displayed as a color-coded chart. 【0333】 Step 6: 【0334】 The user receives design suggestions using an AI model that generates data based on visualized data. The prompt is in the format of "Please tell me the design elements that will get positive feedback from customers," and the AI ​​model outputs suggested design improvements. This allows the user to confirm the direction of specific design changes. 【0335】 (Application Example 2) 【0336】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0337】 In today's advertising industry, providing optimal advertising tailored to the emotions of individual users is challenging. Furthermore, there is a need for methods to efficiently collect vast amounts of customer and market trend information, analyze emotions based on this data, and optimize advertising content. This invention aims to solve this problem. 【0338】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0339】 In this invention, the server includes an information acquisition means for receiving customer information and market trend information, an information feature analysis means for quantifying the above information, and a classification means for classifying the information based on the quantified information. This makes it possible to provide optimal advertisements that respond to the user's emotions. 【0340】 "Customer information" refers to information about users' attributes and activity history that is obtained in order to optimize advertising. 【0341】 "Market trend information" refers to information that shows consumer behavior and trends in the current market. 【0342】 "Information acquisition means" refers to methods or devices for collecting customer information and market trend information. 【0343】 "Information feature analysis means" refers to a method or apparatus for performing the process of quantifying collected information. 【0344】 A "classification tool" is a method or apparatus for analyzing digitized information and classifying it into specific categories or clusters. 【0345】 "Information visualization means" refers to a method or device for visually displaying classified information. 【0346】 "Emotional analysis advertising optimization means" refers to a method or apparatus for analyzing a user's emotions and adjusting advertising content based on the results. 【0347】 In an embodiment of this invention, a server first receives customer information and market trend information. This information may be collected from terminals such as smartphones and personal computers. The collected information is then organized by an information acquisition means. 【0348】 Next, an information feature analysis tool quantifies this information. The quantified data is then analyzed using a natural language processing library (e.g., spaCy or Hugging Face Transformers). This detects the important features of each data point and prepares them for use by subsequent classification tools. 【0349】 The classification method uses machine learning techniques (e.g., K-means, DBSCAN) to classify this quantified information into multiple categories. This helps to organize different user groups and market trends. 【0350】 Furthermore, the sentiment analysis ad optimization method uses sentiment analysis APIs (e.g., IBM Watson, Google Cloud Natural Language) to analyze the user's emotions. Based on this analysis, ad content is optimized in real time to provide the most relevant ads to the user. The ads are displayed through the interface of smartphones and smart glasses. 【0351】 For example, if a user frequently posts positive comments about a particular topic on social media, ads for products related to that topic will be displayed preferentially. 【0352】 An example of a prompt message from this system is: "Perform a sentiment analysis on topics the user has recently been interested in, and generate three relevant ad ideas." 【0353】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0354】 Step 1: 【0355】 The server receives customer information and market trend information from the terminal. This information includes user attribute data, social media posting history, and online purchase history. The server organizes this information using information acquisition methods and prepares it for subsequent processing. The input is raw data, and the output is organized customer information. 【0356】 Step 2: 【0357】 The server quantifies the information it receives using information feature analysis tools. It uses a natural language processing library to analyze text data and extract numerical features. This process converts text features such as word frequency and sentiment scores into numerical data. The input is organized customer information, and the output is quantified data. 【0358】 Step 3: 【0359】 The server uses a classification method to quantify data and then categorizes it using machine learning algorithms. It employs the K-means method to group data with similar characteristics. This allows for categorization tailored to different customer segments and market trends. The input is quantified data, and the output is categorized information. 【0360】 Step 4: 【0361】 The server analyzes the sentiment of categorized information using sentiment analysis advertising optimization methods. It calls a sentiment analysis API to understand the user's emotional state. The input is categorized information, and the output is the analysis results, including sentiment scores. 【0362】 Step 5: 【0363】 The server generates ad content based on sentiment scores and displays it on the device. It utilizes a generative AI model to create ad suggestions most relevant to the sentiment score. Prompts are used to support the ad generation process. Ultimately, the ad is displayed on the user's smartphone or smart glasses. The input is the analysis results, including sentiment scores, and the output is the optimized ad content. 【0364】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0365】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0366】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0367】 [Third Embodiment] 【0368】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0369】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0370】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0371】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0372】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0373】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0374】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0375】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0376】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0377】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0378】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0379】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0380】 As a configuration for carrying out this invention, the AI ​​design system includes a process of collecting user data and market trend data, quantifying this data, and performing clustering. 【0381】 The server first acquires user data and market trend data from their respective data collection devices and stores them in a database. This data includes customer feedback and information on market trends. Next, the server uses a text feature extraction module to extract important keywords and phrases from the data and digitize them. This digitized data is treated as a vector that reflects the text's features. 【0382】 Next, the server executes a clustering algorithm to group the data based on its quantified representation. Machine learning algorithms such as K-means are used as clustering methods to classify the data into multiple clusters. This allows for the grouping of information with similar characteristics, making it possible to understand data patterns. 【0383】 Finally, the data visualization module is used to visually display the clustered data. This visualization maps the clustering results onto a two-dimensional plane, intuitively showing the cluster structure and data distribution as a graph. This allows users to clearly understand product design trends and customer requirements, and to gain insights for designing new products or improving existing ones. 【0384】 For example, if a user wants design proposals for a new product category, the system collects data such as "intuitive operation" and "innovative design" as customer requirements, and analyzes market trends such as "use of sustainable materials" and "integration of smart technology." Based on this data, the system clusters the optimal design proposals and visualizes them as graphs, generating design suggestions that align with customer needs and market trends. This implementation allows users to efficiently advance the design process and develop products that are more suitable for the market. 【0385】 The following describes the processing flow. 【0386】 Step 1: 【0387】 The server receives user data and market trend data from various data collection points. Specifically, this involves acquiring customer reviews, survey results, online market trend reports, etc., and integrating this data before storing it in a database. 【0388】 Step 2: 【0389】 The server uses a text feature extraction module to extract text features from the received data. Here, the TF-IDF (Term Frequency-Inverse Document Frequency) method is used to quantify characteristic words and phrases from each text data point. This process models the text content as a numerical vector. 【0390】 Step 3: 【0391】 The server performs clustering based on digitized vector data using clustering techniques. Algorithms such as K-means are applied to classify the data into multiple clusters based on similarity. This process clearly forms groups representing different customer needs and market trends. 