system

Abstract

The system according to this embodiment aims to analyze collected data, visualize the results, and make appropriate suggestions. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a visualization unit, and a proposal unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The visualization unit visualizes the analysis results obtained by the analysis unit. The proposal unit makes proposals based on the results visualized by the visualization unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the analysis results of the collected data have not been effectively visualized and appropriate proposals have not been sufficiently made, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze the collected data, visualize the results, and make appropriate proposals.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a visualization unit, and a proposal unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The visualization unit visualizes the analysis results obtained by the analysis unit. The proposal unit makes a proposal based on the results visualized by the visualization unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the collected data, visualize the results, and make appropriate suggestions. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. 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).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The SEN-NIN system according to an embodiment of the present invention is a system in which an AI agent manages structured and unstructured data, and performs data visualization and proposals. Unlike conventional data management systems that assume only structured data, the SEN-NIN system can also handle unstructured data such as text, audio, and video. The SEN-NIN system provides a system that centrally manages all data and enables analysis, prediction, and execution. For example, the SEN-NIN system can perform data analysis and prediction in natural language, and data analysis requests can be made in a chat format, eliminating the need for data scientists or analysts. Furthermore, the SEN-NIN system is capable of advanced and highly accurate analysis including email, text, audio, and images. In addition, the SEN-NIN system can visualize data in a format intended for each customer, making it ideal for unstructured analysis. One of the functions of the SEN-NIN system is to convert unstructured data into structured data and automatically edit it into a form that is easy to analyze and utilize. Moreover, the SEN-NIN system can continuously learn information about each customer and create an AI agent that is valuable to the customer. This enables the provision of non-personnel-dependent services that are not affected by sales representative handovers. Furthermore, the SEN-NIN system can refer to internal data and provide analysis and prediction instructions in natural language, eliminating the need for programming and the need to request assistance from data analysts or scientists. In addition to data analysis and visualization, the SEN-NIN system can perform RFM analysis to measure customer purchasing behavior. Moreover, the SEN-NIN system can create customer-specific talk scripts from unstructured data and analyze and learn from successful deals and top-performing deals within the inside sales team. As a result, the SEN-NIN system can suggest what to say next during customer negotiations. The SEN-NIN system is expected to experience significant growth along with the growth of the data lake market. The SEN-NIN system will accelerate development and sales and expand revenue with a pay-as-you-go subscription model. The SEN-NIN system will also provide consulting services to support data utilization after implementation.This allows the SEN-NIN system to centrally manage structured and unstructured data, and to perform data visualization and proposals.

[0029] The SEN-NIN system according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, and a proposal unit. The data collection unit collects data. The data collection unit can collect data in various formats, such as text data, numerical data, and image data. The data collection unit can collect data from the internet using, for example, web scraping technology. The data collection unit can also collect data in real time using sensors. For example, the data collection unit collects data from IoT devices. Furthermore, the data collection unit can collect manually entered data from users. For example, the data collection unit collects text data entered by users. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, machine learning algorithms. For example, the analysis unit classifies the data using clustering algorithms. The analysis unit can also predict data trends using regression analysis. For example, the analysis unit predicts future values ​​of data using linear regression. Furthermore, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit extracts emotions from text data. The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit visually displays data using graphs and charts, for example. For instance, it can display the distribution of data using bar graphs. It can also display changes in data using line graphs, for example, it can display changes in data over time using line graphs. Furthermore, the visualization unit can display data density using heatmaps, for example, it can display the degree of data concentration using heatmaps. The proposal unit makes proposals based on the results visualized by the visualization unit. The proposal unit makes proposals that can be applied to marketing activities, for example. For example, it proposes promotional activities based on customer purchasing behavior. It can also create talk scripts for each customer, for example, it suggests what to talk about next during a business negotiation with a customer. Furthermore, the proposal unit can propose products and services that meet customer needs, for example, it proposes new products based on a customer's past purchase history.As a result, the SEN-NIN system according to the embodiment can consistently perform data collection, analysis, visualization, and proposal. Some or all of the above-described processes in the collection unit, analysis unit, visualization unit, and proposal unit may be performed using AI, for example, or without AI. For example, the collection unit can input data collected using web scraping technology into a generating AI and have the generating AI perform data preprocessing. The analysis unit can take the data collected by the collection unit as input and perform data analysis using an AI model that performs data analysis. The visualization unit can take the analysis results obtained by the analysis unit as input and perform data visualization using an AI model that performs data visualization. The proposal unit can take the results visualized by the visualization unit as input and make proposals using an AI model that makes proposals.

[0030] The data collection unit collects data. The data collection unit can collect data in various formats, such as text data, numerical data, and image data. Specifically, the data collection unit uses web scraping technology to collect data from the internet. Web scraping technology is a technique that automatically extracts necessary information from specific websites, and can collect text data from, for example, news articles, blog posts, and social media posts. The data collection unit can also collect data in real time using sensors. For example, it can collect data from IoT devices. IoT devices can collect environmental data such as temperature, humidity, pressure, and vibration in real time and transmit it to a central database. Furthermore, the data collection unit can collect manually entered data from users. For example, it can collect text data entered by users. Users provide surveys and feedback through dedicated input forms, and this data is collected by the data collection unit. The data collection unit centrally collects data from these diverse data sources and performs data preprocessing. Preprocessing includes data cleaning, normalization, and format conversion. For example, the data collection unit can input data collected using web scraping technology into a generating AI and have the generating AI perform data preprocessing. The generation AI automatically performs preprocessing such as noise reduction and missing value imputation to improve data quality. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses machine learning algorithms to analyze the data. Specifically, the analysis department classifies the data using clustering algorithms. Clustering algorithms are techniques that group data based on similarity; for example, customer purchasing behavior data can be clustered to identify customer segments. The analysis department can also predict data trends using regression analysis. For example, the analysis department uses linear regression to predict future values ​​of data. Linear regression is a technique that predicts future values ​​based on past data; for example, sales data can be used to predict future sales. Furthermore, the analysis department can analyze text data using natural language processing techniques. Natural language processing techniques extract meaning and sentiment from text data; for example, sentiment can be extracted from customer reviews and feedback to evaluate customer satisfaction. By combining these techniques, the analysis department can analyze the collected data from multiple perspectives and gain insights. For example, the analysis department can use an AI model that takes the data collected by the data collection department as input to perform data analysis. The AI ​​model can analyze large amounts of data quickly and accurately, extracting patterns and trends. This allows the analytics department to support data-driven decision-making and maximize the overall effectiveness of the system.

