System

Abstract

An object of a system according to an embodiment is to make an optimal proposal based on a user's preference.SOLUTION: A system includes a collection unit, an analysis unit, a provision unit, and a learning unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The providing unit provides the result analyzed by the analysis unit in a table format. The learning unit learns the preference of the user based on the result provided by the providing unit.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 it is difficult to efficiently collect and analyze data from multiple sources and make optimal suggestions based on user preferences.

[0005] The system according to the embodiment aims to make optimal suggestions based on the user's preferences. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a provision unit, and a learning unit. The collection unit collects information. The analysis unit analyzes the information collected by the collection unit. The provision unit provides the results of the analysis by the analysis unit in a tabular format. The learning unit learns user preferences based on the results provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide optimal suggestions based on the user's preferences. [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 comparison system according to an embodiment of the present invention is a system in which a generation AI collects and analyzes information from multiple sites based on information entered by a user and provides it in a tabular format. This information comparison system can learn the user's preferences and reflect them in subsequent searches. For example, when a user wants to purchase a new smartphone or search for parking spaces near a destination, the generation AI can provide optimal suggestions, thereby saving time and effort. This allows the information comparison system to eliminate the need for users to search and compare multiple sites, enabling users to make optimal choices efficiently. For example, when purchasing a new smartphone or searching for parking spaces near a destination, the generation AI can provide optimal suggestions, thereby saving time and effort.

[0029] An information comparison system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a learning unit. The collection unit collects information from multiple sites based on information input by a user. For example, the collection unit can use web scraping technology to collect information from news sites and social media sites. The collection unit can also acquire data using an API. The collection unit can select the optimal collection method depending on the user's input method (voice, text, image, etc.). For example, if voice input is used, the collection unit can collect information using voice recognition technology. The analysis unit analyzes the information collected by the collection unit and generates a comparison result. For example, the analysis unit can apply statistical analysis or machine learning algorithms to analyze the collected information. The analysis unit can also apply different analysis algorithms depending on the category of information. For example, for product information, analysis can be performed with an emphasis on price and functionality. The provision unit provides the results analyzed by the analysis unit in a table format. For example, the provision unit can provide the generated comparison results in Excel format or HTML table format. The provision unit can also estimate the user's emotions and adjust the display method of the table format provided based on the estimated user emotions. For example, if the user is relaxed, a detailed table format can be provided. The learning unit learns the user's preferences based on the results provided by the providing unit and reflects the learned preferences in subsequent searches. For example, the learning unit can optimize subsequent search results based on the user's past preferences. The learning unit can also update the learning data by reflecting user feedback. This allows the information comparison system according to the embodiment to efficiently collect, analyze, provide, and learn information.

[0030] The collection unit can collect information from multiple sites. For example, the collection unit can collect information from news sites and social media sites. For example, the collection unit can use web scraping technology to collect the latest news articles from news sites. The collection unit can also collect user posts from social media sites. Furthermore, the collection unit can acquire data using an API. For example, the collection unit can use the API of a specific news site to acquire the latest news articles. In this way, by collecting information from multiple sites, the comprehensiveness of the information is improved.

[0031] The analysis unit can analyze the collected information and generate a comparison result. The analysis unit can analyze the collected information by applying, for example, statistical analysis or a machine learning algorithm. For example, the analysis unit can perform price comparisons and performance comparisons based on the collected information. The analysis unit can also apply different analysis algorithms depending on the category of information. For example, for product information, the analysis can focus on price and functionality. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis result can be provided. In this way, useful information can be provided to the user by analyzing the collected information and generating a comparison result.

[0032] The providing unit may provide the generated comparison results in a table format. For example, the providing unit may provide the generated comparison results in an Excel format or an HTML table format. For example, the providing unit may provide the generated comparison results in an Excel format, allowing a user to check the comparison results using Excel software. Alternatively, the providing unit may provide the generated comparison results in an HTML table format, allowing a user to check the comparison results using a web browser. Furthermore, the providing unit may estimate a user's emotion and adjust the display method of the provided table format based on the estimated user's emotion. For example, if the user is relaxed, a detailed table format may be provided. Thus, by providing the generated comparison results in a table format, the user can grasp the information at a glance.