【0392】 Step 4: 【0393】 The server visualizes the clustered data using a data visualization module. In this step, the dimensionality reduction method TSNE (t-Distributed Stochastic Neighbor Embedding) is used to reduce the high-dimensional cluster data to a two-dimensional space for visualization. The visualized data is plotted as a color-coded point cloud, illustrating the distribution and characteristics of each cluster. 【0394】 Step 5: 【0395】 Users gain strategic insights into product design based on visualized cluster data. Specifically, by reviewing the visualization results and considering areas for product improvement and new design suggestions, product development that aligns with customer needs and market trends is facilitated. 【0396】 (Example 1) 【0397】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0398】 Modern product development requires accurately understanding consumer needs and market trends, and designing products quickly and effectively based on that understanding. However, efficiently collecting user and market trend information, and effectively analyzing and utilizing that data, is difficult. There is a need for effective methods to overcome this challenge and develop products that are better suited to the market. 【0399】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0400】 In this invention, the server includes information gathering means for acquiring user information and market trend information, text feature extraction means for converting the above information into numerical format, and clustering means for classifying the information based on the numerical information. This enables efficient analysis of the collected information and the provision of insights for product design based on that analysis. 【0401】 "User information" refers to data and opinions about potential and existing customers of a product or service. 【0402】 "Market trend information" refers to information about current market trends, consumer preferences, major competitive activities, etc. 【0403】 "Information gathering methods" refer to processes and systems that aggregate data from diverse sources. 【0404】 "Text feature extraction means" refers to a technology that identifies important elements and features from documents and text data and converts them into numerical data. 【0405】 "Clustering methods" refer to techniques and technologies for grouping data based on similarity. 【0406】 "Information visualization methods" refer to technologies and tools that display complex data and information in an easy-to-understand manner using graphs and diagrams. 【0407】 "Machine learning technology" refers to automated computational methods that find patterns based on data and perform predictions and classifications. 【0408】 "Two-dimensional space" refers to a coordinate system used to visually arrange data on a plane consisting of a horizontal axis and a vertical axis. 【0409】 An "interface" refers to the on-screen display elements and operating environment that allow a user to interact with a computer system or software. 【0410】 This invention is a system that aggregates user information and market trend information, analyzes them, and visualizes them, with the server at its core. The server first acquires user information and market trend information in real time from various data sources via APIs. This information collection process can utilize digital devices, and data scraping techniques are also employed as needed. 【0411】 After collecting the information, the server uses text feature extraction to extract important keywords and phrases and converts them into numerical data. Natural language processing libraries such as NLTK and SpaCy are used here. The resulting numerical data is then used in the next data analysis step. 【0412】 Using clustering techniques, the server classifies data using the K-means method, a machine learning technique. The data is divided into groups with similar characteristics, forming clusters. This process makes it possible to understand patterns and trends in the data. 【0413】 Finally, the resulting clusters are mapped into a two-dimensional space using data visualization tools. Libraries such as Matplotlib and Seaborn are used for this visualization. These tools allow users to visually check the distribution of the data and explore the details through the interface. 【0414】 For example, if a user is requesting a new product design, this system can be used to analyze customer requirements such as "intuitive operation" and "innovative design." A possible prompt might include instructions like, "Please propose a new product design using intuitive operation and sustainable materials." Implementing such a system allows users to streamline the design development process and quickly develop market-ready products. 【0415】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0416】 Step 1: 【0417】 The server collects user information and market trend information via APIs. This information gathering process retrieves data in digital format from online feedback forms and survey data. Input includes raw text and numerical data. The server organizes this data, removing redundancy and noise. The output is stored in a database as a structured dataset. 【0418】 Step 2: 【0419】 The server uses a text feature extraction module to extract important keywords and phrases from stored data and converts them into numerical vectors. Text data is used as input. The server utilizes natural language processing software to extract important features from the text through morphological analysis and TF-IDF calculations. The output is numerical data from which the features have been extracted. 【0420】 Step 3: 【0421】 The server uses the K-means method, a machine learning technique, to cluster digitized data. The input is digitized vectorized feature data. The server groups data with similar characteristics according to the number of clusters and initial settings. The output is a dataset classified into multiple clusters. 【0422】 Step 4: 【0423】 The server uses a visualization library to map the clustering results onto a two-dimensional plane and perform visualization. The clustered dataset is used as input. The server plots each cluster with a different color and shape, generating a graph that allows for easy visualization of the data distribution. The output is a graph that visually represents the structure and patterns of the data. 【0424】 Step 5: 【0425】 Users gain insights into product development based on the generated graphs. Visualized data is presented as input. Users can select specific clusters and view detailed information using mouse operations. The output provides decision-making support information regarding product design and market strategy. 【0426】 (Application Example 1) 【0427】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0428】 In contemporary urban design projects, a challenge is to provide efficient design proposals that reflect the needs of citizens and societal trends. Traditional methods required significant time and effort to collect and analyze vast amounts of information, and limited visual means of understanding these trends made it difficult for designers to provide optimal solutions. 【0429】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0430】 In this invention, the server includes an information gathering means for receiving user information and social trend information, a text feature extraction means for quantifying the above information, a group classification means for grouping information based on the quantified information, an information visualization means for visualizing the grouping results, and a design proposal generation means for generating design proposals related to urban structure. This makes it possible for urban designers and stakeholders to efficiently generate and visually grasp design proposals that reflect the latest social trends. 【0431】 "User information" refers to data collected from individual users, representing their needs and preferences. 【0432】 "Social trend information" refers to data that shows the latest trends and fashions in the market and society as a whole. 【0433】 "Information gathering means" refers to a device or system that has the function of acquiring and integrating user information and social trend information. 【0434】 A "text feature extraction method" is a technology or module for extracting important characteristics and patterns from collected information and quantifying them. 【0435】 A "group classification method" is a method or algorithm for classifying and organizing information based on similarity, using quantified information. 【0436】 "Information visualization methods" are tools and technologies that visually display grouped information, enabling an intuitive understanding of the trends and arrangement of that information. 【0437】 A "design proposal generation method" is a process or system for generating design proposals for new urban structures or projects based on visualized information. 【0438】 This system efficiently collects and analyzes user information and social trend information to generate urban structure design proposals. The server integrates user information and social trend information using information collection methods. At this stage, data is transmitted from sensing devices and mobile terminals on the network. 【0439】 Next, the server's text feature extraction mechanism analyzes the collected information and quantifies important characteristics. It converts the text data into feature vectors using natural language processing libraries such as Python's NLTK or scikit-learn. Then, it applies an automated learning algorithm such as the K-means method as a group classification mechanism to classify the quantified data based on similarity. 