System

Abstract

An object of the system according to the embodiment is to quickly analyze daily reports and information and extract related keywords and trends.SOLUTION: A system includes a collection unit, an extraction unit, an analysis unit, and a provision unit. The collection unit collects information. The extraction unit analyzes the information collected by the collection unit and extracts a related keyword. The analysis unit analyzes a trend on the basis of the keyword extracted by the extraction unit. The providing unit displays the trend analyzed by the analysis unit through a customizable interface.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that analyzing daily reports and information takes time, making it difficult to quickly extract related keywords and trends.

[0005] The system according to the embodiment aims to quickly analyze daily reports and information and extract related keywords and trends. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an extraction unit, an analysis unit, and a provision unit. The collection unit collects information. The extraction unit analyzes the information collected by the collection unit and extracts related keywords. The analysis unit analyzes trends based on the keywords extracted by the extraction unit. The provision unit displays the trends analyzed by the analysis unit through a customizable interface. [Effects of the Invention]

[0007] The system according to the embodiment can quickly analyze daily reports and information and extract related keywords and trends. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An information analysis system according to an embodiment of the present invention utilizes generative AI technology to automatically analyze daily reports and other information and extract related keywords and trends. This information analysis system provides an easy-to-use, customizable interface, effectively realizing information sharing throughout the company. For example, a user inputs daily reports and other information into the information analysis system. The information analysis system then analyzes the input information using generative AI and extracts related keywords and trends. For example, keywords such as "new customers," "contracts concluded," and "competitors" may be extracted from the sales department's daily reports, and trends such as "increase in new customers" and "improvement in contract conclusion rate" may be identified. The extracted keywords and trends are displayed through a customizable interface. Users can customize the display content to suit their needs. For example, they can set it to display only information related to a specific department or project. Furthermore, the information analysis system effectively realizes information sharing throughout the company. The extracted keywords and trends are shared with all employees in real time, enabling centralized information management. This strengthens collaboration between departments and enables faster decision-making. The information analysis system provides an easy-to-use interface, making it easy to operate even for non-technical users. For example, users can customize the displayed content with drag and drop, or use the filter function to narrow down specific information. The generative AI also learns the user's operation history and makes suggestions to make operations more efficient in the future. For example, it can automatically apply filter settings that have been frequently used in the past, improving user convenience. This enables the information analysis system to automatically analyze daily reports and information, extract related keywords and trends, provide a customizable interface, and effectively share information throughout the company, improving business efficiency and supporting rapid decision-making.

[0029] The information analysis system according to the embodiment includes a collection unit, an extraction unit, an analysis unit, and a provision unit. The collection unit collects information. The information includes, but is not limited to, text data, image data, and audio data. The collection unit collects, for example, daily reports and business reports. The collection unit can also collect information from the Internet and acquire information from internal databases. For example, the collection unit can collect information from the Internet using web scraping technology. The collection unit can also acquire information from internal databases using API integration. The collection unit also supports information collection through manual input. For example, the collection unit collects information manually entered by a user. The extraction unit uses a generation AI to analyze the information collected by the collection unit and extract related keywords. Keyword extraction is performed based on, for example, frequency of appearance or co-occurrence relationships, but is not limited to, examples. For example, the extraction unit uses a text generation AI (e.g., LLM) to extract important keywords from information. The extraction unit can also extract keywords from image and audio data using a multimodal generation AI. The extraction unit can also use the generation AI to analyze the content of the information and extract related keywords. For example, a text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. A multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to select particularly important information from the information and extract keywords based on that information. The analysis unit analyzes trends based on the keywords extracted by the extraction unit. Trend analysis is performed, for example, based on temporal changes or trends related to a specific theme, but is not limited to such examples. The analysis unit, for example, analyzes temporal changes and identifies trends. The analysis unit can also analyze trends related to a specific theme and identify trends. The analysis unit can also use the generation AI to analyze trends based on the extracted keywords. For example, the analysis unit uses the generation AI to analyze the frequency of keyword appearance and co-occurrence relationships to identify trends.The providing unit displays the trends analyzed by the analysis unit through a customizable interface. The providing unit, for example, provides an interface that allows a user to customize the display content. For example, the providing unit provides an interface that allows the user to customize the display content using drag and drop. The providing unit can also provide an interface that allows specific information to be narrowed down using a filter function. The providing unit can also use a generation AI to learn the user's operation history and make suggestions to improve the efficiency of subsequent operations. For example, the providing unit automatically applies filter settings that have been frequently used in the past. This enables the information analysis system according to the embodiment to collect, analyze, extract keywords, analyze trends, and display information through a customizable interface. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's operation history into the generation AI and cause the generation AI to make suggestions to improve the efficiency of subsequent operations.

[0030] The collection unit can collect daily reports or information. Daily reports include, but are not limited to, business reports, progress reports, and problems. Information includes, but is not limited to, text data, image data, and audio data. The collection unit, for example, collects daily reports. For example, the collection unit collects daily reports from the sales department to understand business reports and progress reports. The collection unit can also collect daily reports from the engineering department to understand technical problems and progress reports. The collection unit can also collect daily reports from the management department to understand progress and problems in management work. In this way, collecting daily reports and information enables centralized management of information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input daily reports into a generation AI and have the generation AI execute data for analyzing the contents of the daily reports.

[0031] The extraction unit can analyze the collected information and extract related keywords. Related keywords are extracted based on, for example, frequency of appearance or co-occurrence, but are not limited to these examples. For example, the extraction unit can analyze the collected information and extract frequently appearing keywords. The extraction unit can also analyze the collected information and extract keywords based on co-occurrence. The extraction unit can also analyze the collected information and extract related keywords using a generation AI. For example, the extraction unit can extract important keywords from the information using a text generation AI (e.g., LLM). The extraction unit can also extract keywords from image and audio data using a multimodal generation AI. This facilitates information organization by extracting related keywords from the collected information. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or without AI. For example, the extraction unit can input the collected information to a generation AI and cause the generation AI to extract related keywords.