[0033] The learning unit can learn the user's past preferences and reflect them in subsequent searches. The learning unit can, for example, optimize subsequent search results based on the user's past preferences. For example, the learning unit can analyze data on products and services selected by the user in the past and reflect the data in subsequent search results. The learning unit can also update the learning data by reflecting user feedback. For example, the learning unit can update the learning data based on feedback provided by the user to improve the accuracy of subsequent search results. Furthermore, the learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is relaxed, detailed learning data can be selected. In this way, by learning the user's past preferences, subsequent search results will be more in line with the user's preferences.

[0034] The collection unit can analyze the user's past search history and select the optimal information collection method. The collection unit can, for example, prioritize collecting information from sites that the user has frequently used in the past. For example, the collection unit can collect the latest news articles from news sites that the user has frequently used in the past. The collection unit can also collect related information based on keywords that the user has searched for in the past. For example, the collection unit can collect related product information based on keywords that the user has searched for in the past. Furthermore, the collection unit can optimize the information to be collected for a specific time period based on the user's past search history. For example, the collection unit can collect information related to a specific time period based on information that the user has searched for in the past during that time period. In this way, the optimal information collection method can be selected by analyzing the user's past search history.

[0035] The collection unit can filter information based on the user's current areas of interest when collecting information. The collection unit can, for example, collect information based on product categories in which the user is currently interested. For example, the collection unit can preferentially collect information about smartphones in which the user is currently interested. The collection unit can also preferentially collect information related to topics recently searched by the user. For example, the collection unit can collect news articles related to topics recently searched by the user. Furthermore, the collection unit can filter unnecessary information based on the user's current areas of interest. For example, the collection unit can filter information in categories in which the user is not currently interested. In this way, unnecessary information can be eliminated by filtering information based on the user's current areas of interest.

[0036] When collecting information, the collection unit can select the optimal 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. For example, when the user uses voice input, the collection unit can convert the user's voice into text data using voice recognition technology and collect information based on the text data. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. For example, when the user uses text input, the collection unit can analyze the text entered by the user using text analysis technology and collect information based on the analysis results. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. For example, when the user uses image input, the collection unit can analyze the image entered by the user using image recognition technology and collect information based on the analysis results. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method.

[0037] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit can, for example, collect information about nearby stores based on the user's current location. For example, the collection unit can prioritize collecting information about nearby restaurants and shops based on the user's current location. The collection unit can also prioritize collecting information about nearby parking lots based on the user's geographical location information. For example, the collection unit can prioritize collecting information about nearby parking lots based on the user's current location. Furthermore, the collection unit can also collect information about area-limited special offers and sales by taking into account the user's location information. For example, the collection unit can prioritize collecting information about area-limited discount coupons and sales based on the user's current location. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0038] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit can, for example, collect information about brands that the user follows on social media. For example, the collection unit can collect the latest product information and campaign information about brands that the user follows on social media. The collection unit can also analyze the content of the user's social media posts and collect related product information. For example, the collection unit can analyze the content of the user's social media posts and collect information about products and services in which the user is interested. Furthermore, the collection unit can also collect related information based on information shared by the user's friends. For example, the collection unit can collect related product information based on product reviews and recommendation information shared by the user's friends. In this way, related information can be efficiently collected by analyzing the user's social media activities.