【0440】 In information visualization methods, clustered data is displayed on a two-dimensional plane using visualization tools such as Matplotlib and Plotly. This allows urban planners and stakeholders to visually grasp the current situation and trends. 【0441】 Furthermore, the design proposal generation method generates new urban structure design proposals based on visualized data. This process utilizes a generation AI model to create sophisticated design proposals, which are then provided to stakeholders as concrete proposals. 【0442】 As a concrete example, when designing a new park in the city center, we can obtain user requests such as "design in harmony with nature" and analyze trends in "sustainable energy use" from social trend information. Based on this information, it is possible to generate and visually present park design proposals. 【0443】 Examples of prompts include "Propose an innovative transportation system design for an urban development project" or "Visualize the features of a next-generation smart park based on customer needs." By entering these prompts, you can obtain useful design proposals. 【0444】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0445】 Step 1: 【0446】 The server uses information gathering tools to collect user information and social trend information via the network. Inputs include feedback data from users' mobile devices and market trend data from online databases. This information is stored in the database in its raw form. 【0447】 Step 2: 【0448】 The server's text feature extraction mechanism analyzes the information stored in the database. The input includes user information and social trend information in text format. The server uses the Python NLTK library to extract important keywords and convert them into numerical feature vectors. At this stage, the information is output in vector format. 【0449】 Step 3: 【0450】 The server clusters the digitized information using a group classification method. The input is the feature vectors generated in step 2. The server classifies these vectors into groups based on similarity using the K-means algorithm. As a result of this process, the information is output as clustered groups. 【0451】 Step 4: 【0452】 The server's information visualization method visualizes the clustering results. The input includes the clustering results generated in step 3. The server uses Matplotlib or Plotly to plot the groups in a two-dimensional space, visually showing the arrangement of the information. At this stage, the visualized data is output. 【0453】 Step 5: 【0454】 The server's design proposal generation mechanism generates design proposals based on visualized data. Inputs include visualization results and prompts for the generating AI model. The server uses the AI ​​model to create new urban design proposals that reflect user needs and social trends, and provides the results as output. 【0455】 In each step, the server's role is multifaceted, ranging from data collection and analysis to visualization and design generation, efficiently supporting urban structure design. 【0456】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0457】 This invention is a system that collects user data and market trend data, quantifies and clusters this data, and further combines it with an emotion engine that recognizes user emotions. The system aims to optimize design proposals while taking the influence of emotions into consideration. 【0458】 The server first acquires user data and market trend data using various data collection methods. This acquired data includes customer reviews, survey results, and social media posts. The data is then passed to a text feature extraction module, where it is digitized. This digitized data is used to capture the key points and characteristics of the information. 【0459】 Next, the server uses a clustering mechanism to perform data clustering based on the digitized data. The clustering algorithm utilizes machine learning to classify the data into multiple clusters based on similarity. This allows for the organization of different customer needs and market trends. 【0460】 In addition, the server uses an emotion engine to analyze the user's emotional data. The emotion engine utilizes natural language processing techniques to calculate emotional values ​​from text data and determine whether the emotional state is positive or negative. This emotional information becomes a crucial factor influencing design proposals. 【0461】 Finally, the server visualizes the clustering and sentiment analysis results using data visualization tools. The visualized results are displayed through an interface in a two-dimensional space. This display provides detailed analysis results useful for design consideration, enabling users to gain concrete insights for improving new and existing products. 【0462】 As a concrete example, when a user is determining the direction of a new product design, initial customer reactions to design proposals are evaluated through sentiment analysis. By incorporating elements that evoke strong positive emotions, development that contributes to improved customer satisfaction becomes possible. Through this implementation, users can refine their product strategy based on emotional information and quickly deliver products that are suitable for the market. 【0463】 The following describes the processing flow. 【0464】 Step 1: 【0465】 The server collects user data and market trend data from various data sources. This data includes online reviews, user surveys, and social media posts. This data is stored in a database that is updated in real time or periodically. 【0466】 Step 2: 【0467】 The server analyzes the collected user data using natural language processing tools and quantifies the text data. Specifically, it uses TF-IDF (Term Frequency-Inverse Document Frequency) and word embedding techniques to extract and quantify important features from each text. This quantification is useful for subsequent analysis and helps to capture the characteristics of the data. 【0468】 Step 3: 【0469】 The server performs clustering using the numerical data generated in the previous step. Clustering uses machine learning techniques such as the K-means algorithm to classify the data into multiple clusters. This process clarifies patterns in customer needs and market trends. 【0470】 Step 4: 【0471】 The server uses an emotion engine to recognize the user's emotions. The emotion engine calculates an emotion score from text data and determines whether the user's emotions are positive or negative. Based on the emotion analysis results, it suggests which design elements are desirable or need improvement. 【0472】 Step 5: 【0473】 The server combines clustering results and sentiment analysis results to visualize the data. The visualization module plots these results in a two-dimensional space and presents them to the user through an interface. The visualized information helps users intuitively understand the overall picture and key elements of the data. 【0474】 Step 6: 【0475】 Users refer to visualized information and proceed through a process of determining the direction of product design. Users can highlight positive elements indicated by sentiment analysis and make revisions based on negative feedback. This provides valuable insights for design improvements and new product planning. 【0476】 (Example 2) 【0477】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0478】 Conventional data analysis systems have the problem of difficulty in optimizing design proposals that accurately reflect user sentiment and market trends. Furthermore, there are challenges in efficiently obtaining the insights users need through data classification and visualization. 【0479】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0480】 In this invention, the server includes an information gathering means for receiving user information and market trend information, a feature extraction means for quantifying the above information, a classification means for classifying the information based on the quantified information, and an emotion recognition means for recognizing the user's emotional state. This makes it possible to optimize effective design proposals based on the user's emotions and market trends. 【0481】 "User information" refers to data that includes the preferences, behavioral patterns, and emotional states of an individual or group. 【0482】 "Market trend information" refers to data on consumer purchasing behavior and market trends in a specific time and region. 【0483】 "Information gathering means" refers to the technologies and methods used to acquire user information and market trend information. 【0484】 "Feature extraction methods" refer to technologies that identify useful characteristics and patterns from raw data and perform the process of quantifying them. 【0485】 "Classification method" refers to a technique that performs the process of grouping data based on similar characteristics. 【0486】 "Emotion recognition means" refers to technology that identifies and efficiently determines a user's emotional state from text and other data. 【0487】 "Visualization methods" refer to technologies that visually represent data analysis and classification results, enabling users to intuitively understand the information. 【0488】 This invention is a system that optimizes design proposals based on user information and market trend information. The server first acquires user information and market trend information using information gathering means. In this process, web scraping technology and APIs are used to collect data such as customer reviews, social media posts, and survey results. 【0489】 Next, the server quantifies the information collected through the feature extraction means. This step involves text feature extraction using natural language processing techniques, and vectorizes the text using algorithms such as TF-IDF and Word2Vec. 