system

Abstract

The system according to this embodiment aims to analyze customer feedback and market trends in real time and respond quickly. [Solution] The system according to the embodiment comprises a data collection unit, an analysis unit, a display unit, a notification unit, a dialogue unit, and a competitor analysis unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The display unit displays trend analysis and transition graphs based on the analysis results obtained by the analysis unit. The notification unit notifies of anomaly evaluations based on the trend analysis and transition graphs displayed by the display unit. The dialogue unit provides interactive analysis functions based on the analysis results obtained by the analysis unit. The competitor analysis unit analyzes reviews of competitors based on the data collected by the data collection 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 a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to analyze customer feedback and market trends in real time and respond promptly.

[0005] The system according to the embodiment aims to analyze customer feedback and market trends in real time and respond promptly.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a display unit, a notification unit, an interaction unit, and a competitor analysis unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The display unit displays trend analysis and transition graphs based on the analysis results obtained by the analysis unit. The notification unit notifies of anomaly evaluations based on the trend analysis and transition graphs displayed by the display unit. The interaction unit provides interactive analysis functions based on the analysis results obtained by the analysis unit. The competitor analysis unit analyzes competitor reviews based on the data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze customer feedback and market trends in real time and respond quickly. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The analysis system according to an embodiment of the present invention is a powerful analytical tool for business owners and marketers, and is a system that aggregates and analyzes customer feedback over time. The purpose of this analysis system is to aggregate and analyze customer feedback over time. Specifically, it consists of the following steps: First, an AI agent aggregates data from social media, forums, and news sites and analyzes comprehensive customer voices in real time. Next, it monitors changes in evaluations in real time and responds quickly to market trends and consumer satisfaction by providing trend analysis and change graphs, alert notifications for anomaly evaluations, and interactive analysis functions. Furthermore, it displays not only the content of the evaluations but also the frequency of occurrence, identification of the target product, and related factors on the dashboard immediately to encourage quick response. It also analyzes competitor reviews and visualizes relative evaluations and advantages. For example, the AI ​​agent collects data from social media, forums, and news sites. In this process, it collects detailed data such as customer feedback, reviews, and comments. For example, by collecting customer evaluations and comments on a specific product and having the AI ​​analyze them, it is possible to understand customer voices. Next, the AI ​​analyzes the collected data and displays trend analysis and change graphs. The AI ​​analyzes collected data and monitors changes in ratings in real time. For example, if the rating for a particular product changes drastically, it displays the change in a graph and notifies the user of an alert for the anomaly. This allows business owners and marketers to respond quickly to changes in ratings. Furthermore, it provides interactive analytical capabilities. Users can instantly view rating details, frequency, target products, and related factors on the dashboard. For example, if the rating for a particular product declines, the cause can be identified and countermeasures taken quickly. It also analyzes competitor reviews and visualizes relative ratings and advantages. The AI ​​analyzes competitor reviews and visualizes how their own products and services are being evaluated compared to those of their competitors. This allows business owners and marketers to compare themselves to competitors and develop strategies.This system allows business owners and marketers to analyze customer feedback in real time and respond quickly to changes in evaluations. Furthermore, by comparing themselves to competitors, they can develop strategies and accelerate business growth. Thus, the analytics system can analyze customer feedback in real time and respond quickly to changes in evaluations.

[0029] The analysis system according to the embodiment comprises a data collection unit, an analysis unit, a display unit, a notification unit, an interaction unit, and a competitive analysis unit. The data collection unit collects data. The data collection unit collects data from, for example, social media, forums, and news sites. The data collection unit can collect, for example, posts from social media, comments on forums, and articles on news sites. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, analyze the collected data and monitor changes in evaluation in real time. The analysis unit can, for example, analyze data trends and generate change graphs. The display unit displays the trend analysis and change graphs based on the analysis results obtained by the analysis unit. The display unit can, for example, display the results of the trend analysis as line graphs or bar graphs. The notification unit notifies of anomaly evaluation alerts based on the trend analysis and change graphs displayed by the display unit. The notification unit can, for example, issue an alert when it detects an abnormal change in evaluation. The interaction unit provides interactive analysis functions based on the analysis results obtained by the analysis unit. The dialogue unit allows users to, for example, view evaluation details, frequency of occurrence, identify target products, and check related factors on a dashboard. The competitive analysis unit analyzes competitor reviews based on data collected by the data collection unit. The competitive analysis unit can, for example, analyze competitor reviews and visualize how their products and services are being evaluated compared to those of the user's own company. As a result, the analysis system according to this embodiment can analyze customer feedback in real time and respond quickly to changes in evaluations.

[0030] The data collection unit collects data from sources such as social media, forums, and news sites. Specifically, it uses social media APIs to retrieve content such as posts, comments, images, and videos. From forums, it collects threads and comment content using scraping techniques. From news sites, it collects article titles, body text, publication date, and author information. This data is centrally managed by the data collection unit and stored in a database. The data collection unit can flexibly set the frequency and scope of data collection, and can also filter data based on specific keywords or hashtags. For example, it can be set to collect only posts and comments related to a specific product name or brand name. This allows the data collection unit to efficiently collect data from a wide range of data sources and provide it to the analysis unit. Furthermore, the data collection unit is equipped with preprocessing functions to remove data duplication and noise, thus maintaining the quality of the collected data.

[0031] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data and monitors changes in ratings in real time. Specifically, it uses natural language processing (NLP) techniques to perform sentiment analysis and topic modeling on text data. In sentiment analysis, it automatically classifies ratings into positive, negative, and neutral, and tracks changes in ratings over time. In topic modeling, it uses methods such as LDA (Latent Dirichlet Allocation) to extract key topics in the data and grasp trends. Based on these analysis results, the analysis unit can generate change graphs and heatmaps to visually display changes and trends in ratings. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual changes in ratings in real time and issue early warnings. This allows the analysis unit to quickly and accurately analyze the collected data and monitor changes in ratings in real time.

[0032] The display unit displays trend analysis and change graphs based on the analysis results obtained by the analysis unit. For example, the display unit can display the results of the trend analysis as line graphs or bar graphs. Specifically, it provides a dashboard on the user interface to visually display the analysis results. Line graphs show changes in evaluations along a time axis, allowing users to grasp the evaluation trend over a specific period at a glance. Bar graphs make it easier to compare evaluations across different categories or topics. Furthermore, heatmaps can be used to visually show the concentration of evaluations over a specific period or topic. The display unit also provides a function that allows users to customize graphs and charts, enabling them to focus on or filter specific data points. This allows the display unit to communicate the analysis results to the user intuitively and effectively.

[0033] The notification unit alerts users to anomaly evaluations based on the trend analysis and change graphs displayed by the display unit. For example, the notification unit can issue an alert when it detects an anomaly in evaluation. Specifically, it monitors anomaly evaluation changes detected by the anomaly detection algorithm in real time and immediately notifies the user when an anomaly occurs. Notifications are made using multiple means, such as email, SMS, and push notifications, to enable users to respond quickly. The notification unit can set notification priorities according to the importance of the alert; important alerts are notified immediately, while relatively minor alerts can be compiled into periodic reports. In this way, the notification unit helps users respond quickly to anomaly evaluation changes.