[0032] The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit visually displays data using graphs and charts, for example. Specifically, the visualization unit displays the distribution of data using bar graphs. Bar graphs are graphs that visually compare data by category, for example, sales by customer segment can be compared. The visualization unit can also display changes in data using line graphs. Line graphs are graphs that visually display changes in data over time, for example, the trend of monthly sales can be displayed. Furthermore, the visualization unit can display the density of data using heatmaps. Heatmaps are graphs that visually display the degree of data concentration, for example, the degree of sales concentration by region can be displayed. Using these visualization techniques, the visualization unit can display the analysis results in an intuitively easy-to-understand format. For example, the visualization unit can use an AI model that takes the analysis results obtained by the analysis unit as input and visualize the data. The AI ​​model can automatically detect data patterns and trends and select the optimal visualization method. This allows the visualization unit to quickly and effectively communicate data insights and support decision-making.

[0033] The proposal department makes proposals based on the results visualized by the visualization department. For example, the proposal department makes proposals that can be applied to marketing activities. Specifically, the proposal department proposes promotional activities based on customer purchasing behavior. For example, offering discount coupons or benefits to specific customer segments can increase purchasing intent. The proposal department can also create talk scripts for each customer. For example, the proposal department suggests what to say next during a business negotiation with a customer. This allows sales representatives to communicate effectively according to customer needs. Furthermore, the proposal department can propose products and services that meet customer needs. For example, the proposal department proposes new products based on a customer's past purchase history. This can improve customer satisfaction and encourage repeat purchases. The proposal department can use AI models to make these proposals. For example, the proposal department can use an AI model that takes the results visualized by the visualization department as input and make proposals. The AI ​​model can learn from past data and patterns and automatically generate optimal proposals. This allows the proposal department to quickly make effective, data-driven proposals and maximize the overall effectiveness of the system.

[0034] The data collection unit can convert unstructured data into structured data. For example, the data collection unit can convert text data into structured data. For example, the data collection unit can analyze text data using natural language processing techniques, extract keywords and phrases, and convert them into structured data. The data collection unit can also convert image data into structured data. For example, the data collection unit can analyze image data using image recognition techniques, identify objects within the image, and convert them into structured data. Furthermore, the data collection unit can also convert audio data into structured data. For example, the data collection unit can convert audio data into text data using speech recognition techniques, and then convert that text data into structured data. This makes data management easier by converting unstructured data into structured data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input unstructured data into a generating AI and have the generating AI perform the conversion to structured data.

[0035] The analysis unit can perform data analysis in natural language. For example, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit can extract emotions from text data. The analysis unit can also use generative AI to perform data analysis in natural language. For example, the analysis unit can input text data into the generative AI, which will perform data analysis in natural language. Furthermore, the analysis unit can accept data analysis requests in chat format to perform data analysis in natural language. For example, the analysis unit can accept data analysis requests entered by users in chat format and perform data analysis based on those requests. This enables data analysis in natural language. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input text data into the generative AI to perform data analysis in natural language, which will perform data analysis in natural language.

[0036] The visualization unit can visualize data in a format intended for each customer. For example, the visualization unit can visualize data in graph format according to customer needs. For example, the visualization unit can display data as a bar graph or pie chart according to the format specified by the customer. The visualization unit can also visualize data in tabular format in a format intended for each customer. For example, the visualization unit can display data in tabular format according to the format specified by the customer. Furthermore, the visualization unit can visualize data in heatmap format in a format intended for each customer. For example, the visualization unit can display data in heatmap format according to the format specified by the customer. This makes it possible to visualize data in a format intended for each customer, thereby enabling visualization that meets customer needs. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data into a generating AI to visualize the data according to the format specified by the customer, and the generating AI can perform the data visualization.

[0037] The proposal department can make suggestions that can be applied to marketing activities based on the analysis results. For example, the proposal department can propose promotional activities based on customer purchasing behavior. For example, the proposal department can analyze customer purchase history and propose promotions for specific products or services. The proposal department can also propose marketing campaigns based on customer interests. For example, the proposal department can analyze past customer behavior data and propose campaigns that customers are likely to be interested in. Furthermore, the proposal department can make cross-sell and up-sell suggestions based on customer purchasing patterns. For example, the proposal department can suggest products related to products that customers have purchased. This enables the creation of suggestions that can be applied to marketing activities. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input analysis results into a generating AI, which can then make suggestions that can be applied to marketing activities.

[0038] The proposal department can create individualized sales scripts for each customer. For example, the proposal department can create scripts that suggest what to discuss next during a sales meeting with a customer. For example, the proposal department can analyze past sales data of a customer, extract patterns from successful meetings, and create a sales script. The proposal department can also create customized sales scripts based on the customer's needs and interests. For example, the proposal department can analyze past customer behavior data and create a sales script that includes topics likely to interest the customer. Furthermore, the proposal department can create sales scripts based on the customer's purchase history. For example, the proposal department can create a sales script that includes information related to products the customer has purchased. By creating individualized sales scripts for each customer, the efficiency of sales meetings is improved. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input customer sales data into a generating AI, which can then create a sales script.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can analyze patterns in data previously collected by the user and propose the optimal collection method. For example, the data collection unit can analyze the user's past data collection history, predict the types of data to be collected during specific time periods, and collect them efficiently. The data collection unit can also customize the collection method based on the user's past data collection history and collect data tailored to the user's needs. For example, the data collection unit can analyze the user's past data collection history and select the collection method best suited to the user's needs. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0040] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. For example, the data collection unit can collect necessary data at the appropriate time according to the progress of the user's projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can prioritize collecting data related to the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input information about the user's projects and areas of interest into a generating AI, which can then filter and collect highly relevant data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will prioritize the collection of nearby data based on the user's location information. Furthermore, if the user is on the move, the data collection unit can also prioritize the collection of data related to their destination. For example, the data collection unit will monitor the user's location information in real time and collect data related to their destination. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0042] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. For example, the data collection unit can collect highly relevant data based on the user's interests on social media. The data collection unit can also analyze the activities of the user's followers and friends on social media and collect relevant data. For example, the data collection unit can analyze the activities of the user's followers and friends and collect highly relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can collect relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can evaluate the importance of the data and perform a detailed analysis on data that it determines to be high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can evaluate the importance of the data and perform a simplified analysis on data that it determines to be low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, the generating AI can evaluate the importance, and the level of detail of the analysis can be adjusted based on the result.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit uses image recognition technology to analyze image data. Furthermore, the analysis unit can apply a speech recognition algorithm to audio data. For example, the analysis unit uses speech recognition technology to analyze audio data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, the generating AI can select an appropriate analysis algorithm, and then apply that algorithm to analyze the data.