【0490】 Next, the server groups the quantified information using classification methods. By applying machine learning algorithms such as K-means and hierarchical clustering to classify the information into multiple groups, it grasps the diverse needs of users and market trends. 【0491】 Furthermore, the server analyzes the user's emotional state using emotion recognition technology. From the collected text data, a positive or negative emotion score is derived using natural language processing techniques. This information has a significant impact when making design proposals. 【0492】 Finally, the server visualizes the analysis results using visualization tools. The data is visualized in a two-dimensional space and provided as an interface via the user's terminal. This generated visualization becomes a powerful support tool for users when considering their designs. 【0493】 As a concrete example, when a user is considering the design of a new product, they can receive suggestions from a generating AI model that incorporate elements that evoke positive emotions. An example of a prompt would be, "Please tell me the design elements that will get positive feedback from customers." 【0494】 This allows users to quickly receive compelling design suggestions based on emotional information and market trends, enabling them to effectively refine their product strategies. 【0495】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0496】 Step 1: 【0497】 The server collects user information and market trend information as input data using information gathering methods. In this process, it uses web scraping technology and APIs to obtain information from customer reviews, social media posts, etc. The output is raw data to be used in subsequent processing steps. Specifically, for example, it aggregates 1000 social media posts obtained via API. 【0498】 Step 2: 【0499】 The server takes raw data collected using feature extraction methods as input and converts the text data into numerical data. This involves applying TF-IDF or Word2Vec to convert the text data into numerical vectors. The output of this step is a numerical dataset. Specifically, the review "The product quality is high" is converted into a numerical vector. 【0500】 Step 3: 【0501】 The server utilizes classification methods to perform clustering on a quantified dataset as input. It groups the data using machine learning algorithms such as K-means, and the output is multiple data clusters. This allows for the organization of different customer needs and market trends. For example, data can be segmented into groups such as women in their 20s and men in their 30s. 【0502】 Step 4: 【0503】 The server uses emotion recognition technology to perform sentiment analysis on each clustered data cluster as input. Using natural language processing techniques, it outputs positive or negative sentiment scores from the text. Specifically, emotions like "happy" are represented by high scores, providing data useful for product improvement. 【0504】 Step 5: 【0505】 The server uses visualization tools to visualize the results of sentiment analysis and clustering as input. The data is plotted in a two-dimensional space and output as a diagram to the user's terminal. This visualization is used to consider design proposals. Specifically, the clustering results using PCA are displayed as a color-coded chart. 【0506】 Step 6: 【0507】 The user receives design suggestions using an AI model that generates data based on visualized data. The prompt is in the format of "Please tell me the design elements that will get positive feedback from customers," and the AI ​​model outputs suggested design improvements. This allows the user to confirm the direction of specific design changes. 【0508】 (Application Example 2) 【0509】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0510】 In today's advertising industry, providing optimal advertising tailored to the emotions of individual users is challenging. Furthermore, there is a need for methods to efficiently collect vast amounts of customer and market trend information, analyze emotions based on this data, and optimize advertising content. This invention aims to solve this problem. 【0511】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0512】 In this invention, the server includes an information acquisition means for receiving customer information and market trend information, an information feature analysis means for quantifying the above information, and a classification means for classifying the information based on the quantified information. This makes it possible to provide optimal advertisements that respond to the user's emotions. 【0513】 "Customer information" refers to information about users' attributes and activity history that is obtained in order to optimize advertising. 【0514】 "Market trend information" refers to information that shows consumer behavior and trends in the current market. 【0515】 "Information acquisition means" refers to methods or devices for collecting customer information and market trend information. 【0516】 "Information feature analysis means" refers to a method or apparatus for performing the process of quantifying collected information. 【0517】 A "classification tool" is a method or apparatus for analyzing digitized information and classifying it into specific categories or clusters. 【0518】 "Information visualization means" refers to a method or device for visually displaying classified information. 【0519】 "Emotional analysis advertising optimization means" refers to a method or apparatus for analyzing a user's emotions and adjusting advertising content based on the results. 【0520】 In an embodiment of this invention, a server first receives customer information and market trend information. This information may be collected from terminals such as smartphones and personal computers. The collected information is then organized by an information acquisition means. 【0521】 Next, an information feature analysis tool quantifies this information. The quantified data is then analyzed using a natural language processing library (e.g., spaCy or Hugging Face Transformers). This detects the important features of each data point and prepares them for use by subsequent classification tools. 【0522】 The classification method uses machine learning techniques (e.g., K-means, DBSCAN) to classify this quantified information into multiple categories. This helps to organize different user groups and market trends. 【0523】 Furthermore, the sentiment analysis ad optimization method uses sentiment analysis APIs (e.g., IBM Watson, Google Cloud Natural Language) to analyze the user's emotions. Based on this analysis, ad content is optimized in real time to provide the most relevant ads to the user. The ads are displayed through the interface of smartphones and smart glasses. 【0524】 For example, if a user frequently posts positive comments about a particular topic on social media, ads for products related to that topic will be displayed preferentially. 【0525】 An example of a prompt message from this system is: "Perform a sentiment analysis on topics the user has recently been interested in, and generate three relevant ad ideas." 【0526】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0527】 Step 1: 【0528】 The server receives customer information and market trend information from the terminal. This information includes user attribute data, social media posting history, and online purchase history. The server organizes this information using information acquisition methods and prepares it for subsequent processing. The input is raw data, and the output is organized customer information. 【0529】 Step 2: 【0530】 The server quantifies the information it receives using information feature analysis tools. It uses a natural language processing library to analyze text data and extract numerical features. This process converts text features such as word frequency and sentiment scores into numerical data. The input is organized customer information, and the output is quantified data. 【0531】 Step 3: 【0532】 The server uses a classification method to quantify data and then categorizes it using machine learning algorithms. It employs the K-means method to group data with similar characteristics. This allows for categorization tailored to different customer segments and market trends. The input is quantified data, and the output is categorized information. 【0533】 Step 4: 【0534】 The server analyzes the sentiment of categorized information using sentiment analysis advertising optimization methods. It calls a sentiment analysis API to understand the user's emotional state. The input is categorized information, and the output is the analysis results, including sentiment scores. 【0535】 Step 5: 【0536】 The server generates ad content based on sentiment scores and displays it on the device. It utilizes a generative AI model to create ad suggestions most relevant to the sentiment score. Prompts are used to support the ad generation process. Ultimately, the ad is displayed on the user's smartphone or smart glasses. The input is the analysis results, including sentiment scores, and the output is the optimized ad content. 