[0034] The dialogue unit provides interactive analysis functions based on the analysis results obtained by the analysis unit. For example, the dialogue unit allows users to check evaluation details, frequency of occurrence, identification of target products, and related factors on a dashboard. Specifically, when a user inputs a question in natural language, the dialogue unit responds with appropriate analysis results. For example, in response to a question such as, "What was the highest-rated product in the past month?", the dialogue unit provides information on the relevant product based on the analysis results. The dialogue unit can generate answers in natural language to user questions using generative AI. This allows users to intuitively interact with the system and quickly obtain the necessary information. Furthermore, the dialogue unit can learn from the user's past question history and provide more accurate answers. In this way, the dialogue unit provides flexible analysis functions tailored to user needs and supports user decision-making.

[0035] The Competitive Analysis Department analyzes competitor reviews based on data collected by the Data Collection Department. For example, the Competitive Analysis Department can analyze competitor reviews and visualize how they are evaluated compared to the company's own products and services. Specifically, it collects competitor reviews and performs sentiment analysis and topic modeling using natural language processing technology. This allows the company to understand the trends and key topics in the evaluations of competitors' products and services. Based on these analysis results, the Competitive Analysis Department compares the evaluations of the company's own products and services with those of competitors to clarify its strengths and weaknesses. Furthermore, the Competitive Analysis Department can monitor changes in competitor evaluations in real time and quickly grasp new trends and market developments. As a result, the Competitive Analysis Department can provide valuable information that is useful for the company's strategic planning and marketing activities.

[0036] The data collection unit can collect data from social media, forums, and news sites. For example, the data collection unit can collect posts from social media. For example, the data collection unit can also collect comments from forums. For example, the data collection unit can also collect articles from news sites. This allows for the collection of information from diverse data sources and the analysis of comprehensive customer voices. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input posts from social media into an AI, which can then analyze and collect the content of the posts.

[0037] The analysis unit can analyze the collected data and monitor changes in evaluation in real time. For example, the analysis unit can analyze the collected data and monitor changes in evaluation in real time. For example, the analysis unit can analyze data trends and generate change graphs. For example, the analysis unit can monitor changes in evaluation in real time and detect abnormal changes in evaluation. This allows for real-time understanding of changes in evaluation and rapid response. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into AI, which can analyze the data and monitor changes in evaluation.

[0038] The display unit can display trend analysis and transition graphs. For example, the display unit can display the results of the trend analysis as a line graph or bar graph. For example, the display unit can display transition graphs, allowing users to visually grasp changes in evaluation. For example, the display unit can display changes in evaluation in real time and detect abnormal changes in evaluation. This allows users to visually grasp changes in evaluation. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the analysis results obtained by the analysis unit into the AI, and the AI ​​can generate and display the trend analysis and transition graphs.

[0039] The notification unit can issue alerts for abnormal evaluations. For example, the notification unit can issue an alert when it detects an abnormal change in evaluation. For example, the notification unit can issue an alert when the change in evaluation is rapid. For example, the notification unit can issue an alert when the change in evaluation exceeds a certain threshold. This allows for immediate identification and response to abnormal changes in evaluation. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input the trend analysis and change graph displayed by the display unit into the AI, which can then generate and notify an alert for an abnormal evaluation.

[0040] The dialogue unit can provide interactive analysis functions. For example, the dialogue unit allows users to view evaluation content, frequency of occurrence, identification of target products, and related factors on a dashboard. For example, the dialogue unit allows users to monitor changes in evaluations in real time and take countermeasures. For example, the dialogue unit allows users to identify the cause of changes in evaluations and respond quickly. This enables users to interactively analyze data. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the analysis results obtained by the analysis unit into the AI, and the AI ​​can provide interactive analysis functions.

[0041] The competitive analysis department can analyze reviews of competitors and visualize their relative evaluations and advantages. For example, the competitive analysis department can analyze reviews of competitors and visualize how they are evaluated compared to the company's own products and services. For example, the competitive analysis department can analyze reviews of competitors and display their relative evaluations in a graph. For example, the competitive analysis department can analyze reviews of competitors and visualize their advantages. This allows for comparisons with competitors and the development of strategies. Some or all of the above processes in the competitive analysis department may be performed using AI or not. For example, the competitive analysis department can input data collected by the data collection department into an AI, which can then analyze reviews of competitors and visualize their relative evaluations and advantages.

[0042] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing from past data collection history. For example, the data collection unit can determine the priority of data to be collected based on past data collection history. For example, the data collection unit can analyze past data collection history and identify areas for improvement in the collection method. This allows the optimal collection method to be selected based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data collection history into AI, and the AI ​​can select the optimal collection method.

[0043] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can filter out unnecessary data based on the user's areas of interest. For example, the data collection unit can adjust the types of data it collects in response to changes in the user's areas of interest. This allows the data to be filtered based on the user's areas of interest and highly relevant data to be collected. 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 the user's areas of interest into the AI, and the AI ​​can filter the data based on those areas of interest.

[0044] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, the data collection unit can collect highly relevant data by considering the user's travel history. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0045] 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 collect data related to topics the user has shown interest in on social media. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's social media activity. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. This allows for the collection of highly relevant data based on the user's social media activity. 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 the user's social media activity into AI, which can then collect relevant data.

[0046] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. For example, the analysis unit can perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to 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 or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0047] 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 can apply an image analysis algorithm to image data. For example, the analysis unit can apply a speech analysis algorithm to speech data. This allows for the application of an appropriate analysis algorithm according to the data category, enabling highly accurate analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply an appropriate analysis algorithm according to the category.

[0048] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze trends based on historical data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis based on the collection timing.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0050] The display unit can adjust the level of detail of the display based on the importance of the data during display. For example, the display unit can provide detailed display for highly important data, and simplified display for less important data. The display unit can also determine the display priority according to the importance of the data. This allows for efficient display by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the display based on the importance.

[0051] The display unit can apply different display algorithms depending on the data category during display. For example, the display unit can apply a natural language processing algorithm to text data. For example, the display unit can apply an image analysis algorithm to image data. For example, the display unit can apply an audio analysis algorithm to audio data. This allows for the application of an appropriate display algorithm according to the data category, enabling highly legible display. Some or all of the above-described processes in the display unit may be performed using AI, or they may not be performed using AI. For example, the display unit can input the data category into the AI, and the AI ​​can apply an appropriate display algorithm according to the category.

[0052] The display unit can determine the display priority based on the data collection period when displaying data. For example, the display unit may prioritize displaying the latest data. For example, the display unit may display trends based on past data. For example, the display unit may adjust the display priority according to the data collection period. This allows for efficient display by determining the display priority based on the data collection period. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit may input the data collection period into the AI, and the AI ​​may determine the display priority based on the collection period.

[0053] The display unit can adjust the display order based on the relevance of the data during display. For example, the display unit can prioritize the display of highly relevant data. For example, the display unit can postpone the display of less relevant data. The display unit can adjust the display order according to the relevance of the data. This allows for efficient display by adjusting the display order based on the relevance of the data. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the relevance of the data into the AI, and the AI ​​can adjust the display order based on the relevance.