[0032] The analysis unit can analyze trends based on the extracted keywords. Trends are analyzed, for example, based on temporal changes or trends related to a specific theme, but are not limited to these examples. For example, the analysis unit can analyze temporal changes in the extracted keywords to identify trends. The analysis unit can also analyze trends related to a specific theme of the extracted keywords to identify trends. The analysis unit can also analyze trends based on the extracted keywords using a generation AI. For example, the analysis unit can use the generation AI to analyze the frequency of appearance and co-occurrence of keywords to identify trends. In this way, trends can be analyzed based on the extracted keywords to understand information trends. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the extracted keywords to the generation AI and have the generation AI perform trend analysis.

[0033] The providing unit can display the analyzed trends through a customizable interface. The customizable interface, for example, provides an interface that allows the user to customize the display content. The providing unit can provide an interface that allows the user to customize the display content, for example, by dragging and dropping. The providing unit can also provide an interface that allows the user to narrow down specific information using a filter function. The providing unit can also use a generation AI to learn the user's operation history and make suggestions to improve the efficiency of subsequent operations. For example, the providing unit automatically applies filter settings that have been frequently used in the past. This improves user convenience by displaying the analyzed trends through a customizable interface. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's operation history into the generation AI and cause the generation AI to make suggestions to improve the efficiency of subsequent operations.

[0034] The providing unit can learn the user's operation history and make suggestions to improve the efficiency of subsequent operations. The user's operation history includes, but is not limited to, for example, a click history, an input history, and a browsing history. The providing unit can, for example, learn the user's click history and make suggestions to improve the efficiency of subsequent operations. The providing unit can also learn the user's input history and make suggestions to improve the efficiency of subsequent operations. The providing unit can also learn the user's browsing history and make suggestions to improve the efficiency of subsequent operations. In this way, by learning the user's operation history, subsequent operations are made more efficient. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's operation history into a generating AI and cause the generating AI to execute suggestions to improve the efficiency of subsequent operations.

[0035] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the collection unit prioritizes the selection of a collection method that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method based on the user's past collection history. The collection unit can also select the optimal collection method for a specific time period based on the user's past collection history. In this way, the optimal collection method can be selected by analyzing the user's past information collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0036] When collecting information, the collection unit can filter the information based on the user's current project or area of ​​interest. For example, the collection unit prioritizes collecting information related to the project the user is currently working on. The collection unit can also filter and collect highly relevant information based on the user's area of ​​interest. The collection unit can also collect necessary information in a timely manner depending on the progress of the user's project. This makes it possible to collect highly relevant information by filtering information based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's project information to a generation AI and have the generation AI perform filtering.

[0037] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. This allows the collection of information to be made more efficient by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0038] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting information related to the user's current location. The collection unit can also prioritize collecting nearby information based on the user's geographical location information. The collection unit can also refer to the user's movement history to collect highly relevant information. This allows for efficient collection of necessary information by preferentially collecting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0039] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect highly relevant information. The collection unit can also refer to the activities of the user's friends on social media to collect related information. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related information.

[0040] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit can improve the collection method based on, for example, feedback provided by the user in the past. The collection unit can also adjust the type of information to be collected by referring to the user's past feedback. The collection unit can also optimize the collection timing and means by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0041] The extraction unit can adjust the level of extraction detail based on the importance of the information when extracting keywords. For example, the extraction unit extracts detailed keywords from information with high importance. The extraction unit can also extract simple keywords from information with low importance. The extraction unit can also adjust the number of keywords to be extracted depending on the importance of the information. In this way, appropriate keywords can be extracted by adjusting the level of extraction detail based on the importance of the information. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input information importance data to the generation AI and cause the generation AI to adjust the level of keyword extraction detail.

[0042] When extracting keywords, the extraction unit can apply different extraction algorithms depending on the category of information. For example, the extraction unit can apply an algorithm to extract sales-related keywords from the daily reports of the sales department. The extraction unit can also apply an algorithm to extract technology-related keywords from the daily reports of the engineering department. The extraction unit can also apply an algorithm to extract management-related keywords from the daily reports of the management department. In this way, by applying different extraction algorithms depending on the category of information, appropriate keywords can be extracted. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input information category data into the generation AI and cause the generation AI to apply a keyword extraction algorithm.

[0043] When extracting keywords, the extraction unit can improve the accuracy of the extraction by referring to the user's past extraction results. The extraction unit, for example, adjusts the extraction algorithm based on keywords previously extracted by the user. The extraction unit can also analyze the user's past extraction results to improve accuracy. The extraction unit can also extract optimal keywords by referring to the user's past extraction history. This improves the accuracy of the extraction by referring to the user's past extraction results. Some or all of the above-described processing in the extraction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the extraction unit can input the user's past extraction result data into the generation AI and cause the generation AI to improve the extraction accuracy.

[0044] When extracting keywords, the extraction unit can determine the extraction priority based on the time of information submission. For example, the extraction unit preferentially extracts keywords from the most recent information. The extraction unit can also extract highly important keywords from older information. The extraction unit can also adjust the order of keywords to be extracted depending on the time of information submission. In this way, by determining the extraction priority based on the time of information submission, the most recent information can be preferentially extracted. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input information submission time data into the generation AI and have the generation AI determine the extraction priority.

[0045] When extracting keywords, the extraction unit can adjust the extraction order based on the relevance of the information. For example, the extraction unit preferentially extracts keywords from highly relevant information. The extraction unit can also extract keywords from less relevant information later. The extraction unit can also adjust the order of keywords to be extracted depending on the relevance of the information. In this way, by adjusting the extraction order based on the relevance of the information, highly relevant information can be preferentially extracted. Some or all of the above-mentioned processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit can input information relevance data to the generation AI and have the generation AI adjust the extraction order.