【0537】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0538】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0539】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0540】 [Fourth Embodiment] 【0541】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0542】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0543】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0544】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0545】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0546】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0547】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0548】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0549】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0550】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0551】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0552】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0553】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0554】 As a configuration for carrying out this invention, the AI ​​design system includes a process of collecting user data and market trend data, quantifying this data, and performing clustering. 【0555】 The server first acquires user data and market trend data from their respective data collection devices and stores them in a database. This data includes customer feedback and information on market trends. Next, the server uses a text feature extraction module to extract important keywords and phrases from the data and digitize them. This digitized data is treated as a vector that reflects the text's features. 【0556】 Next, the server executes a clustering algorithm to group the data based on its quantified representation. Machine learning algorithms such as K-means are used as clustering methods to classify the data into multiple clusters. This allows for the grouping of information with similar characteristics, making it possible to understand data patterns. 【0557】 Finally, the data visualization module is used to visually display the clustered data. This visualization maps the clustering results onto a two-dimensional plane, intuitively showing the cluster structure and data distribution as a graph. This allows users to clearly understand product design trends and customer requirements, and to gain insights for designing new products or improving existing ones. 【0558】 For example, if a user wants design proposals for a new product category, the system collects data such as "intuitive operation" and "innovative design" as customer requirements, and analyzes market trends such as "use of sustainable materials" and "integration of smart technology." Based on this data, the system clusters the optimal design proposals and visualizes them as graphs, generating design suggestions that align with customer needs and market trends. This implementation allows users to efficiently advance the design process and develop products that are more suitable for the market. 【0559】 The following describes the processing flow. 【0560】 Step 1: 【0561】 The server receives user data and market trend data from various data collection points. Specifically, this involves acquiring customer reviews, survey results, online market trend reports, etc., and integrating this data before storing it in a database. 【0562】 Step 2: 【0563】 The server uses a text feature extraction module to extract text features from the received data. Here, the TF-IDF (Term Frequency-Inverse Document Frequency) method is used to quantify characteristic words and phrases from each text data point. This process models the text content as a numerical vector. 【0564】 Step 3: 【0565】 The server performs clustering based on digitized vector data using clustering techniques. Algorithms such as K-means are applied to classify the data into multiple clusters based on similarity. This process clearly forms groups representing different customer needs and market trends. 【0566】 Step 4: 【0567】 The server visualizes the clustered data using a data visualization module. In this step, the dimensionality reduction method TSNE (t-Distributed Stochastic Neighbor Embedding) is used to reduce the high-dimensional cluster data to a two-dimensional space for visualization. The visualized data is plotted as a color-coded point cloud, illustrating the distribution and characteristics of each cluster. 【0568】 Step 5: 【0569】 Users gain strategic insights into product design based on visualized cluster data. Specifically, by reviewing the visualization results and considering areas for product improvement and new design suggestions, product development that aligns with customer needs and market trends is facilitated. 【0570】 (Example 1) 【0571】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0572】 Modern product development requires accurately understanding consumer needs and market trends, and designing products quickly and effectively based on that understanding. However, efficiently collecting user and market trend information, and effectively analyzing and utilizing that data, is difficult. There is a need for effective methods to overcome this challenge and develop products that are better suited to the market. 【0573】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0574】 In this invention, the server includes information gathering means for acquiring user information and market trend information, text feature extraction means for converting the above information into numerical format, and clustering means for classifying the information based on the numerical information. This enables efficient analysis of the collected information and the provision of insights for product design based on that analysis. 【0575】 "User information" refers to data and opinions about potential and existing customers of a product or service. 【0576】 "Market trend information" refers to information about current market trends, consumer preferences, major competitive activities, etc. 【0577】 "Information gathering methods" refer to processes and systems that aggregate data from diverse sources. 【0578】 "Text feature extraction means" refers to a technology that identifies important elements and features from documents and text data and converts them into numerical data. 【0579】 "Clustering methods" refer to techniques and technologies for grouping data based on similarity. 【0580】 "Information visualization methods" refer to technologies and tools that display complex data and information in an easy-to-understand manner using graphs and diagrams. 【0581】 "Machine learning technology" refers to automated computational methods that find patterns based on data and perform predictions and classifications. 【0582】 "Two-dimensional space" refers to a coordinate system used to visually arrange data on a plane consisting of a horizontal axis and a vertical axis. 【0583】 An "interface" refers to the on-screen display elements and operating environment that allow a user to interact with a computer system or software. 【0584】 This invention is a system that aggregates user information and market trend information, analyzes them, and visualizes them, with the server at its core. The server first acquires user information and market trend information in real time from various data sources via APIs. This information collection process can utilize digital devices, and data scraping techniques are also employed as needed. 【0585】 After collecting the information, the server uses text feature extraction to extract important keywords and phrases and converts them into numerical data. Natural language processing libraries such as NLTK and SpaCy are used here. The resulting numerical data is then used in the next data analysis step. 【0586】 Using clustering techniques, the server classifies data using the K-means method, a machine learning technique. The data is divided into groups with similar characteristics, forming clusters. This process makes it possible to understand patterns and trends in the data. 【0587】 Finally, the resulting clusters are mapped into a two-dimensional space using data visualization tools. Libraries such as Matplotlib and Seaborn are used for this visualization. These tools allow users to visually check the distribution of the data and explore the details through the interface. 【0588】 For example, if a user is requesting a new product design, this system can be used to analyze customer requirements such as "intuitive operation" and "innovative design." A possible prompt might include instructions like, "Please propose a new product design using intuitive operation and sustainable materials." Implementing such a system allows users to streamline the design development process and quickly develop market-ready products. 【0589】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0590】 Step 1: 【0591】 The server collects user information and market trend information via APIs. This information gathering process retrieves data in digital format from online feedback forms and survey data. Input includes raw text and numerical data. The server organizes this data, removing redundancy and noise. The output is stored in a database as a structured dataset. 【0592】 Step 2: 【0593】 The server uses a text feature extraction module to extract important keywords and phrases from stored data and converts them into numerical vectors. Text data is used as input. The server utilizes natural language processing software to extract important features from the text through morphological analysis and TF-IDF calculations. The output is numerical data from which the features have been extracted. 【0594】 Step 3: 【0595】 The server uses the K-means method, a machine learning technique, to cluster digitized data. The input is digitized vectorized feature data. The server groups data with similar characteristics according to the number of clusters and initial settings. The output is a dataset classified into multiple clusters. 【0596】 Step 4: 【0597】 The server uses a visualization library to map the clustering results onto a two-dimensional plane and perform visualization. The clustered dataset is used as input. The server plots each cluster with a different color and shape, generating a graph that allows for easy visualization of the data distribution. The output is a graph that visually represents the structure and patterns of the data. 