[0045] The analysis department can prioritize analysis based on the data submission timing. For example, the analysis department can prioritize the analysis of data that is urgent. For example, the analysis department can evaluate the data submission timing and prioritize the analysis of data that it deems urgent. The analysis department can also quickly analyze data with approaching submission deadlines. For example, the analysis department can evaluate the data submission deadlines and quickly analyze data with approaching deadlines. Furthermore, the analysis department can adjust the order and priority of analyses according to the submission timing. For example, the analysis department can determine the priority of analyses based on the data submission timing. This allows for the priority of analyzing urgent data by determining the priority of analyses based on the data submission timing. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the data submission timing into a generating AI, which can evaluate the submission timing and determine the priority of analyses based on the results.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. For instance, the analysis unit can evaluate the relevance of the data and prioritize the analysis of data deemed highly relevant. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can evaluate the relevance of the data and postpone the analysis of data deemed less relevant. Furthermore, the analysis unit can adjust the order and priority of analysis according to the relevance of the data. For example, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can evaluate the relevance and adjust the order of analysis based on the results.

[0047] The visualization unit can adjust the level of detail of the visualization based on the importance of the data during visualization. For example, the visualization unit performs detailed visualization for data with high importance. For example, the visualization unit evaluates the importance of the data and performs detailed visualization for data that it determines to be of high importance. The visualization unit can also perform simplified visualization for data with low importance. For example, the visualization unit evaluates the importance of the data and performs simplified visualization for data that it determines to be of low importance. Furthermore, the visualization unit can adjust the depth and scope of the visualization according to the importance of the data. For example, the visualization unit adjusts the level of detail of the visualization based on the importance of the data. This allows for efficient visualization by adjusting the level of detail of the visualization according to the importance of the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input the importance of the data into a generating AI, the generating AI can evaluate the importance, and the level of detail of the visualization can be adjusted based on the result.

[0048] The visualization unit can apply different visualization methods depending on the data category during visualization. For example, the visualization unit can apply word clouds or heatmaps to text data. For instance, the visualization unit analyzes text data and visualizes frequently occurring words using a word cloud. The visualization unit can also apply gallery views or slideshows to image data. For example, the visualization unit analyzes image data and displays it in gallery view. Furthermore, the visualization unit can apply waveform displays or spectrograms to audio data. For example, the visualization unit analyzes audio data and visualizes it using waveform displays. This improves the accuracy of visualization by applying appropriate visualization methods according to the data category. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data category into a generating AI, the generating AI can select an appropriate visualization method, and apply that method to visualize the data.

[0049] The visualization unit can determine the visualization priority based on the data submission date during visualization. For example, the visualization unit will prioritize visualization of data that is urgent. For example, the visualization unit will evaluate the data submission date and prioritize visualization of data that it deems urgent. The visualization unit can also quickly visualize data with an approaching submission deadline. For example, the visualization unit will evaluate the data submission deadline and quickly visualize data with an approaching deadline. Furthermore, the visualization unit can adjust the order and priority of visualization according to the submission date. For example, the visualization unit will determine the visualization priority based on the data submission date. This allows for the priority visualization of urgent data by determining the visualization priority based on the data submission date. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data submission date into a generating AI, which will evaluate the submission date and determine the visualization priority based on the result.

[0050] The visualization unit can adjust the visualization order based on the relevance of the data during visualization. For example, the visualization unit can prioritize the visualization of highly relevant data. For example, the visualization unit can evaluate the relevance of the data and prioritize the visualization of data that it determines to be highly relevant. The visualization unit can also postpone the visualization of less relevant data. For example, the visualization unit can evaluate the relevance of the data and postpone the visualization of data that it determines to be less relevant. Furthermore, the visualization unit can adjust the visualization order and priority according to the relevance of the data. For example, the visualization unit can adjust the visualization order based on the relevance of the data. This allows for efficient visualization by adjusting the visualization order based on the relevance of the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the relevance of the data into a generating AI, the generating AI can evaluate the relevance, and the visualization order can be adjusted based on the results.

[0051] The proposal unit can adjust the level of detail of its proposals based on the importance of the data. For example, the proposal unit can provide detailed proposals for data with high importance. For example, the proposal unit can evaluate the importance of the data and provide detailed proposals for data it deems to be highly important. The proposal unit can also provide simplified proposals for data with low importance. For example, the proposal unit can evaluate the importance of the data and provide simplified proposals for data it deems to be less important. Furthermore, the proposal unit can adjust the depth and scope of its proposals according to the importance of the data. For example, the proposal unit can adjust the level of detail of its proposals based on the importance of the data. This allows for efficient proposals by adjusting the level of detail of proposals according to the importance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the data into a generating AI, the generating AI can evaluate the importance, and the level of detail of the proposal can be adjusted based on the result.

[0052] The proposal unit can apply different proposal algorithms depending on the data category during the proposal process. For example, the proposal unit can apply a natural language processing algorithm to text data. For example, the proposal unit uses natural language processing technology to analyze text data. The proposal unit can also apply an image recognition algorithm to image data. For example, the proposal unit uses image recognition technology to analyze image data. Furthermore, the proposal unit can apply a speech recognition algorithm to audio data. For example, the proposal unit uses speech recognition technology to analyze audio data. By applying an appropriate proposal algorithm according to the data category, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data category into a generating AI, the generating AI can select an appropriate proposal algorithm, and then apply that algorithm to make a proposal.

[0053] The proposal department can determine the priority of proposals based on the data submission timing when submitting proposals. For example, the proposal department will prioritize proposals for data that is urgent. For example, the proposal department will evaluate the data submission timing and prioritize proposals for data that it deems urgent. The proposal department can also quickly submit proposals for data with approaching submission deadlines. For example, the proposal department will evaluate the data submission deadlines and quickly submit proposals for data with approaching deadlines. Furthermore, the proposal department can adjust the order and priority of proposals according to the submission timing. For example, the proposal department will determine the priority of proposals based on the data submission timing. This allows for prioritizing proposals for urgent data by determining the priority of proposals based on the data submission timing. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the data submission timing into a generating AI, which will evaluate the submission timing and determine the priority of proposals based on the results.

[0054] The proposal unit can adjust the order of proposals based on the relevance of the data during the proposal process. For example, the proposal unit can prioritize proposing highly relevant data. For example, the proposal unit can evaluate the relevance of the data and prioritize proposing data that it deems highly relevant. The proposal unit can also postpone proposing less relevant data. For example, the proposal unit can evaluate the relevance of the data and postpone proposing data that it deems less relevant. Furthermore, the proposal unit can adjust the order and priority of proposals according to the relevance of the data. For example, the proposal unit can adjust the order of proposals based on the relevance of the data. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the data into a generating AI, the generating AI can evaluate the relevance, and the order of proposals can be adjusted based on the results.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can analyze patterns in data previously collected by the user and propose the most suitable collection method. Furthermore, based on the user's past data collection history, the unit can customize the collection method to meet the user's needs. In addition, the unit can predict the types of data to be collected during specific time periods and collect them efficiently. This allows for the selection of the optimal collection method by analyzing past data collection history.