[0046] When extracting keywords, the extraction unit can adjust the use of technical terms in the extraction according to the user's level of expertise. For example, if the user has technical expertise, the extraction unit extracts keywords containing technical terms. Alternatively, if the user does not have technical expertise, the extraction unit can extract keywords containing general terms. The extraction unit can also adjust the use of technical terms in the extracted keywords according to the user's level of expertise. This allows appropriate keywords to be extracted by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0047] During trend analysis, the analysis unit can predict a current trend by referring to past trend data. The analysis unit, for example, predicts a current trend based on past trend data. The analysis unit can also predict a future trend by analyzing past trend data. The analysis unit can also predict fluctuations in a current trend by referring to past trend data. In this way, a current trend can be predicted by referring to past trend data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past trend data into the generation AI and have the generation AI predict a current trend.

[0048] The analysis unit can apply different analysis methods to each information category during trend analysis. For example, the analysis unit can apply a sales-related trend analysis method to information from the sales department. The analysis unit can also apply a technology-related trend analysis method to information from the engineering department. The analysis unit can also apply a management-related trend analysis method to information from the management department. This enables appropriate trend analysis by applying different analysis methods to each information category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply the trend analysis method.

[0049] When performing trend analysis, the analysis unit can perform the analysis taking into account attribute information of the information submitter. The analysis unit performs trend analysis taking into account, for example, the position and department of the information submitter. The analysis unit can also perform trend analysis taking into account the experience and expertise of the information submitter. The analysis unit can also perform trend analysis by referring to the past submission history of the information submitter. This enables appropriate trend analysis by taking into account the attribute information of the information submitter. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input attribute data of the information submitter into the generation AI and have the generation AI perform trend analysis.

[0050] During trend analysis, the analysis unit can analyze changes in trends based on the time of information submission. The analysis unit, for example, analyzes changes in current trends based on the latest information. The analysis unit can also predict changes in trends based on past information. The analysis unit can also analyze changes in trends according to the time of information submission. This makes it possible to grasp the latest trends by analyzing changes in trends based on the time of information submission. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the time of information submission into the generation AI and have the generation AI analyze changes in trends.

[0051] During trend analysis, the analysis unit can analyze trends by referring to market data related to the information. The analysis unit, for example, analyzes current trends based on the related market data. The analysis unit can also predict future trends by referring to the related market data. The analysis unit can also analyze trend fluctuations by referring to the related market data. This allows trend fluctuations to be accurately analyzed by referring to the market data related to the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input related market data into the generation AI and have the generation AI perform trend analysis.

[0052] The analysis unit can analyze trends taking into account the technical maturity of the information when analyzing trends. The analysis unit, for example, analyzes trends based on technically mature information. The analysis unit can also predict future trends based on technically immature information. The analysis unit can also adjust the trend analysis method depending on the technical maturity of the information. This allows future trends to be accurately predicted by taking the technical maturity of the information into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input technical maturity data of the information into the generation AI and have the generation AI perform trend analysis.

[0053] When displaying the interface, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can prioritize and provide a display method that the user has frequently used in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. The providing unit can also customize the display content by referring to the user's past operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0054] The providing unit can customize the display content according to the user's current task when displaying the interface. For example, the providing unit prioritizes displaying information related to the task the user is currently working on. The providing unit can also adjust the display content according to the user's task progress. The providing unit can also display necessary information in a timely manner based on the user's task. This allows the necessary information to be efficiently provided by customizing the display content according to the user's current task. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's task data into the generation AI and cause the generation AI to customize the display content.

[0055] The providing unit can improve the display method by reflecting user feedback when displaying the interface. The providing unit improves the display method based on, for example, feedback provided by the user. The providing unit can also adjust the display content by referring to the user feedback. The providing unit can also optimize the interface design by reflecting the user feedback. In this way, the display method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the display method.

[0056] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying the interface. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can also provide a display method that includes detailed information. This makes it possible to provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0057] The providing unit can make the display content multilingual according to the user's language setting when displaying the interface. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This improves user convenience by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to execute the multilingual display content.

[0058] The providing unit can customize the display content according to the user's job title and job content when displaying the interface. The providing unit, for example, prioritizes displaying necessary information according to the user's job title. The providing unit can also display related information based on the user's job content. The providing unit can also customize the display content taking into account the user's job title and job content. This allows necessary information to be efficiently provided by customizing the display content according to the user's job title and job content. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the user's job title and job content data into the generation AI and cause the generation AI to customize the display content.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, it can prioritize the selection of a collection method that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method based on the user's past collection history. Furthermore, the collection unit can select the optimal collection method for a specific time period based on the user's past collection history. In this way, the optimal collection method can be selected by analyzing the user's past information collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information collection history into the generation AI and have the generation AI select the optimal collection method.

[0061] When extracting keywords, the extraction unit can adjust the level of extraction detail based on the importance of the information. For example, detailed keywords are extracted from information with high importance. The extraction unit can also extract simple keywords from information with low importance. Furthermore, the extraction unit can adjust the number of keywords to be extracted depending on the importance of the information. In this way, appropriate keywords can be extracted by adjusting the level of extraction detail based on the importance of the information. Some or all of the above-mentioned processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit can input information importance data to the generation AI and cause the generation AI to adjust the level of keyword extraction detail.

[0062] When analyzing trends, the analysis unit can perform the analysis taking into account the attribute information of the information submitter. For example, the trend analysis can be performed taking into account the job title and department of the information submitter. The analysis unit can also perform the trend analysis taking into account the experience and expertise of the information submitter. Furthermore, the analysis unit can perform the trend analysis by referring to the past submission history of the information submitter. This enables appropriate trend analysis by taking into account the attribute information of the information submitter. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the attribute data of the information submitter into the generation AI and have the generation AI perform the trend analysis.