【0598】 Step 5: 【0599】 Users gain insights into product development based on the generated graphs. Visualized data is presented as input. Users can select specific clusters and view detailed information using mouse operations. The output provides decision-making support information regarding product design and market strategy. 【0600】 (Application Example 1) 【0601】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0602】 In contemporary urban design projects, a challenge is to provide efficient design proposals that reflect the needs of citizens and societal trends. Traditional methods required significant time and effort to collect and analyze vast amounts of information, and limited visual means of understanding these trends made it difficult for designers to provide optimal solutions. 【0603】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0604】 In this invention, the server includes an information gathering means for receiving user information and social trend information, a text feature extraction means for quantifying the above information, a group classification means for grouping information based on the quantified information, an information visualization means for visualizing the grouping results, and a design proposal generation means for generating design proposals related to urban structure. This makes it possible for urban designers and stakeholders to efficiently generate and visually grasp design proposals that reflect the latest social trends. 【0605】 "User information" refers to data collected from individual users, representing their needs and preferences. 【0606】 "Social trend information" refers to data that shows the latest trends and fashions in the market and society as a whole. 【0607】 "Information gathering means" refers to a device or system that has the function of acquiring and integrating user information and social trend information. 【0608】 A "text feature extraction method" is a technology or module for extracting important characteristics and patterns from collected information and quantifying them. 【0609】 A "group classification method" is a method or algorithm for classifying and organizing information based on similarity, using quantified information. 【0610】 "Information visualization methods" are tools and technologies that visually display grouped information, enabling an intuitive understanding of the trends and arrangement of that information. 【0611】 A "design proposal generation method" is a process or system for generating design proposals for new urban structures or projects based on visualized information. 【0612】 This system efficiently collects and analyzes user information and social trend information to generate urban structure design proposals. The server integrates user information and social trend information using information collection methods. At this stage, data is transmitted from sensing devices and mobile terminals on the network. 【0613】 Next, the server's text feature extraction mechanism analyzes the collected information and quantifies important characteristics. It converts the text data into feature vectors using natural language processing libraries such as Python's NLTK or scikit-learn. Then, it applies an automated learning algorithm such as the K-means method as a group classification mechanism to classify the quantified data based on similarity. 【0614】 In information visualization methods, clustered data is displayed on a two-dimensional plane using visualization tools such as Matplotlib and Plotly. This allows urban planners and stakeholders to visually grasp the current situation and trends. 【0615】 Furthermore, the design proposal generation method generates new urban structure design proposals based on visualized data. This process utilizes a generation AI model to create sophisticated design proposals, which are then provided to stakeholders as concrete proposals. 【0616】 As a concrete example, when designing a new park in the city center, we can obtain user requests such as "design in harmony with nature" and analyze trends in "sustainable energy use" from social trend information. Based on this information, it is possible to generate and visually present park design proposals. 【0617】 Examples of prompts include "Propose an innovative transportation system design for an urban development project" or "Visualize the features of a next-generation smart park based on customer needs." By entering these prompts, you can obtain useful design proposals. 【0618】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0619】 Step 1: 【0620】 The server uses information gathering tools to collect user information and social trend information via the network. Inputs include feedback data from users' mobile devices and market trend data from online databases. This information is stored in the database in its raw form. 【0621】 Step 2: 【0622】 The server's text feature extraction mechanism analyzes the information stored in the database. The input includes user information and social trend information in text format. The server uses the Python NLTK library to extract important keywords and convert them into numerical feature vectors. At this stage, the information is output in vector format. 【0623】 Step 3: 【0624】 The server clusters the digitized information using a group classification method. The input is the feature vectors generated in step 2. The server classifies these vectors into groups based on similarity using the K-means algorithm. As a result of this process, the information is output as clustered groups. 【0625】 Step 4: 【0626】 The server's information visualization method visualizes the clustering results. The input includes the clustering results generated in step 3. The server uses Matplotlib or Plotly to plot the groups in a two-dimensional space, visually showing the arrangement of the information. At this stage, the visualized data is output. 【0627】 Step 5: 【0628】 The server's design proposal generation mechanism generates design proposals based on visualized data. Inputs include visualization results and prompts for the generating AI model. The server uses the AI ​​model to create new urban design proposals that reflect user needs and social trends, and provides the results as output. 【0629】 In each step, the server's role is multifaceted, ranging from data collection and analysis to visualization and design generation, efficiently supporting urban structure design. 【0630】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0631】 This invention is a system that collects user data and market trend data, quantifies and clusters this data, and further combines it with an emotion engine that recognizes user emotions. The system aims to optimize design proposals while taking the influence of emotions into consideration. 【0632】 The server first acquires user data and market trend data using various data collection methods. This acquired data includes customer reviews, survey results, and social media posts. The data is then passed to a text feature extraction module, where it is digitized. This digitized data is used to capture the key points and characteristics of the information. 【0633】 Next, the server uses a clustering mechanism to perform data clustering based on the digitized data. The clustering algorithm utilizes machine learning to classify the data into multiple clusters based on similarity. This allows for the organization of different customer needs and market trends. 【0634】 In addition, the server uses an emotion engine to analyze the user's emotional data. The emotion engine utilizes natural language processing techniques to calculate emotional values ​​from text data and determine whether the emotional state is positive or negative. This emotional information becomes a crucial factor influencing design proposals. 【0635】 Finally, the server visualizes the clustering and sentiment analysis results using data visualization tools. The visualized results are displayed through an interface in a two-dimensional space. This display provides detailed analysis results useful for design consideration, enabling users to gain concrete insights for improving new and existing products. 【0636】 As a concrete example, when a user is determining the direction of a new product design, initial customer reactions to design proposals are evaluated through sentiment analysis. By incorporating elements that evoke strong positive emotions, development that contributes to improved customer satisfaction becomes possible. Through this implementation, users can refine their product strategy based on emotional information and quickly deliver products that are suitable for the market. 【0637】 The following describes the processing flow. 【0638】 Step 1: 【0639】 The server collects user data and market trend data from various data sources. This data includes online reviews, user surveys, and social media posts. This data is stored in a database that is updated in real time or periodically. 【0640】 Step 2: 【0641】 The server analyzes the collected user data using natural language processing tools and quantifies the text data. Specifically, it uses TF-IDF (Term Frequency-Inverse Document Frequency) and word embedding techniques to extract and quantify important features from each text. This quantification is useful for subsequent analysis and helps to capture the characteristics of the data. 【0642】 Step 3: 【0643】 The server performs clustering using the numerical data generated in the previous step. Clustering uses machine learning techniques such as the K-means algorithm to classify the data into multiple clusters. This process clarifies patterns in customer needs and market trends. 