[0039] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit can, for example, prioritize information from information sources that the user has previously rated highly. For example, the collection unit can prioritize collecting information from news sites or blogs that the user has previously rated highly. The collection unit can also adjust the categories of information to be collected based on the user's past feedback. For example, the collection unit can prioritize collecting information in categories that the user has previously rated highly. Furthermore, the collection unit can also optimize the collection method by reflecting the user's feedback. For example, the collection unit can adjust the collection method based on the user's feedback and improve the efficiency of information collection. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit can, for example, perform a detailed analysis of information of high importance. For example, the analysis unit can perform a detailed analysis of the price and functions of product information of high importance. The analysis unit can also perform a concise analysis of information of low importance. For example, the analysis unit can perform a summary-style analysis of news articles of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. For example, the analysis unit can prioritize the analysis of information of high importance and postpone the analysis of information of low importance. In this way, by adjusting the level of detail of the analysis based on the importance of the information, efficient analysis is possible.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can perform analysis that emphasizes price and functionality for product information. For example, the analysis unit can apply an analysis algorithm for comparing prices and functions to product information. The analysis unit can also perform analysis that emphasizes fees and availability for parking lot information. For example, the analysis unit can apply an analysis algorithm for comparing fees and analyzing availability to parking lot information. Furthermore, the analysis unit can also perform analysis that emphasizes ratings and menus for restaurant information. For example, the analysis unit can apply an analysis algorithm for analyzing rating scores and menus to restaurant information. This enables more accurate analysis by applying different analysis algorithms depending on the category of information.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results that the user has given high ratings to in the past. For example, the analysis unit can adjust the analysis algorithm based on the analysis results that the user has given high ratings to in the past, thereby improving the accuracy of the analysis. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. For example, the analysis unit can analyze the user's past analysis results and optimize the parameters of the analysis algorithm. Furthermore, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. For example, the analysis unit can build a feedback loop to improve the accuracy of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0043] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. The analysis unit can, for example, prioritize analyzing the latest information. For example, the analysis unit can prioritize analyzing the latest news articles and product information. The analysis unit can also lower the priority of analyzing older information. For example, the analysis unit can lower the priority of analyzing older news articles and product information. Furthermore, the analysis unit can adjust the priority of analysis based on the time when the information was collected. For example, the analysis unit can prioritize analyzing the latest information and postpone analyzing older information based on the time when the information was collected. In this way, by determining the priority of analysis based on the time when the information was collected, the latest information can be analyzed with priority.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit can, for example, prioritize analysis of highly relevant information. For example, the analysis unit can prioritize analysis of highly relevant product information or news articles. The analysis unit can also postpone the order of analysis of less relevant information. For example, the analysis unit can postpone the order of analysis of less relevant product information or news articles. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the analysis unit can adjust the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0045] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. For example, if the user has technical expertise, the analysis unit can provide detailed analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple terms. For example, if the user does not have technical expertise, the analysis unit can provide analysis results explained in simple terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can define technical terms or replace them with general terms according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0046] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit can provide a detailed table format for information of high importance. For example, the providing unit can provide a detailed table format of prices and functions for product information of high importance. The providing unit can also provide a concise table format for information of low importance. For example, the providing unit can provide a summarized table format for news articles of low importance. Furthermore, the providing unit can adjust the level of detail of the information provided based on the importance of the information. For example, the providing unit can provide a table format of an overview level or a detailed level based on the importance of the information. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information.

[0047] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can provide product information in a table format that emphasizes price and functionality. For example, the providing unit can apply a providing algorithm for comparing prices and functionality to the product information. The providing unit can also provide parking lot information in a table format that emphasizes fees and availability. For example, the providing unit can apply a providing algorithm for comparing fees and analyzing availability to the parking lot information. Furthermore, the providing unit can also provide restaurant information in a table format that emphasizes ratings and menus. For example, the providing unit can apply a providing algorithm for analyzing rating scores and menus to the restaurant information. This makes it possible to provide more accurate information by applying different providing algorithms depending on the category of information.

[0048] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the results. The providing unit can improve the accuracy of the provision by referring to, for example, provision results that the user has given high ratings to in the past. For example, the providing unit can adjust the provision algorithm based on the provision results that the user has given high ratings to in the past, thereby improving the accuracy of the provision. The providing unit can also adjust the provision algorithm based on the user's past provision results. For example, the providing unit can analyze the user's past provision results and optimize parameters of the provision algorithm. Furthermore, the providing unit can analyze the user's past provision results and improve the accuracy of the provision. For example, the providing unit can build a feedback loop for improving the accuracy of the provision based on the user's past provision results. As a result, the accuracy of the provision is improved by referring to the user's past provision results.