[0063] When displaying an interface, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can also provide a display method that includes detailed information. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0064] The providing unit can make the display content multilingual according to the user's language setting when displaying the interface. For example, the display content can be automatically set based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the display content in that language. This improves user convenience by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to execute multilingual display content.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The collection unit collects information. This information includes, for example, text data, image data, and audio data. In addition to collecting daily reports and business reports, the collection unit also collects information from the Internet and acquires information from internal databases. For example, it uses web scraping technology to collect information on the Internet and API integration to acquire information from internal databases. It also supports the collection of information through manual input, and collects information manually entered by users. Step 2: The extraction unit uses the generation AI to analyze the information collected by the collection unit and extract relevant keywords. Keyword extraction is based on frequency of appearance and co-occurrence. For example, text generation AI (e.g., LLM) is used to extract important keywords from the information, and multimodal generation AI is used to extract keywords from image and audio data. Step 3: The analysis unit analyzes trends based on the keywords extracted by the extraction unit. Trend analysis is based on changes over time and trends related to specific themes. For example, a generation AI can be used to analyze the frequency of keyword appearances and co-occurrence relationships to identify trends. Step 4: The provider displays the trends analyzed by the analyzer through a customizable interface. The provider provides an interface that allows users to customize the display content and narrow down specific information using a filter function. The provider also uses a generation AI to learn the user's operation history and make suggestions to improve the efficiency of future operations.

[0067] (Example 2) An information analysis system according to an embodiment of the present invention utilizes generative AI technology to automatically analyze daily reports and other information and extract related keywords and trends. This information analysis system provides an easy-to-use, customizable interface, effectively realizing information sharing throughout the company. For example, a user inputs daily reports and other information into the information analysis system. The information analysis system then analyzes the input information using generative AI and extracts related keywords and trends. For example, keywords such as "new customers," "contracts concluded," and "competitors" may be extracted from the sales department's daily reports, and trends such as "increase in new customers" and "improvement in contract conclusion rate" may be identified. The extracted keywords and trends are displayed through a customizable interface. Users can customize the display content to suit their needs. For example, they can set it to display only information related to a specific department or project. Furthermore, the information analysis system effectively realizes information sharing throughout the company. The extracted keywords and trends are shared with all employees in real time, enabling centralized information management. This strengthens collaboration between departments and enables faster decision-making. The information analysis system provides an easy-to-use interface, making it easy to operate even for non-technical users. For example, users can customize the displayed content with drag and drop, or use the filter function to narrow down specific information. The generative AI also learns the user's operation history and makes suggestions to make operations more efficient in the future. For example, it can automatically apply filter settings that have been frequently used in the past, improving user convenience. This enables the information analysis system to automatically analyze daily reports and information, extract related keywords and trends, provide a customizable interface, and effectively share information throughout the company, improving business efficiency and supporting rapid decision-making.

[0068] The information analysis system according to the embodiment includes a collection unit, an extraction unit, an analysis unit, and a provision unit. The collection unit collects information. The information includes, but is not limited to, text data, image data, and audio data. The collection unit collects, for example, daily reports and business reports. The collection unit can also collect information from the Internet and acquire information from internal databases. For example, the collection unit can collect information from the Internet using web scraping technology. The collection unit can also acquire information from internal databases using API integration. The collection unit also supports information collection through manual input. For example, the collection unit collects information manually entered by a user. The extraction unit uses a generation AI to analyze the information collected by the collection unit and extract related keywords. Keyword extraction is performed based on, for example, frequency of appearance or co-occurrence relationships, but is not limited to, examples. For example, the extraction unit uses a text generation AI (e.g., LLM) to extract important keywords from information. The extraction unit can also extract keywords from image and audio data using a multimodal generation AI. The extraction unit can also use the generation AI to analyze the content of the information and extract related keywords. For example, a text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. A multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses keyword extraction technology to select particularly important information from the information and extract keywords based on that information. The analysis unit analyzes trends based on the keywords extracted by the extraction unit. Trend analysis is performed, for example, based on temporal changes or trends related to a specific theme, but is not limited to such examples. The analysis unit, for example, analyzes temporal changes and identifies trends. The analysis unit can also analyze trends related to a specific theme and identify trends. The analysis unit can also use the generation AI to analyze trends based on the extracted keywords. For example, the analysis unit uses the generation AI to analyze the frequency of keyword appearance and co-occurrence relationships to identify trends.The providing unit displays the trends analyzed by the analysis unit through a customizable interface. The providing unit, for example, provides an interface that allows a user to customize the display content. For example, the providing unit provides an interface that allows the user to customize the display content using drag and drop. The providing unit can also provide an interface that allows specific information to be narrowed down using a filter function. The providing unit can also use a generation AI to learn the user's operation history and make suggestions to improve the efficiency of subsequent operations. For example, the providing unit automatically applies filter settings that have been frequently used in the past. This enables the information analysis system according to the embodiment to collect, analyze, extract keywords, analyze trends, and display information through a customizable interface. Some or all of the above-described processing by the providing unit may be performed using, or without, AI. For example, the providing unit can input the user's operation history into the generation AI and cause the generation AI to make suggestions to improve the efficiency of subsequent operations.

[0069] The collection unit can collect daily reports or information. Daily reports include, but are not limited to, business reports, progress reports, and problems. Information includes, but is not limited to, text data, image data, and audio data. The collection unit, for example, collects daily reports. For example, the collection unit collects daily reports from the sales department to understand business reports and progress reports. The collection unit can also collect daily reports from the engineering department to understand technical problems and progress reports. The collection unit can also collect daily reports from the management department to understand progress and problems in management work. In this way, collecting daily reports and information enables centralized management of information. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI or without AI. For example, the collection unit can input daily reports into a generation AI and have the generation AI execute data for analyzing the contents of the daily reports.