【0644】 Step 4: 【0645】 The server uses an emotion engine to recognize the user's emotions. The emotion engine calculates an emotion score from text data and determines whether the user's emotions are positive or negative. Based on the emotion analysis results, it suggests which design elements are desirable or need improvement. 【0646】 Step 5: 【0647】 The server combines clustering results and sentiment analysis results to visualize the data. The visualization module plots these results in a two-dimensional space and presents them to the user through an interface. The visualized information helps users intuitively understand the overall picture and key elements of the data. 【0648】 Step 6: 【0649】 Users refer to visualized information and proceed through a process of determining the direction of product design. Users can highlight positive elements indicated by sentiment analysis and make revisions based on negative feedback. This provides valuable insights for design improvements and new product planning. 【0650】 (Example 2) 【0651】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0652】 Conventional data analysis systems have the problem of difficulty in optimizing design proposals that accurately reflect user sentiment and market trends. Furthermore, there are challenges in efficiently obtaining the insights users need through data classification and visualization. 【0653】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0654】 In this invention, the server includes an information gathering means for receiving user information and market trend information, a feature extraction means for quantifying the above information, a classification means for classifying the information based on the quantified information, and an emotion recognition means for recognizing the user's emotional state. This makes it possible to optimize effective design proposals based on the user's emotions and market trends. 【0655】 "User information" refers to data that includes the preferences, behavioral patterns, and emotional states of an individual or group. 【0656】 "Market trend information" refers to data on consumer purchasing behavior and market trends in a specific time and region. 【0657】 "Information gathering means" refers to the technologies and methods used to acquire user information and market trend information. 【0658】 "Feature extraction methods" refer to technologies that identify useful characteristics and patterns from raw data and perform the process of quantifying them. 【0659】 "Classification method" refers to a technique that performs the process of grouping data based on similar characteristics. 【0660】 "Emotion recognition means" refers to technology that identifies and efficiently determines a user's emotional state from text and other data. 【0661】 "Visualization methods" refer to technologies that visually represent data analysis and classification results, enabling users to intuitively understand the information. 【0662】 This invention is a system that optimizes design proposals based on user information and market trend information. The server first acquires user information and market trend information using information gathering means. In this process, web scraping technology and APIs are used to collect data such as customer reviews, social media posts, and survey results. 【0663】 Next, the server quantifies the information collected through the feature extraction means. This step involves text feature extraction using natural language processing techniques, and vectorizes the text using algorithms such as TF-IDF and Word2Vec. 【0664】 Next, the server groups the quantified information using classification methods. By applying machine learning algorithms such as K-means and hierarchical clustering to classify the information into multiple groups, it grasps the diverse needs of users and market trends. 【0665】 Furthermore, the server analyzes the user's emotional state using emotion recognition technology. From the collected text data, a positive or negative emotion score is derived using natural language processing techniques. This information has a significant impact when making design proposals. 【0666】 Finally, the server visualizes the analysis results using visualization tools. The data is visualized in a two-dimensional space and provided as an interface via the user's terminal. This generated visualization becomes a powerful support tool for users when considering their designs. 【0667】 As a concrete example, when a user is considering the design of a new product, they can receive suggestions from a generating AI model that incorporate elements that evoke positive emotions. An example of a prompt would be, "Please tell me the design elements that will get positive feedback from customers." 【0668】 This allows users to quickly receive compelling design suggestions based on emotional information and market trends, enabling them to effectively refine their product strategies. 【0669】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0670】 Step 1: 【0671】 The server collects user information and market trend information as input data using information gathering methods. In this process, it uses web scraping technology and APIs to obtain information from customer reviews, social media posts, etc. The output is raw data to be used in subsequent processing steps. Specifically, for example, it aggregates 1000 social media posts obtained via API. 【0672】 Step 2: 【0673】 The server takes raw data collected using feature extraction methods as input and converts the text data into numerical data. This involves applying TF-IDF or Word2Vec to convert the text data into numerical vectors. The output of this step is a numerical dataset. Specifically, the review "The product quality is high" is converted into a numerical vector. 【0674】 Step 3: 【0675】 The server utilizes classification methods to perform clustering on a quantified dataset as input. It groups the data using machine learning algorithms such as K-means, and the output is multiple data clusters. This allows for the organization of different customer needs and market trends. For example, data can be segmented into groups such as women in their 20s and men in their 30s. 【0676】 Step 4: 【0677】 The server uses emotion recognition technology to perform sentiment analysis on each clustered data cluster as input. Using natural language processing techniques, it outputs positive or negative sentiment scores from the text. Specifically, emotions like "happy" are represented by high scores, providing data useful for product improvement. 【0678】 Step 5: 【0679】 The server uses visualization tools to visualize the results of sentiment analysis and clustering as input. The data is plotted in a two-dimensional space and output as a diagram to the user's terminal. This visualization is used to consider design proposals. Specifically, the clustering results using PCA are displayed as a color-coded chart. 【0680】 Step 6: 【0681】 The user receives design suggestions using an AI model that generates data based on visualized data. The prompt is in the format of "Please tell me the design elements that will get positive feedback from customers," and the AI ​​model outputs suggested design improvements. This allows the user to confirm the direction of specific design changes. 【0682】 (Application Example 2) 【0683】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0684】 In today's advertising industry, providing optimal advertising tailored to the emotions of individual users is challenging. Furthermore, there is a need for methods to efficiently collect vast amounts of customer and market trend information, analyze emotions based on this data, and optimize advertising content. This invention aims to solve this problem. 【0685】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0686】 In this invention, the server includes an information acquisition means for receiving customer information and market trend information, an information feature analysis means for quantifying the above information, and a classification means for classifying the information based on the quantified information. This makes it possible to provide optimal advertisements that respond to the user's emotions. 【0687】 "Customer information" refers to information about users' attributes and activity history that is obtained in order to optimize advertising. 【0688】 "Market trend information" refers to information that shows consumer behavior and trends in the current market. 【0689】 "Information acquisition means" refers to methods or devices for collecting customer information and market trend information. 【0690】 "Information feature analysis means" refers to a method or apparatus for performing the process of quantifying collected information. 【0691】 A "classification tool" is a method or apparatus for analyzing digitized information and classifying it into specific categories or clusters. 【0692】 "Information visualization means" refers to a method or device for visually displaying classified information. 【0693】 "Emotional analysis advertising optimization means" refers to a method or apparatus for analyzing a user's emotions and adjusting advertising content based on the results. 【0694】 In an embodiment of this invention, a server first receives customer information and market trend information. This information may be collected from terminals such as smartphones and personal computers. The collected information is then organized by an information acquisition means. 【0695】 Next, an information feature analysis tool quantifies this information. The quantified data is then analyzed using a natural language processing library (e.g., spaCy or Hugging Face Transformers). This detects the important features of each data point and prepares them for use by subsequent classification tools. 