[0054] The notification unit can adjust the level of detail of notifications based on the importance of the data when sending notifications. For example, the notification unit can provide detailed notifications for high-importance data, and simplified notifications for low-importance data. The notification unit can also determine the priority of notifications based on the importance of the data. This allows for efficient notifications by adjusting the level of detail of notifications according to the importance of the data. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the importance of the data into the AI, which can then adjust the level of detail of the notifications based on the importance.

[0055] The notification unit can apply different notification algorithms depending on the data category when a notification is sent. For example, the notification unit can apply a natural language processing algorithm to text data. For example, the notification unit can apply an image analysis algorithm to image data. For example, the notification unit can apply an audio analysis algorithm to audio data. This allows for the application of an appropriate notification algorithm according to the data category, enabling highly visible notifications. Some or all of the above-described processing in the notification unit may be performed using AI or not. For example, the notification unit can input the data category into the AI, and the AI ​​can apply an appropriate notification algorithm according to the category.

[0056] The notification unit can determine the priority of notifications based on the data collection period when a notification is sent. For example, the notification unit may prioritize notifications of the latest data. For example, the notification unit may notify of trends based on past data. For example, the notification unit may adjust the priority of notifications according to the data collection period. This enables efficient notifications by determining the priority of notifications based on the data collection period. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may input the data collection period into the AI, and the AI ​​may determine the priority of notifications based on the collection period.

[0057] The notification unit can adjust the order of notifications based on the relevance of the data when sending notifications. For example, the notification unit may prioritize notifications for highly relevant data. For example, the notification unit may postpone notifications for less relevant data. The notification unit can adjust the order of notifications according to the relevance of the data. This allows for efficient notifications by adjusting the order of notifications based on the relevance of the data. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of notifications based on the relevance.

[0058] The dialogue unit can adjust the level of detail in the dialogue based on the importance of the data during the dialogue. For example, the dialogue unit can conduct a detailed dialogue for data of high importance. For example, the dialogue unit can conduct a simplified dialogue for data of low importance. For example, the dialogue unit can determine the priority of the dialogue according to the importance of the data. This allows for efficient dialogue by adjusting the level of detail in the dialogue according to the importance of the data. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail in the dialogue based on the importance.

[0059] The dialogue unit can apply different dialogue algorithms depending on the data category during a dialogue. For example, the dialogue unit can apply a natural language processing algorithm to text data. For example, the dialogue unit can apply an image analysis algorithm to image data. For example, the dialogue unit can apply a speech analysis algorithm to speech data. This allows for the application of an appropriate dialogue algorithm according to the data category, enabling highly understandable dialogue. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the data category into the AI, and the AI ​​can apply an appropriate dialogue algorithm according to the category.

[0060] The dialogue unit can determine the priority of conversations based on the data collection timing during a conversation. For example, the dialogue unit can prioritize the use of the most recent data in the conversation. For example, the dialogue unit can reflect trends in the conversation based on past data. For example, the dialogue unit can adjust the priority of conversations according to the data collection timing. This enables efficient conversations by determining the priority of conversations based on the data collection timing. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the data collection timing into the AI, and the AI ​​can determine the priority of conversations based on the collection timing.

[0061] The dialogue unit can adjust the order of dialogue based on the relevance of the data during the dialogue. For example, the dialogue unit can prioritize the use of highly relevant data in the dialogue. For example, the dialogue unit can postpone the use of less relevant data. The dialogue unit can adjust the order of dialogue according to the relevance of the data. This allows for efficient dialogue by adjusting the order of dialogue based on the relevance of the data. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of dialogue based on the relevance.

[0062] The competitive analysis unit can adjust the level of detail of the competitive analysis based on the importance of the data during the competitive analysis. For example, the competitive analysis unit can perform a detailed competitive analysis on data with high importance. For example, the competitive analysis unit can perform a simplified competitive analysis on data with low importance. For example, the competitive analysis unit can determine the priority of the competitive analysis according to the importance of the data. This allows for efficient competitive analysis by adjusting the level of detail of the competitive analysis according to the importance of the data. Some or all of the above processes in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the competitive analysis based on the importance.

[0063] The competitive analysis unit can apply different competitive analysis algorithms depending on the data category during competitive analysis. For example, the competitive analysis unit can apply a natural language processing algorithm to text data. For example, the competitive analysis unit can apply an image analysis algorithm to image data. For example, the competitive analysis unit can apply an audio analysis algorithm to audio data. This allows for the application of an appropriate competitive analysis algorithm according to the data category, enabling highly visual competitive analysis. Some or all of the above-described processes in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input the data category into the AI, which can then apply an appropriate competitive analysis algorithm according to the category.

[0064] The competitive analysis unit can determine the priority of competitive analysis based on the data collection timing during competitive analysis. For example, the competitive analysis unit can prioritize the use of the latest data in competitive analysis. For example, the competitive analysis unit can reflect trends in competitive analysis based on historical data. For example, the competitive analysis unit can adjust the priority of competitive analysis according to the data collection timing. This enables efficient competitive analysis by determining the priority of competitive analysis based on the data collection timing. Some or all of the above processes in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of competitive analysis based on the collection timing.

[0065] The competitive analysis unit can adjust the order of competitive analysis based on the relevance of the data during the competitive analysis. For example, the competitive analysis unit can prioritize the use of highly relevant data in the competitive analysis. For example, the competitive analysis unit can postpone the use of less relevant data. For example, the competitive analysis unit can adjust the order of competitive analysis according to the relevance of the data. This allows for efficient competitive analysis by adjusting the order of competitive analysis based on the relevance of the data. Some or all of the above processing in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of competitive analysis based on the relevance.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The data collection unit can analyze the user's past behavior history during data collection and prioritize the collection of highly relevant data. For example, if a user has shown interest in a particular topic in the past, it will prioritize the collection of data related to that topic. Furthermore, it can adjust the type and timing of data collection based on the user's behavior history. This enables efficient data collection based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0068] The data collection unit can adjust the collection frequency while considering the battery status of the user's device. For example, if the battery is low, the collection frequency can be reduced to conserve battery power. Conversely, if the battery is sufficiently charged, the collection frequency can be increased to collect more detailed data. The type of data collected can also be adjusted according to the battery status. This enables efficient data collection based on the device's battery status. Some or all of the above-described processes in the data collection unit may be performed using AI or not.

[0069] The analysis unit can adjust the level of detail of the analysis based on the reliability of the data. For example, it can perform a detailed analysis on highly reliable data and a simplified analysis on less reliable data. It can also determine the priority of the analysis according to the reliability of the data. This enables efficient analysis based on the reliability of the data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI.

[0070] The display unit can adjust the displayed content according to the user's screen size during display. For example, it can display detailed information on a large screen and concise information on a small screen. It can also adjust the size of displayed graphs and charts according to the screen resolution. This enables a display optimized for the user's device. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI.

[0071] The notification unit can adjust the notification method based on the user's current activity when a notification is sent. For example, if the user is in a meeting, notifications can be switched to silent mode, and if the user is driving, voice notifications can be prioritized. Also, if the user is relaxed, detailed notifications can be provided. This allows for the provision of the most appropriate notification method according to the user's current activity. Some or all of the above processing in the notification unit may be performed using AI or not.