[0057] The data collection unit can filter data based on the user's current projects and areas of interest during the data collection process. For example, the unit can prioritize collecting data related to the user's current projects. It can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the unit can collect necessary data at the appropriate time according to the progress of the user's projects. This allows for the collection of highly relevant data by filtering it based on the user's projects and areas of interest.

[0058] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. Furthermore, if the user is on the move, the data collection unit can prioritize the collection of data related to their destination. In addition, based on the user's location, the data collection unit can prioritize the collection of nearby data. This allows for the priority collection of highly relevant data by considering the user's geographical location.

[0059] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, it can analyze the content of users' social media posts and collect relevant data. It can also collect highly relevant data based on users' interests on social media. Furthermore, it can analyze the activity of users' followers and friends on social media and collect relevant data. In this way, relevant data can be collected by analyzing users' social media activity.

[0060] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data.

[0061] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply natural language processing algorithms to text data, image recognition algorithms to image data, and speech recognition algorithms to audio data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The data collection unit collects data. The data collection unit can collect data in various formats, such as text data, numerical data, and image data. For example, the data collection unit can collect data from the internet using web scraping technology. The data collection unit can also collect data in real time using sensors. Furthermore, the data collection unit can also collect manually entered data from users. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms. For example, it can classify data using clustering algorithms or predict data trends using regression analysis. Furthermore, it can also analyze text data using natural language processing techniques. Step 3: The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit visually displays the data using graphs and charts. For example, it can display the distribution of data using bar graphs, or show how the data changes over time using line graphs. Furthermore, it can also display the density of the data using heatmaps. Step 4: The proposal department makes proposals based on the results visualized by the visualization department. The proposal department makes proposals that can be applied to marketing activities. For example, they can propose promotional activities based on customer purchasing behavior or create individualized talk scripts for each customer. Furthermore, they can propose products and services that meet customer needs.

[0064] (Example of form 2) The SEN-NIN system according to an embodiment of the present invention is a system in which an AI agent manages structured and unstructured data, and performs data visualization and proposals. Unlike conventional data management systems that assume only structured data, the SEN-NIN system can also handle unstructured data such as text, audio, and video. The SEN-NIN system provides a system that centrally manages all data and enables analysis, prediction, and execution. For example, the SEN-NIN system can perform data analysis and prediction in natural language, and data analysis requests can be made in a chat format, eliminating the need for data scientists or analysts. Furthermore, the SEN-NIN system is capable of advanced and highly accurate analysis including email, text, audio, and images. In addition, the SEN-NIN system can visualize data in a format intended for each customer, making it ideal for unstructured analysis. One of the functions of the SEN-NIN system is to convert unstructured data into structured data and automatically edit it into a form that is easy to analyze and utilize. Moreover, the SEN-NIN system can continuously learn information about each customer and create an AI agent that is valuable to the customer. This enables the provision of non-personnel-dependent services that are not affected by sales representative handovers. Furthermore, the SEN-NIN system can refer to internal data and provide analysis and prediction instructions in natural language, eliminating the need for programming and the need to request assistance from data analysts or scientists. In addition to data analysis and visualization, the SEN-NIN system can perform RFM analysis to measure customer purchasing behavior. Moreover, the SEN-NIN system can create customer-specific talk scripts from unstructured data and analyze and learn from successful deals and top-performing deals within the inside sales team. As a result, the SEN-NIN system can suggest what to say next during customer negotiations. The SEN-NIN system is expected to experience significant growth along with the growth of the data lake market. The SEN-NIN system will accelerate development and sales and expand revenue with a pay-as-you-go subscription model. The SEN-NIN system will also provide consulting services to support data utilization after implementation.This allows the SEN-NIN system to centrally manage structured and unstructured data, and to perform data visualization and proposals.

[0065] The SEN-NIN system according to this embodiment comprises a data collection unit, an analysis unit, a visualization unit, and a proposal unit. The data collection unit collects data. The data collection unit can collect data in various formats, such as text data, numerical data, and image data. The data collection unit can collect data from the internet using, for example, web scraping technology. The data collection unit can also collect data in real time using sensors. For example, the data collection unit collects data from IoT devices. Furthermore, the data collection unit can collect manually entered data from users. For example, the data collection unit collects text data entered by users. The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, machine learning algorithms. For example, the analysis unit classifies the data using clustering algorithms. The analysis unit can also predict data trends using regression analysis. For example, the analysis unit predicts future values ​​of data using linear regression. Furthermore, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit extracts emotions from text data. The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit visually displays data using graphs and charts, for example. For instance, it can display the distribution of data using bar graphs. It can also display changes in data using line graphs, for example, it can display changes in data over time using line graphs. Furthermore, the visualization unit can display data density using heatmaps, for example, it can display the degree of data concentration using heatmaps. The proposal unit makes proposals based on the results visualized by the visualization unit. The proposal unit makes proposals that can be applied to marketing activities, for example. For example, it proposes promotional activities based on customer purchasing behavior. It can also create talk scripts for each customer, for example, it suggests what to talk about next during a business negotiation with a customer. Furthermore, the proposal unit can propose products and services that meet customer needs, for example, it proposes new products based on a customer's past purchase history.As a result, the SEN-NIN system according to the embodiment can consistently perform data collection, analysis, visualization, and proposal. Some or all of the above-described processes in the collection unit, analysis unit, visualization unit, and proposal unit may be performed using AI, for example, or without AI. For example, the collection unit can input data collected using web scraping technology into a generating AI and have the generating AI perform data preprocessing. The analysis unit can take the data collected by the collection unit as input and perform data analysis using an AI model that performs data analysis. The visualization unit can take the analysis results obtained by the analysis unit as input and perform data visualization using an AI model that performs data visualization. The proposal unit can take the results visualized by the visualization unit as input and make proposals using an AI model that makes proposals.

[0066] The data collection unit collects data. The data collection unit can collect data in various formats, such as text data, numerical data, and image data. Specifically, the data collection unit uses web scraping technology to collect data from the internet. Web scraping technology is a technique that automatically extracts necessary information from specific websites, and can collect text data from, for example, news articles, blog posts, and social media posts. The data collection unit can also collect data in real time using sensors. For example, it can collect data from IoT devices. IoT devices can collect environmental data such as temperature, humidity, pressure, and vibration in real time and transmit it to a central database. Furthermore, the data collection unit can collect manually entered data from users. For example, it can collect text data entered by users. Users provide surveys and feedback through dedicated input forms, and this data is collected by the data collection unit. The data collection unit centrally collects data from these diverse data sources and performs data preprocessing. Preprocessing includes data cleaning, normalization, and format conversion. For example, the data collection unit can input data collected using web scraping technology into a generating AI and have the generating AI perform data preprocessing. The generation AI automatically performs preprocessing such as noise reduction and missing value imputation to improve data quality. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0067] The analysis department analyzes the data collected by the data collection department. For example, the analysis department uses machine learning algorithms to analyze the data. Specifically, the analysis department classifies the data using clustering algorithms. Clustering algorithms are techniques that group data based on similarity; for example, customer purchasing behavior data can be clustered to identify customer segments. The analysis department can also predict data trends using regression analysis. For example, the analysis department uses linear regression to predict future values ​​of data. Linear regression is a technique that predicts future values ​​based on past data; for example, sales data can be used to predict future sales. Furthermore, the analysis department can analyze text data using natural language processing techniques. Natural language processing techniques extract meaning and sentiment from text data; for example, sentiment can be extracted from customer reviews and feedback to evaluate customer satisfaction. By combining these techniques, the analysis department can analyze the collected data from multiple perspectives and gain insights. For example, the analysis department can use an AI model that takes the data collected by the data collection department as input to perform data analysis. The AI ​​model can analyze large amounts of data quickly and accurately, extracting patterns and trends. This allows the analytics department to support data-driven decision-making and maximize the overall effectiveness of the system.