【0696】 The classification method uses machine learning techniques (e.g., K-means, DBSCAN) to classify this quantified information into multiple categories. This helps to organize different user groups and market trends. 【0697】 Furthermore, the sentiment analysis ad optimization method uses sentiment analysis APIs (e.g., IBM Watson, Google Cloud Natural Language) to analyze the user's emotions. Based on this analysis, ad content is optimized in real time to provide the most relevant ads to the user. The ads are displayed through the interface of smartphones and smart glasses. 【0698】 For example, if a user frequently posts positive comments about a particular topic on social media, ads for products related to that topic will be displayed preferentially. 【0699】 An example of a prompt message from this system is: "Perform a sentiment analysis on topics the user has recently been interested in, and generate three relevant ad ideas." 【0700】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0701】 Step 1: 【0702】 The server receives customer information and market trend information from the terminal. This information includes user attribute data, social media posting history, and online purchase history. The server organizes this information using information acquisition methods and prepares it for subsequent processing. The input is raw data, and the output is organized customer information. 【0703】 Step 2: 【0704】 The server quantifies the information it receives using information feature analysis tools. It uses a natural language processing library to analyze text data and extract numerical features. This process converts text features such as word frequency and sentiment scores into numerical data. The input is organized customer information, and the output is quantified data. 【0705】 Step 3: 【0706】 The server uses a classification method to quantify data and then categorizes it using machine learning algorithms. It employs the K-means method to group data with similar characteristics. This allows for categorization tailored to different customer segments and market trends. The input is quantified data, and the output is categorized information. 【0707】 Step 4: 【0708】 The server analyzes the sentiment of categorized information using sentiment analysis advertising optimization methods. It calls a sentiment analysis API to understand the user's emotional state. The input is categorized information, and the output is the analysis results, including sentiment scores. 【0709】 Step 5: 【0710】 The server generates ad content based on sentiment scores and displays it on the device. It utilizes a generative AI model to create ad suggestions most relevant to the sentiment score. Prompts are used to support the ad generation process. Ultimately, the ad is displayed on the user's smartphone or smart glasses. The input is the analysis results, including sentiment scores, and the output is the optimized ad content. 【0711】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0712】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0713】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0714】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0715】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0716】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0717】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0718】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0719】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0720】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0721】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0722】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0723】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0724】 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. 【0725】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0726】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0727】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0728】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0729】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0730】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0731】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0732】 The following is further disclosed regarding the embodiments described above. 【0733】 (Claim 1) 【0734】 A data collection method for receiving user data and market trend data, 【0735】 A text feature extraction method for quantifying the above data, 【0736】 A clustering method that clusters data based on digitized data, 【0737】 A data visualization method for visualizing clustering results, 【0738】 A system that includes this. 【0739】 (Claim 2) 【0740】 The system according to claim 1, wherein the clustering means uses a machine learning algorithm for classifying data into multiple clusters. 【0741】 (Claim 3) 【0742】 The system according to claim 1, wherein the data visualization means provides an interface that allows the distribution of data to be visually confirmed by displaying the clustering results in a two-dimensional space. 【0743】 "Example 1" 【0744】 (Claim 1) 【0745】 Information gathering means for acquiring user information and market trend information, 【0746】 A text feature extraction means for converting the above information into a numerical format, 【0747】 A clustering method that classifies information based on quantified information, 【0748】 Information visualization means for displaying classification results, 【0749】 An information processing system that includes this. 【0750】 (Claim 2) 【0751】 The information processing system according to claim 1, wherein the clustering means uses machine learning techniques to divide information into multiple groups. 【0752】 (Claim 3) 【0753】 The information processing system according to claim 1, wherein the above-mentioned information visualization means displays the classification results in a two-dimensional space, thereby providing an interface that allows for visual confirmation of the distribution of information. 【0754】 "Application Example 1" 【0755】 (Claim 1) 【0756】 Information gathering means for receiving user information and social trend information, 【0757】 A text feature extraction method for quantifying the above information, 【0758】 A group classification means that groups information based on quantified information, 【0759】 Information visualization means for visualizing grouping results, 【0760】 A design proposal generation means for generating design proposals related to urban structures, 【0761】 A system that includes this. 【0762】 (Claim 2) 【0763】 The system according to claim 1, wherein the group classification means uses an automated learning algorithm for classifying information into multiple groups. 【0764】 (Claim 3) 【0765】 The system according to claim 1, which provides a connection means that allows the distribution of information to be visually confirmed by displaying the grouping results in a two-dimensional space. 【0766】 "Example 2 of combining an emotion engine" 【0767】 (Claim 1) 【0768】 Information gathering means for receiving user information and market trend information, 【0769】 A feature extraction method for quantifying the above information, 【0770】 A classification method that classifies information based on quantified information, 【0771】 A visualization method for visualizing the classification results, 【0772】 A means of recognizing the emotional state of a user, 【0773】 A system that includes this. 【0774】 (Claim 2) 【0775】 The system according to claim 1, wherein the classification means uses a learning algorithm for classifying information into multiple groups. 【0776】 (Claim 3) 【0777】 The system according to claim 1, wherein the above-mentioned visualization means displays the classification results in a two-dimensional space, thereby providing an interface that allows for visual confirmation of the distribution of information. 【0778】 "Application example 2 when combining with an emotional engine" 【0779】 (Claim 1) 【0780】 Information acquisition means for receiving customer information and market trend information, 【0781】 Information feature analysis means for quantifying the above information, 【0782】 A classification method that classifies information based on quantified information, 【0783】 Information visualization means for visualizing classification results, 【0784】 A sentiment analysis advertising optimization method that analyzes user emotions and optimizes advertising content, 【0785】 A system that includes this. 【0786】 (Claim 2) 【0787】 The system according to claim 1, wherein the classification means uses a machine learning method for classifying information into multiple categories. 【0788】 (Claim 3) 【0789】 The system according to claim 1, wherein the above-mentioned information visualization means displays the classification results in a two-dimensional area, thereby providing an interface that allows for visual confirmation of the information distribution. [Explanation of Symbols] 【0790】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A data collection method for receiving user data and market trend data, A text feature extraction method for quantifying the above data, A clustering method that clusters data based on digitized data, A data visualization method for visualizing clustering results, A system that includes this. [Claim 2] The system according to claim 1, wherein the clustering means uses a machine learning algorithm for classifying data into multiple clusters. [Claim 3] The system according to claim 1, wherein the data visualization means provides an interface that allows the distribution of data to be visually confirmed by displaying the clustering results in a two-dimensional space.

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