[0072] The dialogue unit can adjust the content of the conversation based on the user's language settings during the conversation. For example, if the user selects English, the dialogue unit will conduct the conversation in English; if the user selects Japanese, it will conduct the conversation in Japanese. It can also adjust the difficulty level of the conversation according to the user's language level. This enables optimal conversation according to the user's language settings. Some or all of the above processing in the dialogue unit may be performed using AI, or it may be performed without using AI.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The collection unit collects data. The collection unit collects data from sources such as social media, forums, and news sites. For example, the collection unit can collect posts from social media, comments from forums, articles from news sites, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data and monitors changes in evaluation in real time. For example, the analysis unit can analyze data trends and generate change graphs. Step 3: The display unit displays the trend analysis and transition graph based on the analysis results obtained by the analysis unit. The display unit can, for example, display the results of the trend analysis as a line graph or a bar graph. Step 4: The notification unit notifies of anomaly evaluations based on the trend analysis and change graphs displayed by the display unit. For example, the notification unit can issue an alert when it detects an abnormal change in evaluation. Step 5: The dialogue unit provides interactive analysis functions based on the analysis results obtained by the analysis unit. For example, the dialogue unit allows users to check evaluation details, frequency of occurrence, identification of target products, and related factors on a dashboard. Step 6: The competitive analysis department analyzes competitor reviews based on the data collected by the data collection department. For example, the competitive analysis department can analyze competitor reviews and visualize how they are being evaluated compared to the company's own products and services.

[0075] (Example of form 2) The analysis system according to an embodiment of the present invention is a powerful analytical tool for business owners and marketers, and is a system that aggregates and analyzes customer feedback over time. The purpose of this analysis system is to aggregate and analyze customer feedback over time. Specifically, it consists of the following steps: First, an AI agent aggregates data from social media, forums, and news sites and analyzes comprehensive customer voices in real time. Next, it monitors changes in evaluations in real time and responds quickly to market trends and consumer satisfaction by providing trend analysis and change graphs, alert notifications for anomaly evaluations, and interactive analysis functions. Furthermore, it displays not only the content of the evaluations but also the frequency of occurrence, identification of the target product, and related factors on the dashboard immediately to encourage quick response. It also analyzes competitor reviews and visualizes relative evaluations and advantages. For example, the AI ​​agent collects data from social media, forums, and news sites. In this process, it collects detailed data such as customer feedback, reviews, and comments. For example, by collecting customer evaluations and comments on a specific product and having the AI ​​analyze them, it is possible to understand customer voices. Next, the AI ​​analyzes the collected data and displays trend analysis and change graphs. The AI ​​analyzes collected data and monitors changes in ratings in real time. For example, if the rating for a particular product changes drastically, it displays the change in a graph and notifies the user of an alert for the anomaly. This allows business owners and marketers to respond quickly to changes in ratings. Furthermore, it provides interactive analytical capabilities. Users can instantly view rating details, frequency, target products, and related factors on the dashboard. For example, if the rating for a particular product declines, the cause can be identified and countermeasures taken quickly. It also analyzes competitor reviews and visualizes relative ratings and advantages. The AI ​​analyzes competitor reviews and visualizes how their own products and services are being evaluated compared to those of their competitors. This allows business owners and marketers to compare themselves to competitors and develop strategies.This system allows business owners and marketers to analyze customer feedback in real time and respond quickly to changes in evaluations. Furthermore, by comparing themselves to competitors, they can develop strategies and accelerate business growth. Thus, the analytics system can analyze customer feedback in real time and respond quickly to changes in evaluations.

[0076] The analysis system according to the embodiment comprises a data collection unit, an analysis unit, a display unit, a notification unit, an interaction unit, and a competitive analysis unit. The data collection unit collects data. The data collection unit collects data from, for example, social media, forums, and news sites. The data collection unit can collect, for example, posts from social media, comments on forums, and articles on news sites. The analysis unit analyzes the data collected by the data collection unit. The analysis unit can, for example, analyze the collected data and monitor changes in evaluation in real time. The analysis unit can, for example, analyze data trends and generate change graphs. The display unit displays the trend analysis and change graphs based on the analysis results obtained by the analysis unit. The display unit can, for example, display the results of the trend analysis as line graphs or bar graphs. The notification unit notifies of anomaly evaluation alerts based on the trend analysis and change graphs displayed by the display unit. The notification unit can, for example, issue an alert when it detects an abnormal change in evaluation. The interaction unit provides interactive analysis functions based on the analysis results obtained by the analysis unit. The dialogue unit allows users to, for example, view evaluation details, frequency of occurrence, identify target products, and check related factors on a dashboard. The competitive analysis unit analyzes competitor reviews based on data collected by the data collection unit. The competitive analysis unit can, for example, analyze competitor reviews and visualize how their products and services are being evaluated compared to those of the user's own company. As a result, the analysis system according to this embodiment can analyze customer feedback in real time and respond quickly to changes in evaluations.

[0077] The data collection unit collects data from sources such as social media, forums, and news sites. Specifically, it uses social media APIs to retrieve content such as posts, comments, images, and videos. From forums, it collects threads and comment content using scraping techniques. From news sites, it collects article titles, body text, publication date, and author information. This data is centrally managed by the data collection unit and stored in a database. The data collection unit can flexibly set the frequency and scope of data collection, and can also filter data based on specific keywords or hashtags. For example, it can be set to collect only posts and comments related to a specific product name or brand name. This allows the data collection unit to efficiently collect data from a wide range of data sources and provide it to the analysis unit. Furthermore, the data collection unit is equipped with preprocessing functions to remove data duplication and noise, thus maintaining the quality of the collected data.

[0078] The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit analyzes the collected data and monitors changes in ratings in real time. Specifically, it uses natural language processing (NLP) techniques to perform sentiment analysis and topic modeling on text data. In sentiment analysis, it automatically classifies ratings into positive, negative, and neutral, and tracks changes in ratings over time. In topic modeling, it uses methods such as LDA (Latent Dirichlet Allocation) to extract key topics in the data and grasp trends. Based on these analysis results, the analysis unit can generate change graphs and heatmaps to visually display changes and trends in ratings. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual changes in ratings in real time and issue early warnings. This allows the analysis unit to quickly and accurately analyze the collected data and monitor changes in ratings in real time.

[0079] The display unit displays trend analysis and change graphs based on the analysis results obtained by the analysis unit. For example, the display unit can display the results of the trend analysis as line graphs or bar graphs. Specifically, it provides a dashboard on the user interface to visually display the analysis results. Line graphs show changes in evaluations along a time axis, allowing users to grasp the evaluation trend over a specific period at a glance. Bar graphs make it easier to compare evaluations across different categories or topics. Furthermore, heatmaps can be used to visually show the concentration of evaluations over a specific period or topic. The display unit also provides a function that allows users to customize graphs and charts, enabling them to focus on or filter specific data points. This allows the display unit to communicate the analysis results to the user intuitively and effectively.

[0080] The notification unit alerts users to anomaly evaluations based on the trend analysis and change graphs displayed by the display unit. For example, the notification unit can issue an alert when it detects an anomaly in evaluation. Specifically, it monitors anomaly evaluation changes detected by the anomaly detection algorithm in real time and immediately notifies the user when an anomaly occurs. Notifications are made using multiple means, such as email, SMS, and push notifications, to enable users to respond quickly. The notification unit can set notification priorities according to the importance of the alert; important alerts are notified immediately, while relatively minor alerts can be compiled into periodic reports. In this way, the notification unit helps users respond quickly to anomaly evaluation changes.