[0070] The extraction unit can analyze the collected information and extract related keywords. Related keywords are extracted based on, for example, frequency of appearance or co-occurrence, but are not limited to these examples. For example, the extraction unit can analyze the collected information and extract frequently appearing keywords. The extraction unit can also analyze the collected information and extract keywords based on co-occurrence. The extraction unit can also analyze the collected information and extract related keywords using a generation AI. For example, the extraction unit can extract important keywords from the information using a text generation AI (e.g., LLM). The extraction unit can also extract keywords from image and audio data using a multimodal generation AI. This facilitates information organization by extracting related keywords from the collected information. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or without AI. For example, the extraction unit can input the collected information to a generation AI and cause the generation AI to extract related keywords.

[0071] The analysis unit can analyze trends based on the extracted keywords. Trends are analyzed, for example, based on temporal changes or trends related to a specific theme, but are not limited to these examples. For example, the analysis unit can analyze temporal changes in the extracted keywords to identify trends. The analysis unit can also analyze trends related to a specific theme of the extracted keywords to identify trends. The analysis unit can also analyze trends based on the extracted keywords using a generation AI. For example, the analysis unit can use the generation AI to analyze the frequency of appearance and co-occurrence of keywords to identify trends. In this way, trends can be analyzed based on the extracted keywords to understand information trends. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the extracted keywords to the generation AI and have the generation AI perform trend analysis.

[0072] The providing unit can display the analyzed trends through a customizable interface. The customizable interface, for example, provides an interface that allows the user to customize the display content. The providing unit can provide an interface that allows the user to customize the display content, for example, by dragging and dropping. The providing unit can also provide an interface that allows the user to narrow down specific information using a filter function. The providing unit can also use a generation AI to learn the user's operation history and make suggestions to improve the efficiency of subsequent operations. For example, the providing unit automatically applies filter settings that have been frequently used in the past. This improves user convenience by displaying the analyzed trends through a customizable interface. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's operation history into the generation AI and cause the generation AI to make suggestions to improve the efficiency of subsequent operations.

[0073] The providing unit can learn the user's operation history and make suggestions to improve the efficiency of subsequent operations. The user's operation history includes, but is not limited to, for example, a click history, an input history, and a browsing history. The providing unit can, for example, learn the user's click history and make suggestions to improve the efficiency of subsequent operations. The providing unit can also learn the user's input history and make suggestions to improve the efficiency of subsequent operations. The providing unit can also learn the user's browsing history and make suggestions to improve the efficiency of subsequent operations. In this way, by learning the user's operation history, subsequent operations are made more efficient. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the user's operation history into a generating AI and cause the generating AI to execute suggestions to improve the efficiency of subsequent operations.

[0074] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to efficiently collect information. Furthermore, if the user is in a hurry, the collection unit can immediately set the collection timing to quickly collect information. This reduces the user's burden by adjusting the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0075] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, the collection unit prioritizes the selection of a collection method that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method based on the user's past collection history. The collection unit can also select the optimal collection method for a specific time period based on the user's past collection history. In this way, the optimal collection method can be selected by analyzing the user's past information collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information collection history into the generation AI and cause the generation AI to select the optimal collection method.

[0076] When collecting information, the collection unit can filter the information based on the user's current project or area of ​​interest. For example, the collection unit prioritizes collecting information related to the project the user is currently working on. The collection unit can also filter and collect highly relevant information based on the user's area of ​​interest. The collection unit can also collect necessary information in a timely manner depending on the progress of the user's project. This makes it possible to collect highly relevant information by filtering information based on the user's current project or area of ​​interest. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's project information to a generation AI and have the generation AI perform filtering.

[0077] When collecting information, the collection unit can select an appropriate collection means depending on the user's input method. For example, when the user uses voice input, the collection unit can collect information using voice recognition technology. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. This allows the collection of information to be made more efficient by selecting the optimal collection means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's input data into a generation AI and have the generation AI select the optimal collection means.

[0078] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit postpones collecting less important information. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting more important information. Furthermore, when the user is in a hurry, the collection unit can immediately collect the most important information. Thus, by determining the priority of information to be collected according to the user's emotions, important information can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of information.

[0079] When collecting information, the collection unit can prioritize collecting highly relevant information based on the user's geographical location information. For example, the collection unit prioritizes collecting information related to the user's current location. The collection unit can also prioritize collecting nearby information based on the user's geographical location information. The collection unit can also refer to the user's movement history to collect highly relevant information. This allows for efficient collection of necessary information by preferentially collecting highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant information.

[0080] When collecting information, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit collects related information based on information shared by the user on social media. The collection unit can also analyze the user's social media activities and collect highly relevant information. The collection unit can also refer to the activities of the user's friends on social media to collect related information. In this way, highly relevant information can be collected by analyzing the user's social media activities. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's social media activity data into a generation AI and cause the generation AI to collect related information.

[0081] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit can improve the collection method based on, for example, feedback provided by the user in the past. The collection unit can also adjust the type of information to be collected by referring to the user's past feedback. The collection unit can also optimize the collection timing and means by reflecting the user's feedback. In this way, the collection method can be optimized by reflecting the user's past feedback. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's feedback data into the generation AI and have the generation AI customize the collection method.

[0082] The extraction unit can estimate the user's emotions and adjust the keyword extraction method based on the estimated user emotions. For example, if the user is feeling stressed, the extraction unit can prioritize extracting simple keywords. Furthermore, if the user is relaxed, the extraction unit can also extract detailed keywords. Furthermore, if the user is in a hurry, the extraction unit can instantly extract keywords with high importance. This allows appropriate keywords to be extracted by adjusting the keyword extraction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the extraction unit can be performed using, for example, AI, or without AI. For example, the extraction unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the keyword extraction method.