[0049] The providing unit can determine the priority of provision based on the time when the information is collected at the time of provision. The providing unit can, for example, provide the latest information with priority. For example, the providing unit can provide the latest news articles or product information with priority. The providing unit can also lower the priority of older information when providing it. For example, the providing unit can lower the priority of older news articles or product information when providing it. Furthermore, the providing unit can adjust the priority of provision according to the time when the information was collected. For example, the providing unit can provide the latest information with priority and postpone providing older information according to the time when the information was collected. In this way, by determining the priority of provision based on the time when the information was collected, the latest information can be provided with priority.

[0050] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. The providing unit can, for example, provide highly relevant information preferentially. For example, the providing unit can provide highly relevant product information or news articles preferentially. The providing unit can also postpone the order of provision of less relevant information. For example, the providing unit can postpone the order of provision of less relevant product information or news articles. Furthermore, the providing unit can also adjust the order of provision based on the relevance of the information. For example, the providing unit can adjust the order of provision based on the relevance of the information. This enables efficient information provision by adjusting the order of provision based on the relevance of the information.

[0051] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit can provide a table format that uses a lot of technical terminology. For example, if the user has technical expertise, the providing unit can provide a detailed table format that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can provide a table format in simple language. For example, if the user does not have technical expertise, the providing unit can provide a table format explained in simple language. Furthermore, the providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, the providing unit can define technical terms or replace them with general terms according to the user's level of expertise. This makes it possible to provide information that is easy for the user to understand by adjusting the use of technical terminology in the provided information according to the user's level of expertise.

[0052] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can adjust the learning algorithm based on, for example, the user's past preferences. For example, the learning unit can optimize the parameters of the learning algorithm based on the user's past preferences. The learning unit can also analyze past learning data to improve the accuracy of the learning algorithm. For example, the learning unit can tune the learning algorithm based on the past learning data. Furthermore, the learning unit can optimize the parameters of the learning algorithm by referring to the past learning data. For example, the learning unit can adjust the parameters of the learning algorithm based on the past learning data to improve the accuracy of learning. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data.

[0053] The learning unit can update the learning data by reflecting user feedback during learning. For example, the learning unit can update the learning data based on feedback provided by the user. For example, the learning unit can add or modify the learning data based on feedback provided by the user. The learning unit can also improve the accuracy of the learning data by reflecting user feedback. For example, the learning unit can improve the quality of the learning data based on user feedback. Furthermore, the learning unit can analyze user feedback and optimize the learning data. For example, the learning unit can optimize the structure and content of the learning data based on user feedback. In this way, the accuracy of the learning data is improved by reflecting user feedback.

[0054] During learning, the learning unit can analyze the user's past preferences and reflect them in subsequent searches. The learning unit can, for example, optimize subsequent search results based on the user's past preferences. For example, the learning unit can adjust subsequent search algorithms based on the user's past preferences. The learning unit can also analyze the user's past preferences and customize subsequent search results. For example, the learning unit can adjust subsequent search results to suit the user's preferences based on the user's past preferences. Furthermore, the learning unit can reflect the user's past preferences to improve the accuracy of subsequent search results. For example, the learning unit can improve the quality of subsequent search results based on the user's past preferences. In this way, by analyzing the user's past preferences, subsequent search results will better match the user's preferences.

[0055] During learning, the learning unit can weight the learning data based on the time when the information was collected. For example, the learning unit can weight the learning data by emphasizing the most recent information. For example, the learning unit can weight the learning data by emphasizing the most recent news articles or product information. The learning unit can also lower the weighting for older information during learning. For example, the learning unit can lower the weighting for older news articles or product information during learning. Furthermore, the learning unit can adjust the weighting of the learning data depending on the time when the information was collected. For example, the learning unit can emphasize the most recent information and lower the weighting for older information depending on the time when the information was collected. In this way, weighting the learning data based on the time when the information was collected enables learning that emphasizes the most recent information.