[0081] The dialogue unit provides interactive analysis functions based on the analysis results obtained by the analysis unit. For example, the dialogue unit allows users to check evaluation details, frequency of occurrence, identification of target products, and related factors on a dashboard. Specifically, when a user inputs a question in natural language, the dialogue unit responds with appropriate analysis results. For example, in response to a question such as, "What was the highest-rated product in the past month?", the dialogue unit provides information on the relevant product based on the analysis results. The dialogue unit can generate answers in natural language to user questions using generative AI. This allows users to intuitively interact with the system and quickly obtain the necessary information. Furthermore, the dialogue unit can learn from the user's past question history and provide more accurate answers. In this way, the dialogue unit provides flexible analysis functions tailored to user needs and supports user decision-making.

[0082] The Competitive Analysis Department analyzes competitor reviews based on data collected by the Data Collection Department. For example, the Competitive Analysis Department can analyze competitor reviews and visualize how they are evaluated compared to the company's own products and services. Specifically, it collects competitor reviews and performs sentiment analysis and topic modeling using natural language processing technology. This allows the company to understand the trends and key topics in the evaluations of competitors' products and services. Based on these analysis results, the Competitive Analysis Department compares the evaluations of the company's own products and services with those of competitors to clarify its strengths and weaknesses. Furthermore, the Competitive Analysis Department can monitor changes in competitor evaluations in real time and quickly grasp new trends and market developments. As a result, the Competitive Analysis Department can provide valuable information that is useful for the company's strategic planning and marketing activities.

[0083] The data collection unit can collect data from social media, forums, and news sites. For example, the data collection unit can collect posts from social media. For example, the data collection unit can also collect comments from forums. For example, the data collection unit can also collect articles from news sites. This allows for the collection of information from diverse data sources and the analysis of comprehensive customer voices. Some or all of the processing described above in the data collection unit may be performed using AI or not. For example, the data collection unit can input posts from social media into an AI, which can then analyze and collect the content of the posts.

[0084] The analysis unit can analyze the collected data and monitor changes in evaluation in real time. For example, the analysis unit can analyze the collected data and monitor changes in evaluation in real time. For example, the analysis unit can analyze data trends and generate change graphs. For example, the analysis unit can monitor changes in evaluation in real time and detect abnormal changes in evaluation. This allows for real-time understanding of changes in evaluation and rapid response. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the collected data into AI, which can analyze the data and monitor changes in evaluation.

[0085] The display unit can display trend analysis and transition graphs. For example, the display unit can display the results of the trend analysis as a line graph or bar graph. For example, the display unit can display transition graphs, allowing users to visually grasp changes in evaluation. For example, the display unit can display changes in evaluation in real time and detect abnormal changes in evaluation. This allows users to visually grasp changes in evaluation. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the analysis results obtained by the analysis unit into the AI, and the AI ​​can generate and display the trend analysis and transition graphs.

[0086] The notification unit can issue alerts for abnormal evaluations. For example, the notification unit can issue an alert when it detects an abnormal change in evaluation. For example, the notification unit can issue an alert when the change in evaluation is rapid. For example, the notification unit can issue an alert when the change in evaluation exceeds a certain threshold. This allows for immediate identification and response to abnormal changes in evaluation. Some or all of the above-described processes in the notification unit may be performed using AI or not. For example, the notification unit can input the trend analysis and change graph displayed by the display unit into the AI, which can then generate and notify an alert for an abnormal evaluation.

[0087] The dialogue unit can provide interactive analysis functions. For example, the dialogue unit allows users to view evaluation content, frequency of occurrence, identification of target products, and related factors on a dashboard. For example, the dialogue unit allows users to monitor changes in evaluations in real time and take countermeasures. For example, the dialogue unit allows users to identify the cause of changes in evaluations and respond quickly. This enables users to interactively analyze data. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the analysis results obtained by the analysis unit into the AI, and the AI ​​can provide interactive analysis functions.

[0088] The competitive analysis department can analyze reviews of competitors and visualize their relative evaluations and advantages. For example, the competitive analysis department can analyze reviews of competitors and visualize how they are evaluated compared to the company's own products and services. For example, the competitive analysis department can analyze reviews of competitors and display their relative evaluations in a graph. For example, the competitive analysis department can analyze reviews of competitors and visualize their advantages. This allows for comparisons with competitors and the development of strategies. Some or all of the above processes in the competitive analysis department may be performed using AI or not. For example, the competitive analysis department can input data collected by the data collection department into an AI, which can then analyze reviews of competitors and visualize their relative evaluations and advantages.

[0089] 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 burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed information. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This allows for efficient data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the timing of data collection.

[0090] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection timing from past data collection history. For example, the data collection unit can determine the priority of data to be collected based on past data collection history. For example, the data collection unit can analyze past data collection history and identify areas for improvement in the collection method. This allows the optimal collection method to be selected based on past data collection history. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input past data collection history into AI, and the AI ​​can select the optimal collection method.

[0091] The data collection unit can filter data based on the user's current areas of interest during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. For example, the data collection unit can filter out unnecessary data based on the user's areas of interest. For example, the data collection unit can adjust the types of data it collects in response to changes in the user's areas of interest. This allows the data to be filtered based on the user's areas of interest and highly relevant data to be collected. 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 the user's areas of interest into the AI, and the AI ​​can filter the data based on those areas of interest.

[0092] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting only important data. For example, if the user is relaxed, the data collection unit can prioritize collecting detailed data. For example, if the user is in a hurry, the data collection unit can prioritize data that can be collected quickly. This allows for efficient data collection by determining the priority of data to collect according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI, which can estimate the emotions and determine the priority of data to collect.

[0093] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. For example, the data collection unit can collect region-specific data based on the user's geographical location information. For example, the data collection unit can collect highly relevant data by considering the user's travel history. This allows for the collection of highly relevant data based on the user's geographical location information. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input the user's geographical location information into the AI, which can then prioritize the collection of highly relevant data.

[0094] 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 collect data related to topics the user has shown interest in on social media. For example, the data collection unit can prioritize the collection of highly relevant data based on the user's social media activity. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. This allows for the collection of highly relevant data based on the user's social media activity. 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 the user's social media activity into AI, which can then collect relevant data.

[0095] 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 tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows the presentation of the analysis to be adjusted according to the user's emotions, providing an easy-to-understand analysis result. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the presentation of the analysis.

[0096] 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 perform a simplified analysis on data with low importance. For example, the analysis unit can determine the priority of the analysis according to 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 or not. For example, the analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the analysis based on the importance.

[0097] 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 can apply an image analysis algorithm to image data. For example, the analysis unit can apply a speech analysis algorithm to speech data. This allows for the application of an appropriate analysis algorithm according to the data category, enabling highly accurate analysis. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may be performed without AI. For example, the analysis unit can input the data category into the AI, and the AI ​​can apply an appropriate analysis algorithm according to the category.

[0098] 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 perform a short, concise analysis. If the user is relaxed, the analysis unit can perform a detailed analysis. If the user is excited, the analysis unit can perform a visually stimulating analysis. This allows for efficient analysis by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the length of the analysis.