[0068] The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit visually displays data using graphs and charts, for example. Specifically, the visualization unit displays the distribution of data using bar graphs. Bar graphs are graphs that visually compare data by category, for example, sales by customer segment can be compared. The visualization unit can also display changes in data using line graphs. Line graphs are graphs that visually display changes in data over time, for example, the trend of monthly sales can be displayed. Furthermore, the visualization unit can display the density of data using heatmaps. Heatmaps are graphs that visually display the degree of data concentration, for example, the degree of sales concentration by region can be displayed. Using these visualization techniques, the visualization unit can display the analysis results in an intuitively easy-to-understand format. For example, the visualization unit can use an AI model that takes the analysis results obtained by the analysis unit as input and visualize the data. The AI ​​model can automatically detect data patterns and trends and select the optimal visualization method. This allows the visualization unit to quickly and effectively communicate data insights and support decision-making.

[0069] The proposal department makes proposals based on the results visualized by the visualization department. For example, the proposal department makes proposals that can be applied to marketing activities. Specifically, the proposal department proposes promotional activities based on customer purchasing behavior. For example, offering discount coupons or benefits to specific customer segments can increase purchasing intent. The proposal department can also create talk scripts for each customer. For example, the proposal department suggests what to say next during a business negotiation with a customer. This allows sales representatives to communicate effectively according to customer needs. Furthermore, the proposal department can propose products and services that meet customer needs. For example, the proposal department proposes new products based on a customer's past purchase history. This can improve customer satisfaction and encourage repeat purchases. The proposal department can use AI models to make these proposals. For example, the proposal department can use an AI model that takes the results visualized by the visualization department as input and make proposals. The AI ​​model can learn from past data and patterns and automatically generate optimal proposals. This allows the proposal department to quickly make effective, data-driven proposals and maximize the overall effectiveness of the system.

[0070] The data collection unit can convert unstructured data into structured data. For example, the data collection unit can convert text data into structured data. For example, the data collection unit can analyze text data using natural language processing techniques, extract keywords and phrases, and convert them into structured data. The data collection unit can also convert image data into structured data. For example, the data collection unit can analyze image data using image recognition techniques, identify objects within the image, and convert them into structured data. Furthermore, the data collection unit can also convert audio data into structured data. For example, the data collection unit can convert audio data into text data using speech recognition techniques, and then convert that text data into structured data. This makes data management easier by converting unstructured data into structured data. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input unstructured data into a generating AI and have the generating AI perform the conversion to structured data.

[0071] The analysis unit can perform data analysis in natural language. For example, the analysis unit can analyze text data using natural language processing technology. For example, the analysis unit can extract emotions from text data. The analysis unit can also use generative AI to perform data analysis in natural language. For example, the analysis unit can input text data into the generative AI, which will perform data analysis in natural language. Furthermore, the analysis unit can accept data analysis requests in chat format to perform data analysis in natural language. For example, the analysis unit can accept data analysis requests entered by users in chat format and perform data analysis based on those requests. This enables data analysis in natural language. Some or all of the above processes in the analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input text data into the generative AI to perform data analysis in natural language, which will perform data analysis in natural language.

[0072] The visualization unit can visualize data in a format intended for each customer. For example, the visualization unit can visualize data in graph format according to customer needs. For example, the visualization unit can display data as a bar graph or pie chart according to the format specified by the customer. The visualization unit can also visualize data in tabular format in a format intended for each customer. For example, the visualization unit can display data in tabular format according to the format specified by the customer. Furthermore, the visualization unit can visualize data in heatmap format in a format intended for each customer. For example, the visualization unit can display data in heatmap format according to the format specified by the customer. This makes it possible to visualize data in a format intended for each customer, thereby enabling visualization that meets customer needs. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data into a generating AI to visualize the data according to the format specified by the customer, and the generating AI can perform the data visualization.

[0073] The proposal department can make suggestions that can be applied to marketing activities based on the analysis results. For example, the proposal department can propose promotional activities based on customer purchasing behavior. For example, the proposal department can analyze customer purchase history and propose promotions for specific products or services. The proposal department can also propose marketing campaigns based on customer interests. For example, the proposal department can analyze past customer behavior data and propose campaigns that customers are likely to be interested in. Furthermore, the proposal department can make cross-sell and up-sell suggestions based on customer purchasing patterns. For example, the proposal department can suggest products related to products that customers have purchased. This enables the creation of suggestions that can be applied to marketing activities. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input analysis results into a generating AI, which can then make suggestions that can be applied to marketing activities.

[0074] The proposal department can create individualized sales scripts for each customer. For example, the proposal department can create scripts that suggest what to discuss next during a sales meeting with a customer. For example, the proposal department can analyze past sales data of a customer, extract patterns from successful meetings, and create a sales script. The proposal department can also create customized sales scripts based on the customer's needs and interests. For example, the proposal department can analyze past customer behavior data and create a sales script that includes topics likely to interest the customer. Furthermore, the proposal department can create sales scripts based on the customer's purchase history. For example, the proposal department can create a sales script that includes information related to products the customer has purchased. By creating individualized sales scripts for each customer, the efficiency of sales meetings is improved. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can input customer sales data into a generating AI, which can then create a sales script.

[0075] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of data collection to alleviate the user's burden. For example, the data collection unit can monitor the user's emotions in real time and adjust the frequency of data collection if it determines that the user is stressed. The data collection unit can also increase the frequency of data collection and collect more detailed data if the user is relaxed. For example, the data collection unit can monitor the user's emotions and adjust the frequency of data collection if it determines that the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can adjust the timing of data collection to quickly collect the necessary data. For example, the data collection unit can monitor the user's emotions and adjust the timing of data collection if it determines that the user is in a hurry. In this way, the user's burden can be reduced by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, the generating AI can estimate the emotion, and the timing of data collection can be adjusted based on the result.