[0056] During learning, the learning unit can integrate information from different data sources to expand the learning data. For example, the learning unit can integrate information collected from multiple sites to expand the learning data. For example, the learning unit can integrate information collected from multiple news sites and blogs to expand the learning data. The learning unit can also integrate information from different categories to expand the learning data. For example, the learning unit can integrate information from the news category and the technology category to expand the learning data. Furthermore, the learning unit can also integrate information in different formats (text, images, audio, etc.) to expand the learning data. For example, the learning unit can integrate text data, image data, and audio data to expand the learning data. In this way, by integrating information from different data sources, the comprehensiveness of the learning data is improved.

[0057] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit can adjust the learning algorithm, for example, based on user feedback. For example, the learning unit can optimize parameters of the learning algorithm based on user feedback. The learning unit can also improve the accuracy of the learning algorithm by reflecting user feedback. For example, the learning unit can tune the learning algorithm based on user feedback. Furthermore, the learning unit can analyze user feedback and optimize the learning algorithm. For example, the learning unit can optimize the structure and content of the learning algorithm based on user feedback. In this way, the accuracy of the learning algorithm is improved by reflecting user feedback.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The analysis unit can improve the accuracy of search results based on the user's past search history. For example, the analysis unit can analyze keywords that the user has searched for in the past and prioritize analysis of related information. The analysis unit can also prioritize analysis of similar information based on information that the user has previously given a high rating. Furthermore, the analysis unit can customize the way search results are displayed based on the user's past search history. In this way, by utilizing the user's past search history, the accuracy of search results can be improved and more useful information can be provided to the user.

[0060] The collection unit can prioritize collection of relevant information based on the user's current geographical location information. For example, the collection unit can collect nearby store information and event information based on the user's current location. The collection unit can also prioritize collection of area-limited special offers and sales information based on the user's location information. Furthermore, the collection unit can analyze the user's movement history and collect relevant information. In this way, by utilizing the user's geographical location information, more relevant information can be provided.

[0061] The learning unit can optimize subsequent search results based on the user's past preferences. For example, the learning unit can analyze data on products and services selected by the user in the past and reflect this in subsequent search results. The learning unit can also update the learning data by reflecting user feedback. Furthermore, the learning unit can customize the way search results are displayed based on the user's past preferences. In this way, by utilizing the user's past preferences, subsequent search results will be more in line with the user's preferences.

[0062] The analysis unit can apply different analysis algorithms depending on the category of information. For example, for product information, analysis can be performed with emphasis on price and functionality. For parking information, analysis can be performed with emphasis on fees and availability. For restaurant information, analysis can be performed with emphasis on ratings and menus. By applying different analysis algorithms depending on the category of information, more accurate analysis is possible.

[0063] The collection unit can analyze the user's social media activities and collect related information. For example, it can collect information about brands the user follows on social media. It can also analyze the content of the user's social media posts and collect related product information. It can also collect related information based on information shared by the user's friends. In this way, it is possible to efficiently collect related information by analyzing the user's social media activities.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The collection unit collects information from multiple sites based on the information entered by the user. For example, the collection unit can use web scraping technology to collect information from news sites and social media sites. The collection unit can also obtain data using an API. Furthermore, the collection unit can select the optimal collection method depending on the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Step 2: The analysis unit analyzes the information collected by the collection unit and generates a comparison result. For example, the analysis unit can apply statistical analysis or machine learning algorithms to analyze the collected information. The analysis unit can also apply different analysis algorithms depending on the category of information. For example, for product information, the analysis can focus on price and functionality. Step 3: The providing unit provides the results analyzed by the analyzing unit in a table format. For example, the providing unit can provide the generated comparison results in Excel format or HTML table format. The providing unit can also estimate the user's emotion and adjust the display method of the table format to be provided based on the estimated user's emotion. For example, if the user is relaxed, a detailed table format can be provided. Step 4: The learning unit learns the user's preferences based on the results provided by the providing unit and reflects them in subsequent searches. For example, the learning unit can optimize subsequent search results based on the user's past preferences. The learning unit can also update the learning data to reflect user feedback.