[0099] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may analyze trends based on historical data. For example, the analysis unit may adjust the priority of analysis according to the data collection timing. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of analysis based on the collection timing.

[0100] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant data. For example, the analysis unit may postpone the analysis of less relevant data. The analysis unit can adjust the order of analysis according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of analysis based on the relevance.

[0101] The display unit can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the display unit can provide a simple and highly visible display method. For example, if the user is relaxed, the display unit can provide a display method that includes detailed information. For example, if the user is in a hurry, the display unit can provide a display method that gets straight to the point. This allows for a highly visible display by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the display method.

[0102] The display unit can adjust the level of detail of the display based on the importance of the data during display. For example, the display unit can provide detailed display for highly important data, and simplified display for less important data. The display unit can also determine the display priority according to the importance of the data. This allows for efficient display by adjusting the level of detail according to the importance of the data. Some or all of the above processing in the display unit may be performed using AI, or not. For example, the display unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the display based on the importance.

[0103] The display unit can apply different display algorithms depending on the data category during display. For example, the display unit can apply a natural language processing algorithm to text data. For example, the display unit can apply an image analysis algorithm to image data. For example, the display unit can apply an audio analysis algorithm to audio data. This allows for the application of an appropriate display algorithm according to the data category, enabling highly legible display. Some or all of the above-described processes in the display unit may be performed using AI, or they may not be performed using AI. For example, the display unit can input the data category into the AI, and the AI ​​can apply an appropriate display algorithm according to the category.

[0104] The display unit can estimate the user's emotions and adjust the length of the display based on the estimated emotions. For example, if the user is in a hurry, the display unit can provide a short, concise display. For example, if the user is relaxed, the display unit can provide a detailed display. For example, if the user is excited, the display unit can provide a visually stimulating display. This allows for efficient display by adjusting the length of the display according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. The 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 display unit may be performed using AI or not. For example, the display unit can input user emotion data into the generative AI, which can estimate the emotion and adjust the length of the display.

[0105] The display unit can determine the display priority based on the data collection period when displaying data. For example, the display unit may prioritize displaying the latest data. For example, the display unit may display trends based on past data. For example, the display unit may adjust the display priority according to the data collection period. This allows for efficient display by determining the display priority based on the data collection period. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit may input the data collection period into the AI, and the AI ​​may determine the display priority based on the collection period.

[0106] The display unit can adjust the display order based on the relevance of the data during display. For example, the display unit can prioritize the display of highly relevant data. For example, the display unit can postpone the display of less relevant data. The display unit can adjust the display order according to the relevance of the data. This allows for efficient display by adjusting the display order based on the relevance of the data. Some or all of the above processing in the display unit may be performed using AI or not. For example, the display unit can input the relevance of the data into the AI, and the AI ​​can adjust the display order based on the relevance.

[0107] The notification unit can estimate the user's emotions and adjust the notification method based on the estimated emotions. For example, if the user is stressed, the notification unit can provide a simple and highly visible notification method. For example, if the user is relaxed, the notification unit can provide a notification method that includes detailed information. For example, if the user is in a hurry, the notification unit can provide a notification method that gets straight to the point. This allows for notifications to be adjusted according to the user's emotions, resulting in highly visible notifications. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the notification method.

[0108] The notification unit can adjust the level of detail of notifications based on the importance of the data when sending notifications. For example, the notification unit can provide detailed notifications for high-importance data, and simplified notifications for low-importance data. The notification unit can also determine the priority of notifications based on the importance of the data. This allows for efficient notifications by adjusting the level of detail of notifications according to the importance of the data. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the importance of the data into the AI, which can then adjust the level of detail of the notifications based on the importance.

[0109] The notification unit can apply different notification algorithms depending on the data category when a notification is sent. For example, the notification unit can apply a natural language processing algorithm to text data. For example, the notification unit can apply an image analysis algorithm to image data. For example, the notification unit can apply an audio analysis algorithm to audio data. This allows for the application of an appropriate notification algorithm according to the data category, enabling highly visible notifications. Some or all of the above-described processing in the notification unit may be performed using AI or not. For example, the notification unit can input the data category into the AI, and the AI ​​can apply an appropriate notification algorithm according to the category.

[0110] The notification unit can estimate the user's emotions and adjust the length of the notification based on the estimated emotions. For example, if the user is in a hurry, the notification unit can provide a short, to-the-point notification. If the user is relaxed, the notification unit can provide a detailed notification. If the user is excited, the notification unit can provide a visually stimulating notification. This allows for efficient notification by adjusting the length of the notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The 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 notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into the generative AI, which can estimate the emotion and adjust the length of the notification.

[0111] The notification unit can determine the priority of notifications based on the data collection period when a notification is sent. For example, the notification unit may prioritize notifications of the latest data. For example, the notification unit may notify of trends based on past data. For example, the notification unit may adjust the priority of notifications according to the data collection period. This enables efficient notifications by determining the priority of notifications based on the data collection period. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit may input the data collection period into the AI, and the AI ​​may determine the priority of notifications based on the collection period.

[0112] The notification unit can adjust the order of notifications based on the relevance of the data when sending notifications. For example, the notification unit may prioritize notifications for highly relevant data. For example, the notification unit may postpone notifications for less relevant data. The notification unit can adjust the order of notifications according to the relevance of the data. This allows for efficient notifications by adjusting the order of notifications based on the relevance of the data. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of notifications based on the relevance.

[0113] The dialogue unit can estimate the user's emotions and adjust the dialogue method based on the estimated emotions. For example, if the user is nervous, the dialogue unit can provide a simple and easy-to-understand dialogue method. For example, if the user is relaxed, the dialogue unit can provide a dialogue method that includes detailed information. For example, if the user is in a hurry, the dialogue unit can provide a dialogue method that gets straight to the point. This allows for a highly understandable dialogue by adjusting the dialogue method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the dialogue method.

[0114] The dialogue unit can adjust the level of detail in the dialogue based on the importance of the data during the dialogue. For example, the dialogue unit can conduct a detailed dialogue for data of high importance. For example, the dialogue unit can conduct a simplified dialogue for data of low importance. For example, the dialogue unit can determine the priority of the dialogue according to the importance of the data. This allows for efficient dialogue by adjusting the level of detail in the dialogue according to the importance of the data. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail in the dialogue based on the importance.

[0115] The dialogue unit can apply different dialogue algorithms depending on the data category during a dialogue. For example, the dialogue unit can apply a natural language processing algorithm to text data. For example, the dialogue unit can apply an image analysis algorithm to image data. For example, the dialogue unit can apply a speech analysis algorithm to speech data. This allows for the application of an appropriate dialogue algorithm according to the data category, enabling highly understandable dialogue. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the data category into the AI, and the AI ​​can apply an appropriate dialogue algorithm according to the category.

[0116] The dialogue unit can estimate the user's emotions and adjust the length of the dialogue based on the estimated emotions. For example, if the user is in a hurry, the dialogue unit will conduct a short, to-the-point conversation. For example, if the user is relaxed, the dialogue unit can conduct a detailed conversation. For example, if the user is excited, the dialogue unit can conduct a visually stimulating conversation. This allows for efficient dialogue by adjusting the length of the conversation according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input user emotion data into a generative AI, which can estimate the emotion and adjust the length of the dialogue.