[0076] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit can analyze patterns in data previously collected by the user and propose the optimal collection method. For example, the data collection unit can analyze the user's past data collection history, predict the types of data to be collected during specific time periods, and collect them efficiently. The data collection unit can also customize the collection method based on the user's past data collection history and collect data tailored to the user's needs. For example, the data collection unit can analyze the user's past data collection history and select the collection method best suited to the user's needs. This allows the optimal collection method to be selected by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into a generating AI, which can then select the optimal collection method.

[0077] The data collection unit can filter data based on the user's current projects and areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to the user's current projects. For example, the data collection unit can collect necessary data at the appropriate time according to the progress of the user's projects. The data collection unit can also filter and collect highly relevant data based on the user's areas of interest. For example, the data collection unit can prioritize collecting data related to the user's areas of interest. This allows for the collection of highly relevant data by filtering data based on the user's projects and areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input information about the user's projects and areas of interest into a generating AI, which can then filter and collect highly relevant data.

[0078] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit will prioritize collecting high-priority data. For example, the data collection unit monitors the user's emotions in real time and prioritizes collecting important data if it determines that the user is stressed. The data collection unit can also prioritize collecting detailed data if the user is relaxed. For example, the data collection unit monitors the user's emotions and prioritizes collecting detailed data if it determines that the user is relaxed. Furthermore, if the user is in a hurry, the data collection unit can prioritize collecting data that can be collected quickly. For example, the data collection unit monitors the user's emotions and prioritizes collecting data that can be collected quickly if it determines that the user is in a hurry. In this way, by prioritizing data according to the user's emotions, important data can be collected preferentially. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generating AI, the generating AI can estimate the emotion, and based on the result, it can determine the priority of the data to be collected.

[0079] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. For example, the data collection unit will prioritize the collection of nearby data based on the user's location information. Furthermore, if the user is on the move, the data collection unit can also prioritize the collection of data related to their destination. For example, the data collection unit will monitor the user's location information in real time and collect data related to their destination. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI, which can then prioritize the collection of highly relevant data.

[0080] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. For example, the data collection unit can collect highly relevant data based on the user's interests on social media. The data collection unit can also analyze the activities of the user's followers and friends on social media and collect relevant data. For example, the data collection unit can analyze the activities of the user's followers and friends and collect highly relevant data. In this way, relevant data can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can collect relevant data.

[0081] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand analysis results. For example, the analysis unit can monitor the user's emotions in real time and provide simple analysis results if it determines that the user is stressed. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit can monitor the user's emotions and provide detailed analysis results if it determines that the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results that get straight to the point. For example, the analysis unit can monitor the user's emotions and provide concise analysis results if it determines that the user is in a hurry. In this way, by adjusting the presentation of the analysis according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, the generating AI can estimate the emotion, and the method of expressing the analysis can be adjusted based on the result.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can evaluate the importance of the data and perform a detailed analysis on data that it determines to be high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit can evaluate the importance of the data and perform a simplified analysis on data that it determines to be low importance. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI, the generating AI can evaluate the importance, and the level of detail of the analysis can be adjusted based on the result.

[0083] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. For example, the analysis unit uses natural language processing technology to analyze text data. The analysis unit can also apply an image recognition algorithm to image data. For example, the analysis unit uses image recognition technology to analyze image data. Furthermore, the analysis unit can apply a speech recognition algorithm to audio data. For example, the analysis unit uses speech recognition technology to analyze audio data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI, the generating AI can select an appropriate analysis algorithm, and then apply that algorithm to analyze the data.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. For example, the analysis unit can monitor the user's emotions in real time and provide a short analysis if it determines that the user is in a hurry. The analysis unit can also provide a longer analysis with detailed explanations if the user is relaxed. For example, the analysis unit can monitor the user's emotions and provide a longer analysis if it determines that the user is relaxed. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. For example, the analysis unit can monitor the user's emotions and provide a visually stimulating analysis if it determines that the user is excited. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input user emotion data into a generating AI, the generating AI can estimate the emotion, and the length of the analysis can be adjusted based on the result.

[0085] The analysis department can prioritize analysis based on the data submission timing. For example, the analysis department can prioritize the analysis of data that is urgent. For example, the analysis department can evaluate the data submission timing and prioritize the analysis of data that it deems urgent. The analysis department can also quickly analyze data with approaching submission deadlines. For example, the analysis department can evaluate the data submission deadlines and quickly analyze data with approaching deadlines. Furthermore, the analysis department can adjust the order and priority of analyses according to the submission timing. For example, the analysis department can determine the priority of analyses based on the data submission timing. This allows for the priority of analyzing urgent data by determining the priority of analyses based on the data submission timing. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the data submission timing into a generating AI, which can evaluate the submission timing and determine the priority of analyses based on the results.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data. For instance, the analysis unit can evaluate the relevance of the data and prioritize the analysis of data deemed highly relevant. The analysis unit can also postpone the analysis of less relevant data. For example, the analysis unit can evaluate the relevance of the data and postpone the analysis of data deemed less relevant. Furthermore, the analysis unit can adjust the order and priority of analysis according to the relevance of the data. For example, the analysis unit can adjust the order of analysis based on the relevance of the data. This allows for more efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into a generating AI, which can evaluate the relevance and adjust the order of analysis based on the results.

[0087] The visualization unit can estimate the user's emotions and adjust the display method of the visualization based on the estimated user emotions. For example, if the user is nervous, the visualization unit can provide a simple and highly visible display method. For example, the visualization unit can monitor the user's emotions in real time and provide a simple display method if it determines that the user is nervous. The visualization unit can also provide a display method that includes detailed information if the user is relaxed. For example, the visualization unit can monitor the user's emotions and provide a detailed display method if it determines that the user is relaxed. Furthermore, the visualization unit can provide a concise display method if the user is in a hurry. For example, the visualization unit can monitor the user's emotions and provide a concise display method if it determines that the user is in a hurry. By adjusting the display method of the visualization according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into a generating AI, the generating AI can estimate the emotion, and the display method of the visualization can be adjusted based on the result.

[0088] The visualization unit can adjust the level of detail of the visualization based on the importance of the data during visualization. For example, the visualization unit performs detailed visualization for data with high importance. For example, the visualization unit evaluates the importance of the data and performs detailed visualization for data that it determines to be of high importance. The visualization unit can also perform simplified visualization for data with low importance. For example, the visualization unit evaluates the importance of the data and performs simplified visualization for data that it determines to be of low importance. Furthermore, the visualization unit can adjust the depth and scope of the visualization according to the importance of the data. For example, the visualization unit adjusts the level of detail of the visualization based on the importance of the data. This allows for efficient visualization by adjusting the level of detail of the visualization according to the importance of the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without using AI. For example, the visualization unit can input the importance of the data into a generating AI, the generating AI can evaluate the importance, and the level of detail of the visualization can be adjusted based on the result.