[0083] The extraction unit can adjust the level of extraction detail based on the importance of the information when extracting keywords. For example, the extraction unit extracts detailed keywords from information with high importance. The extraction unit can also extract simple keywords from information with low importance. The extraction unit can also adjust the number of keywords to be extracted depending on the importance of the information. In this way, appropriate keywords can be extracted by adjusting the level of extraction detail based on the importance of the information. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input information importance data to the generation AI and cause the generation AI to adjust the level of keyword extraction detail.

[0084] When extracting keywords, the extraction unit can apply different extraction algorithms depending on the category of information. For example, the extraction unit can apply an algorithm to extract sales-related keywords from the daily reports of the sales department. The extraction unit can also apply an algorithm to extract technology-related keywords from the daily reports of the engineering department. The extraction unit can also apply an algorithm to extract management-related keywords from the daily reports of the management department. In this way, by applying different extraction algorithms depending on the category of information, appropriate keywords can be extracted. Some or all of the above-mentioned processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input information category data into the generation AI and cause the generation AI to apply a keyword extraction algorithm.

[0085] When extracting keywords, the extraction unit can improve the accuracy of the extraction by referring to the user's past extraction results. The extraction unit, for example, adjusts the extraction algorithm based on keywords previously extracted by the user. The extraction unit can also analyze the user's past extraction results to improve accuracy. The extraction unit can also extract optimal keywords by referring to the user's past extraction history. This improves the accuracy of the extraction by referring to the user's past extraction results. Some or all of the above-described processing in the extraction unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the extraction unit can input the user's past extraction result data into the generation AI and cause the generation AI to improve the extraction accuracy.

[0086] The extraction unit can estimate the user's emotions and adjust the length of keywords to be extracted based on the estimated user emotions. For example, if the user is feeling stressed, the extraction unit can prioritize extracting short keywords. Furthermore, if the user is relaxed, the extraction unit can also extract long keywords. Furthermore, if the user is in a hurry, the extraction unit can extract short, concise keywords. By adjusting the length of keywords to be extracted according to the user's emotions, appropriate keywords can be extracted. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit can be performed using, for example, an AI, or without an AI. For example, the extraction unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the keywords.

[0087] When extracting keywords, the extraction unit can determine the extraction priority based on the time of information submission. For example, the extraction unit preferentially extracts keywords from the most recent information. The extraction unit can also extract highly important keywords from older information. The extraction unit can also adjust the order of keywords to be extracted depending on the time of information submission. In this way, by determining the extraction priority based on the time of information submission, the most recent information can be preferentially extracted. Some or all of the above-described processing in the extraction unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the extraction unit can input information submission time data into the generation AI and have the generation AI determine the extraction priority.

[0088] When extracting keywords, the extraction unit can adjust the extraction order based on the relevance of the information. For example, the extraction unit preferentially extracts keywords from highly relevant information. The extraction unit can also extract keywords from less relevant information later. The extraction unit can also adjust the order of keywords to be extracted depending on the relevance of the information. In this way, by adjusting the extraction order based on the relevance of the information, highly relevant information can be preferentially extracted. Some or all of the above-mentioned processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit can input information relevance data to the generation AI and have the generation AI adjust the extraction order.

[0089] When extracting keywords, the extraction unit can adjust the use of technical terms in the extraction according to the user's level of expertise. For example, if the user has technical expertise, the extraction unit extracts keywords containing technical terms. Alternatively, if the user does not have technical expertise, the extraction unit can extract keywords containing general terms. The extraction unit can also adjust the use of technical terms in the extracted keywords according to the user's level of expertise. This allows appropriate keywords to be extracted by adjusting the use of technical terms according to the user's level of expertise. Some or all of the above-described processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit can input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terms.

[0090] The analysis unit can estimate the user's emotions and adjust the trend analysis method based on the estimated user emotions. For example, the analysis unit can perform a simple trend analysis when the user is stressed. The analysis unit can also perform a detailed trend analysis when the user is relaxed. The analysis unit can also instantly analyze important trends when the user is in a hurry. This enables appropriate trend analysis by adjusting the trend analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the trend analysis method.

[0091] During trend analysis, the analysis unit can predict a current trend by referring to past trend data. The analysis unit, for example, predicts a current trend based on past trend data. The analysis unit can also predict a future trend by analyzing past trend data. The analysis unit can also predict fluctuations in a current trend by referring to past trend data. In this way, a current trend can be predicted by referring to past trend data. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input past trend data into the generation AI and have the generation AI predict a current trend.

[0092] The analysis unit can apply different analysis methods to each information category during trend analysis. For example, the analysis unit can apply a sales-related trend analysis method to information from the sales department. The analysis unit can also apply a technology-related trend analysis method to information from the engineering department. The analysis unit can also apply a management-related trend analysis method to information from the management department. This enables appropriate trend analysis by applying different analysis methods to each information category. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input information category data into the generation AI and have the generation AI apply the trend analysis method.

[0093] When performing trend analysis, the analysis unit can perform the analysis taking into account attribute information of the information submitter. The analysis unit performs trend analysis taking into account, for example, the position and department of the information submitter. The analysis unit can also perform trend analysis taking into account the experience and expertise of the information submitter. The analysis unit can also perform trend analysis by referring to the past submission history of the information submitter. This enables appropriate trend analysis by taking into account the attribute information of the information submitter. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input attribute data of the information submitter into the generation AI and have the generation AI perform trend analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the importance of trends based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can postpone analyzing trends with lower importance. Furthermore, if the user is relaxed, the analysis unit can also prioritize analyzing trends with higher importance. Furthermore, if the user is in a hurry, the analysis unit can instantly analyze the most important trends. By adjusting the importance of trends according to the user's emotions, important trends can be prioritized for analysis. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the importance of trends.