[0117] The dialogue unit can determine the priority of conversations based on the data collection timing during a conversation. For example, the dialogue unit can prioritize the use of the most recent data in the conversation. For example, the dialogue unit can reflect trends in the conversation based on past data. For example, the dialogue unit can adjust the priority of conversations according to the data collection timing. This enables efficient conversations by determining the priority of conversations based on the data collection timing. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the data collection timing into the AI, and the AI ​​can determine the priority of conversations based on the collection timing.

[0118] The dialogue unit can adjust the order of dialogue based on the relevance of the data during the dialogue. For example, the dialogue unit can prioritize the use of highly relevant data in the dialogue. For example, the dialogue unit can postpone the use of less relevant data. The dialogue unit can adjust the order of dialogue according to the relevance of the data. This allows for efficient dialogue by adjusting the order of dialogue based on the relevance of the data. Some or all of the above processing in the dialogue unit may be performed using AI or not. For example, the dialogue unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of dialogue based on the relevance.

[0119] The competitive analysis unit can estimate the user's emotions and adjust the competitive analysis method based on the estimated user emotions. For example, if the user is nervous, the competitive analysis unit can provide a simple and easy-to-understand competitive analysis method. For example, if the user is relaxed, the competitive analysis unit can provide a competitive analysis method that includes detailed information. For example, if the user is in a hurry, the competitive analysis unit can provide a concise competitive analysis method. This allows for a highly understandable competitive analysis by adjusting the competitive analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the competitive analysis method.

[0120] The competitive analysis unit can adjust the level of detail of the competitive analysis based on the importance of the data during the competitive analysis. For example, the competitive analysis unit can perform a detailed competitive analysis on data with high importance. For example, the competitive analysis unit can perform a simplified competitive analysis on data with low importance. For example, the competitive analysis unit can determine the priority of the competitive analysis according to the importance of the data. This allows for efficient competitive analysis by adjusting the level of detail of the competitive analysis according to the importance of the data. Some or all of the above processes in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input the importance of the data into the AI, and the AI ​​can adjust the level of detail of the competitive analysis based on the importance.

[0121] The competitive analysis unit can apply different competitive analysis algorithms depending on the data category during competitive analysis. For example, the competitive analysis unit can apply a natural language processing algorithm to text data. For example, the competitive analysis unit can apply an image analysis algorithm to image data. For example, the competitive analysis unit can apply an audio analysis algorithm to audio data. This allows for the application of an appropriate competitive analysis algorithm according to the data category, enabling highly visual competitive analysis. Some or all of the above-described processes in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input the data category into the AI, which can then apply an appropriate competitive analysis algorithm according to the category.

[0122] The competitive analysis unit can estimate the user's emotions and adjust the length of the competitive analysis based on the estimated emotions. For example, if the user is in a hurry, the competitive analysis unit can perform a short, concise competitive analysis. If the user is relaxed, the competitive analysis unit can perform a detailed competitive analysis. If the user is excited, the competitive analysis unit can perform a visually stimulating competitive analysis. This allows for efficient competitive analysis by adjusting the length of the competitive analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input user emotion data into a generative AI, which can then estimate the emotions and adjust the length of the competitive analysis.

[0123] The competitive analysis unit can determine the priority of competitive analysis based on the data collection timing during competitive analysis. For example, the competitive analysis unit can prioritize the use of the latest data in competitive analysis. For example, the competitive analysis unit can reflect trends in competitive analysis based on historical data. For example, the competitive analysis unit can adjust the priority of competitive analysis according to the data collection timing. This enables efficient competitive analysis by determining the priority of competitive analysis based on the data collection timing. Some or all of the above processes in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input the data collection timing into the AI, and the AI ​​can determine the priority of competitive analysis based on the collection timing.

[0124] The competitive analysis unit can adjust the order of competitive analysis based on the relevance of the data during the competitive analysis. For example, the competitive analysis unit can prioritize the use of highly relevant data in the competitive analysis. For example, the competitive analysis unit can postpone the use of less relevant data. For example, the competitive analysis unit can adjust the order of competitive analysis according to the relevance of the data. This allows for efficient competitive analysis by adjusting the order of competitive analysis based on the relevance of the data. Some or all of the above processing in the competitive analysis unit may be performed using AI or not. For example, the competitive analysis unit can input the relevance of the data into the AI, and the AI ​​can adjust the order of competitive analysis based on the relevance.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The analysis unit can estimate the user's emotions and adjust the depth of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a concise analysis result, while if the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. This allows for adjusting the depth of the analysis according to the user's emotions, enabling the provision of information that is optimal for the user. Emotion estimation is achieved using an emotion engine or generative AI, among other things. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0127] The data collection unit can analyze the user's past behavior history during data collection and prioritize the collection of highly relevant data. For example, if a user has shown interest in a particular topic in the past, it will prioritize the collection of data related to that topic. Furthermore, it can adjust the type and timing of data collection based on the user's behavior history. This enables efficient data collection based on the user's past behavior history. Some or all of the above processing in the data collection unit may be performed using AI or not.

[0128] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and easy-to-understand analysis results, while if the user is relaxed, it can provide detailed analysis results. Furthermore, if the user is in a hurry, it can provide concise analysis results. This allows for the presentation method of analysis results to be adjusted according to the user's emotions, enabling the provision of optimal information to the user. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0129] The display unit can estimate the user's emotions and adjust the display layout based on those emotions. For example, if the user is stressed, the display unit can provide a simple and highly visible layout; if the user is relaxed, it can provide a layout containing detailed information; and if the user is in a hurry, it can provide a layout that gets straight to the point. This allows the display layout to be adjusted according to the user's emotions, enabling the provision of information that is optimal for the user. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0130] The notification unit can estimate the user's emotions and adjust the timing of notifications based on those emotions. For example, if the user is stressed, the notification unit can reduce the frequency of notifications, and if the user is relaxed, it can increase the frequency. Also, if the user is in a hurry, it can prioritize only important notifications. This allows for the timing of notifications to be adjusted according to the user's emotions, enabling the provision of information that is optimal for the user. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI.

[0131] The data collection unit can adjust the collection frequency while considering the battery status of the user's device. For example, if the battery is low, the collection frequency can be reduced to conserve battery power. Conversely, if the battery is sufficiently charged, the collection frequency can be increased to collect more detailed data. The type of data collected can also be adjusted according to the battery status. This enables efficient data collection based on the device's battery status. Some or all of the above-described processes in the data collection unit may be performed using AI or not.

[0132] The analysis unit can adjust the level of detail of the analysis based on the reliability of the data. For example, it can perform a detailed analysis on highly reliable data and a simplified analysis on less reliable data. It can also determine the priority of the analysis according to the reliability of the data. This enables efficient analysis based on the reliability of the data. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without using AI.

[0133] The display unit can adjust the displayed content according to the user's screen size during display. For example, it can display detailed information on a large screen and concise information on a small screen. It can also adjust the size of displayed graphs and charts according to the screen resolution. This enables a display optimized for the user's device. Some or all of the above processing in the display unit may be performed using AI, or it may be performed without AI.