[0066] (Example 2) An information comparison system according to an embodiment of the present invention is a system in which a generation AI collects and analyzes information from multiple sites based on information entered by a user and provides it in a tabular format. This information comparison system can learn the user's preferences and reflect them in subsequent searches. For example, when a user wants to purchase a new smartphone or search for parking spaces near a destination, the generation AI can provide optimal suggestions, thereby saving time and effort. This allows the information comparison system to eliminate the need for users to search and compare multiple sites, enabling users to make optimal choices efficiently. For example, when purchasing a new smartphone or searching for parking spaces near a destination, the generation AI can provide optimal suggestions, thereby saving time and effort.

[0067] An information comparison system according to an embodiment includes a collection unit, an analysis unit, a provision unit, and a learning unit. The collection unit collects information from multiple sites based on information input by a user. For example, the collection unit can use web scraping technology to collect information from news sites and social media sites. The collection unit can also acquire data using an API. The collection unit can select the optimal collection method depending on the user's input method (voice, text, image, etc.). For example, if voice input is used, the collection unit can collect information using voice recognition technology. The analysis unit analyzes the information collected by the collection unit and generates a comparison result. For example, the analysis unit can apply statistical analysis or machine learning algorithms to analyze the collected information. The analysis unit can also apply different analysis algorithms depending on the category of information. For example, for product information, analysis can be performed with an emphasis on price and functionality. The provision unit provides the results analyzed by the analysis unit in a table format. For example, the provision unit can provide the generated comparison results in Excel format or HTML table format. The provision unit can also estimate the user's emotions and adjust the display method of the table format provided based on the estimated user emotions. For example, if the user is relaxed, a detailed table format can be provided. The learning unit learns the user's preferences based on the results provided by the providing unit and reflects the learned preferences in subsequent searches. For example, the learning unit can optimize subsequent search results based on the user's past preferences. The learning unit can also update the learning data by reflecting user feedback. This allows the information comparison system according to the embodiment to efficiently collect, analyze, provide, and learn information.

[0068] The collection unit can collect information from multiple sites. For example, the collection unit can collect information from news sites and social media sites. For example, the collection unit can use web scraping technology to collect the latest news articles from news sites. The collection unit can also collect user posts from social media sites. Furthermore, the collection unit can acquire data using an API. For example, the collection unit can use the API of a specific news site to acquire the latest news articles. In this way, by collecting information from multiple sites, the comprehensiveness of the information is improved.

[0069] The analysis unit can analyze the collected information and generate a comparison result. The analysis unit can analyze the collected information by applying, for example, statistical analysis or a machine learning algorithm. For example, the analysis unit can perform price comparisons and performance comparisons based on the collected information. The analysis unit can also apply different analysis algorithms depending on the category of information. For example, for product information, the analysis can focus on price and functionality. Furthermore, the analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis result can be provided. In this way, useful information can be provided to the user by analyzing the collected information and generating a comparison result.

[0070] The providing unit may provide the generated comparison results in a table format. For example, the providing unit may provide the generated comparison results in an Excel format or an HTML table format. For example, the providing unit may provide the generated comparison results in an Excel format, allowing a user to check the comparison results using Excel software. Alternatively, the providing unit may provide the generated comparison results in an HTML table format, allowing a user to check the comparison results using a web browser. Furthermore, the providing unit may estimate a user's emotion and adjust the display method of the provided table format based on the estimated user's emotion. For example, if the user is relaxed, a detailed table format may be provided. Thus, by providing the generated comparison results in a table format, the user can grasp the information at a glance.

[0071] The learning unit can learn the user's past preferences and reflect them in subsequent searches. The learning unit can, for example, optimize subsequent search results based on the user's past preferences. For example, the learning unit can analyze data on products and services selected by the user in the past and reflect the data in subsequent search results. The learning unit can also update the learning data by reflecting user feedback. For example, the learning unit can update the learning data based on feedback provided by the user to improve the accuracy of subsequent search results. Furthermore, the learning unit can estimate the user's emotions and select learning data based on the estimated user emotions. For example, if the user is relaxed, detailed learning data can be selected. In this way, by learning the user's past preferences, subsequent search results will be more in line with the user's preferences.