[0095] During trend analysis, the analysis unit can analyze changes in trends based on the time of information submission. The analysis unit, for example, analyzes changes in current trends based on the latest information. The analysis unit can also predict changes in trends based on past information. The analysis unit can also analyze changes in trends according to the time of information submission. This makes it possible to grasp the latest trends by analyzing changes in trends based on the time of information submission. Some or all of the above-described processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input data on the time of information submission into the generation AI and have the generation AI analyze changes in trends.

[0096] During trend analysis, the analysis unit can analyze trends by referring to market data related to the information. The analysis unit, for example, analyzes current trends based on the related market data. The analysis unit can also predict future trends by referring to the related market data. The analysis unit can also analyze trend fluctuations by referring to the related market data. This allows trend fluctuations to be accurately analyzed by referring to the market data related to the information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input related market data into the generation AI and have the generation AI perform trend analysis.

[0097] The analysis unit can analyze trends taking into account the technical maturity of the information when analyzing trends. The analysis unit, for example, analyzes trends based on technically mature information. The analysis unit can also predict future trends based on technically immature information. The analysis unit can also adjust the trend analysis method depending on the technical maturity of the information. This allows future trends to be accurately predicted by taking the technical maturity of the information into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input technical maturity data of the information into the generation AI and have the generation AI perform trend analysis.

[0098] The providing unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, when the user is feeling stressed, the providing unit can provide a simple, highly visible interface. Furthermore, when the user is relaxed, the providing unit can provide an interface containing detailed information. Furthermore, when the user is in a hurry, the providing unit can provide an interface that focuses on the main points. This improves user convenience by adjusting the interface display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the interface display method.

[0099] When displaying the interface, the providing unit can select the optimal display method by referring to the user's past operation history. For example, the providing unit can prioritize and provide a display method that the user has frequently used in the past. The providing unit can also suggest the optimal display method based on the user's past operation history. The providing unit can also customize the display content by referring to the user's past operation history. In this way, the optimal display method can be provided by referring to the user's past operation history. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's past operation history data into the generation AI and cause the generation AI to select the optimal display method.

[0100] The providing unit can customize the display content according to the user's current task when displaying the interface. For example, the providing unit prioritizes displaying information related to the task the user is currently working on. The providing unit can also adjust the display content according to the user's task progress. The providing unit can also display necessary information in a timely manner based on the user's task. This allows the necessary information to be efficiently provided by customizing the display content according to the user's current task. Some or all of the above-mentioned processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's task data into the generation AI and cause the generation AI to customize the display content.

[0101] The providing unit can improve the display method by reflecting user feedback when displaying the interface. The providing unit improves the display method based on, for example, feedback provided by the user. The providing unit can also adjust the display content by referring to the user feedback. The providing unit can also optimize the interface design by reflecting the user feedback. In this way, the display method can be optimized by reflecting the user feedback. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the providing unit can input user feedback data into the generation AI and cause the generation AI to improve the display method.

[0102] The providing unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, the providing unit simplifies the operation procedures when the user is feeling stressed. The providing unit can also provide detailed operation procedures when the user is relaxed. The providing unit can also provide procedures that allow the user to operate quickly when the user is in a hurry. This adjusts the interface operation procedures according to the user's emotions, thereby improving the efficiency of user operations. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the operation procedures.

[0103] The providing unit can select the optimal display method by taking into consideration the user's device information when displaying the interface. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can also provide a display method that includes detailed information. This makes it possible to provide the optimal display method by taking into consideration the user's device information. Some or all of the above-described processing in the providing unit may be performed using, or without, the generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0104] The providing unit can make the display content multilingual according to the user's language setting when displaying the interface. The providing unit automatically sets the display content based on, for example, the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. The providing unit can also provide the display content in a specific language when the user selects that language. This improves user convenience by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to execute the multilingual display content.

[0105] The providing unit can customize the display content according to the user's job title and job content when displaying the interface. The providing unit, for example, prioritizes displaying necessary information according to the user's job title. The providing unit can also display related information based on the user's job content. The providing unit can also customize the display content taking into account the user's job title and job content. This allows necessary information to be efficiently provided by customizing the display content according to the user's job title and job content. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI, for example. For example, the providing unit can input the user's job title and job content data into the generation AI and cause the generation AI to customize the display content. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, extraction unit, analysis unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit can collect information using the camera 42 or microphone 38B of the smart device 14. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts keywords from the collected information using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes trends based on the extracted keywords. The provision unit is realized by the control unit 46A of the smart device 14 and displays the analysis results through a customizable interface. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, extraction unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit can collect information using the camera 42 or microphone 238 of the smart glasses 214. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts keywords from the collected information using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes trends based on the extracted keywords. The provision unit is realized by the control unit 46A of the smart glasses 214 and displays the analysis results through a customizable interface. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, extraction unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit can collect information using the camera 42 or microphone 238 of the headset type terminal 314. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts keywords from the collected information using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes trends based on the extracted keywords. The provision unit is realized by the control unit 46A of the headset type terminal 314 and displays the analysis results through a customizable interface. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, extraction unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit can collect information using the camera 42 or microphone 238 of the robot 414. The extraction unit is realized by the specific processing unit 290 of the data processing device 12 and extracts keywords from the collected information using a generation AI. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes trends based on the extracted keywords. The provision unit is realized by the control unit 46A of the robot 414 and displays the analysis results through a customizable interface.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The collection unit can analyze the user's past information collection history and select the optimal collection method. For example, it can prioritize the selection of a collection method that the user has frequently used in the past. The collection unit can also suggest the most efficient collection method based on the user's past collection history. Furthermore, the collection unit can select the optimal collection method for a specific time period based on the user's past collection history. In this way, the optimal collection method can be selected by analyzing the user's past information collection history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past information collection history into the generation AI and have the generation AI select the optimal collection method.