[0089] The visualization unit can apply different visualization methods depending on the data category during visualization. For example, the visualization unit can apply word clouds or heatmaps to text data. For instance, the visualization unit analyzes text data and visualizes frequently occurring words using a word cloud. The visualization unit can also apply gallery views or slideshows to image data. For example, the visualization unit analyzes image data and displays it in gallery view. Furthermore, the visualization unit can apply waveform displays or spectrograms to audio data. For example, the visualization unit analyzes audio data and visualizes it using waveform displays. This improves the accuracy of visualization by applying appropriate visualization methods according to the data category. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data category into a generating AI, the generating AI can select an appropriate visualization method, and apply that method to visualize the data.

[0090] The visualization unit can estimate the user's emotions and adjust the length of the visualization based on the estimated emotions. For example, if the user is in a hurry, the visualization unit can provide a short, concise visualization. For example, the visualization unit can monitor the user's emotions in real time and provide a short visualization if it determines that the user is in a hurry. The visualization unit can also provide a longer visualization with detailed explanations if the user is relaxed. For example, the visualization unit can monitor the user's emotions and provide a longer visualization if it determines that the user is relaxed. Furthermore, if the user is excited, the visualization unit can provide a visualization with visually stimulating effects. For example, the visualization unit can monitor the user's emotions and provide a visually stimulating visualization if it determines that the user is excited. By adjusting the length of the visualization according to the user's emotions, the system can provide the user with the optimal visualization result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input user emotion data into a generating AI, the generating AI can estimate the emotion, and the length of the visualization can be adjusted based on the result.

[0091] The visualization unit can determine the visualization priority based on the data submission date during visualization. For example, the visualization unit will prioritize visualization of data that is urgent. For example, the visualization unit will evaluate the data submission date and prioritize visualization of data that it deems urgent. The visualization unit can also quickly visualize data with an approaching submission deadline. For example, the visualization unit will evaluate the data submission deadline and quickly visualize data with an approaching deadline. Furthermore, the visualization unit can adjust the order and priority of visualization according to the submission date. For example, the visualization unit will determine the visualization priority based on the data submission date. This allows for the priority visualization of urgent data by determining the visualization priority based on the data submission date. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the data submission date into a generating AI, which will evaluate the submission date and determine the visualization priority based on the result.

[0092] The visualization unit can adjust the visualization order based on the relevance of the data during visualization. For example, the visualization unit can prioritize the visualization of highly relevant data. For example, the visualization unit can evaluate the relevance of the data and prioritize the visualization of data that it determines to be highly relevant. The visualization unit can also postpone the visualization of less relevant data. For example, the visualization unit can evaluate the relevance of the data and postpone the visualization of data that it determines to be less relevant. Furthermore, the visualization unit can adjust the visualization order and priority according to the relevance of the data. For example, the visualization unit can adjust the visualization order based on the relevance of the data. This allows for efficient visualization by adjusting the visualization order based on the relevance of the data. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the relevance of the data into a generating AI, the generating AI can evaluate the relevance, and the visualization order can be adjusted based on the results.

[0093] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can provide simple and easily understandable suggestions. For example, it can monitor the user's emotions in real time and provide simple suggestions if it determines the user is stressed. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, it can monitor the user's emotions and provide detailed suggestions if it determines the user is relaxed. Furthermore, if the user is in a hurry, the suggestion unit can provide concise suggestions that get straight to the point. For example, it can monitor the user's emotions and provide concise suggestions if it determines the user is in a hurry. By adjusting the way suggestions are presented according to the user's emotions, it becomes possible to provide suggestions that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI, which will estimate the emotion and adjust the way the proposal is expressed based on the result.

[0094] The proposal unit can adjust the level of detail of its proposals based on the importance of the data. For example, the proposal unit can provide detailed proposals for data with high importance. For example, the proposal unit can evaluate the importance of the data and provide detailed proposals for data it deems to be highly important. The proposal unit can also provide simplified proposals for data with low importance. For example, the proposal unit can evaluate the importance of the data and provide simplified proposals for data it deems to be less important. Furthermore, the proposal unit can adjust the depth and scope of its proposals according to the importance of the data. For example, the proposal unit can adjust the level of detail of its proposals based on the importance of the data. This allows for efficient proposals by adjusting the level of detail of proposals according to the importance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the importance of the data into a generating AI, the generating AI can evaluate the importance, and the level of detail of the proposal can be adjusted based on the result.

[0095] The proposal unit can apply different proposal algorithms depending on the data category during the proposal process. For example, the proposal unit can apply a natural language processing algorithm to text data. For example, the proposal unit uses natural language processing technology to analyze text data. The proposal unit can also apply an image recognition algorithm to image data. For example, the proposal unit uses image recognition technology to analyze image data. Furthermore, the proposal unit can apply a speech recognition algorithm to audio data. For example, the proposal unit uses speech recognition technology to analyze audio data. By applying an appropriate proposal algorithm according to the data category, the accuracy of the proposal is improved. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the data category into a generating AI, the generating AI can select an appropriate proposal algorithm, and then apply that algorithm to make a proposal.

[0096] The suggestion function can estimate the user's emotions and adjust the length of the suggestions based on those emotions. For example, if the user is in a hurry, the suggestion function can provide short, concise suggestions. For example, the suggestion function can monitor the user's emotions in real time and provide short suggestions if it determines the user is in a hurry. The suggestion function can also provide longer suggestions with more detailed explanations if the user is relaxed. For example, the suggestion function can monitor the user's emotions and provide longer suggestions if it determines the user is relaxed. Furthermore, if the user is excited, the suggestion function can provide suggestions with visually stimulating effects. For example, the suggestion function can monitor the user's emotions and provide visually stimulating suggestions if it determines the user is excited. By adjusting the length of suggestions according to the user's emotions, the system can provide the user with the most optimal suggestion results. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input user emotion data into a generating AI, the generating AI can estimate the emotion, and the length of the proposal can be adjusted based on the result.

[0097] The proposal department can determine the priority of proposals based on the data submission timing when submitting proposals. For example, the proposal department will prioritize proposals for data that is urgent. For example, the proposal department will evaluate the data submission timing and prioritize proposals for data that it deems urgent. The proposal department can also quickly submit proposals for data with approaching submission deadlines. For example, the proposal department will evaluate the data submission deadlines and quickly submit proposals for data with approaching deadlines. Furthermore, the proposal department can adjust the order and priority of proposals according to the submission timing. For example, the proposal department will determine the priority of proposals based on the data submission timing. This allows for prioritizing proposals for urgent data by determining the priority of proposals based on the data submission timing. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the data submission timing into a generating AI, which will evaluate the submission timing and determine the priority of proposals based on the results.