[0134] The notification unit can adjust the notification method based on the user's current activity when a notification is sent. For example, if the user is in a meeting, notifications can be switched to silent mode, and if the user is driving, voice notifications can be prioritized. Also, if the user is relaxed, detailed notifications can be provided. This allows for the provision of the most appropriate notification method according to the user's current activity. Some or all of the above processing in the notification unit may be performed using AI or not.

[0135] The dialogue unit can adjust the content of the conversation based on the user's language settings during the conversation. For example, if the user selects English, the dialogue unit will conduct the conversation in English; if the user selects Japanese, it will conduct the conversation in Japanese. It can also adjust the difficulty level of the conversation according to the user's language level. This enables optimal conversation according to the user's language settings. Some or all of the above processing in the dialogue unit may be performed using AI, or it may be performed without using AI.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The collection unit collects data. The collection unit collects data from sources such as social media, forums, and news sites. For example, the collection unit can collect posts from social media, comments from forums, articles from news sites, etc. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data and monitors changes in evaluation in real time. For example, the analysis unit can analyze data trends and generate change graphs. Step 3: The display unit displays the trend analysis and transition graph based on the analysis results obtained by the analysis unit. The display unit can, for example, display the results of the trend analysis as a line graph or a bar graph. Step 4: The notification unit notifies of anomaly evaluations based on the trend analysis and change graphs displayed by the display unit. For example, the notification unit can issue an alert when it detects an abnormal change in evaluation. Step 5: The dialogue unit provides interactive analysis functions based on the analysis results obtained by the analysis unit. For example, the dialogue unit allows users to check evaluation details, frequency of occurrence, identification of target products, and related factors on a dashboard. Step 6: The competitive analysis department analyzes competitor reviews based on the data collected by the data collection department. For example, the competitive analysis department can analyze competitor reviews and visualize how they are being evaluated compared to the company's own products and services.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the data collection unit, analysis unit, display unit, notification unit, dialogue unit, and competitive analysis unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data from social media, forums, and news sites using the control unit 46A of the smart device 14. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and monitors changes in evaluation in real time. The display unit displays trend analysis and change graphs using the display 40A of the smart device 14. The notification unit notifies of anomaly evaluation alerts using the control unit 46A of the smart device 14. The dialogue unit provides interactive analysis functions using the control unit 46A of the smart device 14. The competitive analysis unit analyzes competitor reviews using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the data collection unit, analysis unit, display unit, notification unit, dialogue unit, and competitive analysis unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects data from social media, forums, and news sites by the control unit 46A of the smart glasses 214. The analysis unit analyzes the collected data by the identification processing unit 290 of the data processing unit 12 and monitors changes in evaluation in real time. The display unit displays trend analysis and change graphs by the display of the smart glasses 214. The notification unit notifies of anomaly evaluation alerts by the control unit 46A of the smart glasses 214. The dialogue unit provides interactive analysis functions by the control unit 46A of the smart glasses 214. The competitive analysis unit analyzes competitor reviews by the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the data collection unit, analysis unit, display unit, notification unit, dialogue unit, and competitive analysis unit, is implemented in at least one of the following: the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data from social media, forums, and news sites using the control unit 46A of the headset terminal 314. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and monitors changes in evaluation in real time. The display unit displays trend analysis and change graphs using the display 343 of the headset terminal 314. The notification unit notifies users of anomaly evaluation alerts using the control unit 46A of the headset terminal 314. The dialogue unit provides interactive analysis functions using the control unit 46A of the headset terminal 314. The competitive analysis unit analyzes competitor reviews using the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the data collection unit, analysis unit, display unit, notification unit, dialogue unit, and competitive analysis unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects data from social media, forums, and news sites by the control unit 46A of the robot 414. The analysis unit analyzes the collected data by, for example, the identification processing unit 290 of the data processing unit 12 and monitors changes in evaluation in real time. The display unit displays trend analysis and change graphs by, for example, the display of the robot 414. The notification unit notifies, for example, of anomaly evaluation alerts by the control unit 46A of the robot 414. The dialogue unit provides interactive analysis functions by, for example, the control unit 46A of the robot 414. The competitive analysis unit analyzes reviews of competitors by, for example, the identification processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, A display unit that displays trend analysis and transition graphs based on the analysis results obtained by the aforementioned analysis unit, A notification unit that notifies an alert for an anomaly based on the trend analysis and change graph displayed by the aforementioned display unit, A dialogue unit provides an interactive analysis function based on the analysis results obtained by the aforementioned analysis unit, The system includes a competitive analysis unit that analyzes competitor reviews based on data collected by the aforementioned data collection unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data from social media, forums, and news sites. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected data is analyzed, and changes in evaluation are monitored in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned display unit is Display trend analysis and change graphs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Notify alerts for anomaly assessments. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned dialogue unit, Provides interactive analysis capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned competitive analysis unit, Analyze reviews from competitors to visualize relative evaluations and competitive advantages. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant data based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned display unit is It estimates the user's emotions and adjusts the display method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned display unit is When displaying data, 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 22) The aforementioned display unit is When displaying data, different display algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned display unit is It estimates the user's emotions and adjusts the display length based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned display unit is When displaying data, the display priority is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is When displaying data, adjust the display order based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned notification unit, It estimates the user's emotions and adjusts the notification method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned notification unit, When a notification is sent, 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 28) The aforementioned notification unit, When sending notifications, different notification algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned notification unit, It estimates the user's emotions and adjusts the length of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, When sending notifications, the system prioritizes notifications based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, adjust the order of notifications based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the way it interacts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned dialogue unit, During the conversation, 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 34) The aforementioned dialogue unit, During interaction, different dialogue algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned dialogue unit, It estimates the user's emotions and adjusts the length of the conversation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned dialogue unit, During the dialogue, prioritize the dialogue based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned dialogue unit, During the dialogue, adjust the order of the dialogue based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned competitive analysis unit, We estimate user sentiment and adjust the competitive analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned competitive analysis unit, During competitive 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 40) The aforementioned competitive analysis unit, When performing competitive analysis, different competitive analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned competitive analysis unit, It estimates user sentiment and adjusts the length of the competitive analysis based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned competitive analysis unit, When conducting competitive analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned competitive analysis unit, During competitive analysis, adjust the order of competitive analyses based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0210] 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 display unit that displays trend analysis and transition graphs based on the analysis results obtained by the aforementioned analysis unit, A notification unit that notifies an alert for an anomaly based on the trend analysis and change graph displayed by the aforementioned display unit, A dialogue unit provides an interactive analysis function based on the analysis results obtained by the aforementioned analysis unit, The system includes a competitive analysis unit that analyzes competitor reviews based on data collected by the aforementioned data collection unit. A system characterized by the following features.

2. The aforementioned collection unit is We collect data from social media, forums, and news sites. The system according to feature 1.

3. The aforementioned analysis unit, The collected data is analyzed, and changes in evaluation are monitored in real time. The system according to feature 1.

4. The aforementioned display unit is Display trend analysis and change graphs. The system according to feature 1.

5. The aforementioned notification unit, Notify alerts for anomaly assessments. The system according to feature 1.

6. The aforementioned dialogue unit, Provides interactive analysis capabilities. The system according to feature 1.

7. The aforementioned competitive analysis unit, Analyze reviews from competitors to visualize relative evaluations and competitive advantages. The system according to feature 1.

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

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

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

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