[0108] When extracting keywords, the extraction unit can adjust the level of extraction detail based on the importance of the information. For example, detailed keywords are extracted from information with high importance. The extraction unit can also extract simple keywords from information with low importance. Furthermore, the extraction unit can adjust the number of keywords to be extracted depending on the importance of the information. In this way, appropriate keywords can be extracted by adjusting the level of extraction detail based on the importance of the information. Some or all of the above-mentioned processing in the extraction unit may be performed using, or without, a generation AI. For example, the extraction unit can input information importance data to the generation AI and cause the generation AI to adjust the level of keyword extraction detail.

[0109] When analyzing trends, the analysis unit can perform the analysis taking into account the attribute information of the information submitter. For example, the trend analysis can be performed taking into account the job title and department of the information submitter. The analysis unit can also perform the trend analysis taking into account the experience and expertise of the information submitter. Furthermore, the analysis unit can perform the trend analysis by referring to the past submission history of the information submitter. This enables appropriate trend analysis by taking into account the attribute information of the information submitter. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the analysis unit can input the attribute data of the information submitter into the generation AI and have the generation AI perform the trend analysis.

[0110] When displaying an interface, the providing unit can select the optimal display method by taking into account the user's device information. For example, if the user is using a smartphone, the providing unit can provide a display method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a display method that is optimized for a large screen. Furthermore, if the user is using a desktop, the providing unit can also provide a display method that includes detailed information. This makes it possible to provide the optimal display method by taking into account the user's device information. Some or all of the above-described processing in the providing unit may be performed using, or without, a generation AI. For example, the providing unit can input the user's device information into the generation AI and cause the generation AI to select the optimal display method.

[0111] The providing unit can make the display content multilingual according to the user's language setting when displaying the interface. For example, the display content can be automatically set based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, when the user selects a specific language, the providing unit can provide the display content in that language. This improves user convenience by making the display content multilingual according to the user's language setting. Some or all of the above-described processing by the providing unit can be performed, for example, using a generation AI or without using a generation AI. For example, the providing unit can input the user's language setting data into the generation AI and cause the generation AI to execute multilingual display content.

[0112] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection timing can be delayed to reduce the user's burden. Furthermore, if the user is relaxed, the collection unit can also accelerate the collection timing to efficiently collect information. Furthermore, if the user is in a hurry, the collection unit can immediately set the collection timing to quickly collect information. This reduces the user's burden by adjusting the timing of information collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of information collection.

[0113] The extraction unit can estimate the user's emotions and adjust the keyword extraction method based on the estimated user emotions. For example, if the user is stressed, simple keywords are preferentially extracted. The extraction unit can also extract detailed keywords if the user is relaxed. Furthermore, if the user is in a hurry, the extraction unit can instantly extract keywords with high importance. This allows appropriate keywords to be extracted by adjusting the keyword extraction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the extraction unit can be performed using, for example, an AI, or without an AI. For example, the extraction unit can input the user's emotion data into the generation AI and have the generation AI adjust the keyword extraction method.

[0114] The analysis unit can estimate the user's emotions and adjust the trend analysis method based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can perform a simple trend analysis. Furthermore, if the user is relaxed, the analysis unit can also perform a detailed trend analysis. Furthermore, if the user is in a hurry, the analysis unit can instantly analyze important trends. This enables appropriate trend analysis by adjusting the trend analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the trend analysis method.

[0115] The providing unit can estimate the user's emotions and adjust the interface display method based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible interface. Furthermore, if the user is relaxed, the providing unit can provide an interface containing detailed information. Furthermore, if the user is in a hurry, the providing unit can provide an interface that focuses on the main points. This improves user convenience by adjusting the interface display method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the interface display method.

[0116] The providing unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user emotions. For example, if the user is feeling stressed, the providing unit can simplify the operation procedures. The providing unit can also provide detailed operation procedures if the user is relaxed. Furthermore, the providing unit can also provide procedures that allow the user to operate quickly if the user is in a hurry. This allows the interface operation procedures to be adjusted according to the user's emotions, thereby improving the efficiency of user operations. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the operation procedures.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection unit collects information. This information includes, for example, text data, image data, and audio data. In addition to collecting daily reports and business reports, the collection unit also collects information from the Internet and acquires information from internal databases. For example, it uses web scraping technology to collect information on the Internet and API integration to acquire information from internal databases. It also supports the collection of information through manual input, and collects information manually entered by users. Step 2: The extraction unit uses the generation AI to analyze the information collected by the collection unit and extract relevant keywords. Keyword extraction is based on frequency of appearance and co-occurrence. For example, text generation AI (e.g., LLM) is used to extract important keywords from the information, and multimodal generation AI is used to extract keywords from image and audio data. Step 3: The analysis unit analyzes trends based on the keywords extracted by the extraction unit. Trend analysis is based on changes over time and trends related to specific themes. For example, a generation AI can be used to analyze the frequency of keyword appearances and co-occurrence relationships to identify trends. Step 4: The provider displays the trends analyzed by the analyzer through a customizable interface. The provider provides an interface that allows users to customize the display content and narrow down specific information using a filter function. The provider also uses a generation AI to learn the user's operation history and make suggestions to improve the efficiency of future operations.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0159] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects information; an extraction unit that analyzes the information collected by the collection unit and extracts related keywords; an analysis unit that analyzes trends based on the keywords extracted by the extraction unit; a providing unit that displays the trends analyzed by the analyzing unit through a customizable interface; Equipped with A system characterized by:

2. The collecting unit Collect daily reports or information The system of claim 1 .

3. The extraction unit Analyze the collected information and extract related keywords The system of claim 1 .

4. The analysis unit Analyze trends based on extracted keywords The system of claim 1 .

5. The providing unit Display analyzed trends through a customizable interface The system of claim 1 .

6. The providing unit Learns the user's operation history and makes suggestions to improve the efficiency of future operations The system of claim 1 .

7. The collecting unit Estimates user emotions and adjusts information collection timing based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Analyze the user's past information collection history and select the appropriate collection method The system of claim 1 .

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