[0098] The proposal unit can adjust the order of proposals based on the relevance of the data during the proposal process. For example, the proposal unit can prioritize proposing highly relevant data. For example, the proposal unit can evaluate the relevance of the data and prioritize proposing data that it deems highly relevant. The proposal unit can also postpone proposing less relevant data. For example, the proposal unit can evaluate the relevance of the data and postpone proposing data that it deems less relevant. Furthermore, the proposal unit can adjust the order and priority of proposals according to the relevance of the data. For example, the proposal unit can adjust the order of proposals based on the relevance of the data. This allows for efficient proposals by adjusting the order of proposals based on the relevance of the data. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the relevance of the data into a generating AI, the generating AI can evaluate the relevance, and the order of proposals can be adjusted based on the results.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on those emotions. For example, if the user is stressed, the unit can reduce the frequency of data collection to lessen the user's burden. Conversely, if the user is relaxed, the unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the unit can adjust the timing of data collection to quickly collect the necessary data. In this way, by adjusting the timing of data collection according to the user's emotions, the user's burden can be reduced.

[0101] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can analyze patterns in data previously collected by the user and propose the most suitable collection method. Furthermore, based on the user's past data collection history, the unit can customize the collection method to meet the user's needs. In addition, the unit can predict the types of data to be collected during specific time periods and collect them efficiently. This allows for the selection of the optimal collection method by analyzing past data collection history.

[0102] The data collection unit can filter data based on the user's current projects and areas of interest during the data collection process. For example, the unit can prioritize collecting data related to the user's current projects. It can also filter and collect highly relevant data based on the user's areas of interest. Furthermore, the unit can collect necessary data at the appropriate time according to the progress of the user's projects. This allows for the collection of highly relevant data by filtering it based on the user's projects and areas of interest.

[0103] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is stressed, the unit can prioritize collecting high-priority data. Similarly, if the user is relaxed, the unit can prioritize collecting detailed data. Furthermore, if the user is in a hurry, the unit can prioritize collecting data that can be retrieved quickly. This allows for the priority collection of important data by prioritizing it according to the user's emotions.

[0104] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit can prioritize the collection of data related to that region. Furthermore, if the user is on the move, the data collection unit can prioritize the collection of data related to their destination. In addition, based on the user's location, the data collection unit can prioritize the collection of nearby data. This allows for the priority collection of highly relevant data by considering the user's geographical location.

[0105] The data collection unit can analyze users' social media activity and collect relevant data during data collection. For example, it can analyze the content of users' social media posts and collect relevant data. It can also collect highly relevant data based on users' interests on social media. Furthermore, it can analyze the activity of users' followers and friends on social media and collect relevant data. In this way, relevant data can be collected by analyzing users' social media activity.

[0106] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is stressed, the analysis unit can provide simple and easy-to-understand results. If the user is relaxed, it can provide detailed results. Furthermore, if the user is in a hurry, it can provide concise and to-the-point results. By adjusting the presentation of the analysis according to the user's emotions, the analysis can be presented in a way that is easy for the user to understand.

[0107] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on high-importance data, and a simplified analysis on low-importance data. Furthermore, the analysis unit can adjust the depth and scope of the analysis according to the importance of the data. This allows for efficient analysis by adjusting the level of detail according to the importance of the data.

[0108] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply natural language processing algorithms to text data, image recognition algorithms to image data, and speech recognition algorithms to audio data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved.

[0109] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on that estimation. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a longer analysis with more detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The data collection unit collects data. The data collection unit can collect data in various formats, such as text data, numerical data, and image data. For example, the data collection unit can collect data from the internet using web scraping technology. The data collection unit can also collect data in real time using sensors. Furthermore, the data collection unit can also collect manually entered data from users. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using machine learning algorithms. For example, it can classify data using clustering algorithms or predict data trends using regression analysis. Furthermore, it can also analyze text data using natural language processing techniques. Step 3: The visualization unit visualizes the analysis results obtained by the analysis unit. The visualization unit visually displays the data using graphs and charts. For example, it can display the distribution of data using bar graphs, or show how the data changes over time using line graphs. Furthermore, it can also display the density of the data using heatmaps. Step 4: The proposal department makes proposals based on the results visualized by the visualization department. The proposal department makes proposals that can be applied to marketing activities. For example, they can propose promotional activities based on customer purchasing behavior or create individualized talk scripts for each customer. Furthermore, they can propose products and services that meet customer needs.

[0112] 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.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and proposal unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects text data, numerical data, image data, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms. The visualization unit is implemented by the control unit 46A of the smart device 14 and visually displays the data using graphs and charts. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the visualized results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] 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.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] 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.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] 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.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] 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.

[0129] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and proposal unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects text data, numerical data, image data, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms. The visualization unit is implemented by the control unit 46A of the smart glasses 214 and visually displays the data using graphs and charts. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the visualized results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] 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.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] 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.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] 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.

[0139] 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.

[0140] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] 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.

[0145] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and proposal unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects text data, numerical data, image data, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms. The visualization unit is implemented by the control unit 46A of the headset terminal 314 and visually displays the data using graphs and charts. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the visualized results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] 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.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] 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.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] 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.

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

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

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

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

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

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the data collection unit, analysis unit, visualization unit, and proposal unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects text data, numerical data, image data, etc. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the data using machine learning algorithms. The visualization unit is implemented by the control unit 46A of the robot 414 and visually displays the data using graphs and charts. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes proposals based on the visualized results. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] 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.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] 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.

[0168] 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.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] 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."

[0171] 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.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A visualization unit that visualizes the analysis results obtained by the analysis unit, The system includes a proposal unit that makes suggestions based on the results visualized by the visualization unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Convert unstructured data to structured data The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Perform data analysis using natural language. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, Visualize data in a format intended for each customer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the analysis results, we will make suggestions that can be applied to marketing activities. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Create a talk script for each customer. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting data, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, prioritize the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display method of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, When visualizing, adjust the level of detail of the visualization based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, When visualizing data, different visualization methods are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, It estimates the user's emotions and adjusts the length of the visualization based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, When visualizing data, prioritize visualizations based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, When visualizing, adjust the order of visualizations based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting a proposal, we will prioritize the proposals based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A visualization unit that visualizes the analysis results obtained by the analysis unit, The system includes a proposal unit that makes suggestions based on the results visualized by the visualization unit. A system characterized by the following features.

2. The aforementioned collection unit is Convert unstructured data to structured data The system according to feature 1.

3. The aforementioned analysis unit is Perform data analysis using natural language. The system according to feature 1.

4. The aforementioned visualization unit, Visualize data in a format intended for each customer. The system according to feature 1.

5. The aforementioned proposal section is, Based on the analysis results, we will make suggestions that can be applied to marketing activities. The system according to feature 1.

6. The aforementioned proposal section is, Create a talk script for each customer. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system according to feature 1.

9. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current projects and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

Images