[0072] 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 reduce the frequency of information collection and collect information when the user is relaxed. Furthermore, if the user is excited, the collection unit can collect information in real time and provide it immediately. Furthermore, if the user is tired, the collection unit can temporarily stop information collection and resume it after the user has rested. This allows the user's stress to be reduced by adjusting the timing of information collection according to the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the collection unit can input facial expression data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0073] The collection unit can analyze the user's past search history and select the optimal information collection method. The collection unit can, for example, prioritize collecting information from sites that the user has frequently used in the past. For example, the collection unit can collect the latest news articles from news sites that the user has frequently used in the past. The collection unit can also collect related information based on keywords that the user has searched for in the past. For example, the collection unit can collect related product information based on keywords that the user has searched for in the past. Furthermore, the collection unit can optimize the information to be collected for a specific time period based on the user's past search history. For example, the collection unit can collect information related to a specific time period based on information that the user has searched for in the past during that time period. In this way, the optimal information collection method can be selected by analyzing the user's past search history.

[0074] The collection unit can filter information based on the user's current areas of interest when collecting information. The collection unit can, for example, collect information based on product categories in which the user is currently interested. For example, the collection unit can preferentially collect information about smartphones in which the user is currently interested. The collection unit can also preferentially collect information related to topics recently searched by the user. For example, the collection unit can collect news articles related to topics recently searched by the user. Furthermore, the collection unit can filter unnecessary information based on the user's current areas of interest. For example, the collection unit can filter information in categories in which the user is not currently interested. In this way, unnecessary information can be eliminated by filtering information based on the user's current areas of interest.

[0075] When collecting information, the collection unit can select the optimal 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. For example, when the user uses voice input, the collection unit can convert the user's voice into text data using voice recognition technology and collect information based on the text data. Furthermore, when the user uses text input, the collection unit can also collect information using text analysis technology. For example, when the user uses text input, the collection unit can analyze the text entered by the user using text analysis technology and collect information based on the analysis results. Furthermore, when the user uses image input, the collection unit can also collect information using image recognition technology. For example, when the user uses image input, the collection unit can analyze the image entered by the user using image recognition technology and collect information based on the analysis results. This improves the efficiency of information collection by selecting the optimal collection means depending on the user's input method.

[0076] 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, if the user is feeling stressed, the collection unit can prioritize collecting relaxing information. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing music and video information. Furthermore, if the user is excited, the collection unit can prioritize collecting interesting information. For example, if the user is excited, the collection unit can prioritize collecting the latest news and entertainment information. Furthermore, if the user is tired, the collection unit can prioritize collecting easily understandable information. For example, if the user is tired, the collection unit can prioritize collecting concise news articles and summary information. This allows the user to be provided with optimal information by determining the priority of information based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the collection unit can input the user's facial expression data captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0077] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. The collection unit can, for example, collect information about nearby stores based on the user's current location. For example, the collection unit can prioritize collecting information about nearby restaurants and shops based on the user's current location. The collection unit can also prioritize collecting information about nearby parking lots based on the user's geographical location information. For example, the collection unit can prioritize collecting information about nearby parking lots based on the user's current location. Furthermore, the collection unit can also collect information about area-limited special offers and sales by taking into account the user's location information. For example, the collection unit can prioritize collecting information about area-limited discount coupons and sales based on the user's current location. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0078] When collecting information, the collection unit can analyze the user's social media activities and collect related information. The collection unit can, for example, collect information about brands that the user follows on social media. For example, the collection unit can collect the latest product information and campaign information about brands that the user follows on social media. The collection unit can also analyze the content of the user's social media posts and collect related product information. For example, the collection unit can analyze the content of the user's social media posts and collect information about products and services in which the user is interested. Furthermore, the collection unit can also collect related information based on information shared by the user's friends. For example, the collection unit can collect related product information based on product reviews and recommendation information shared by the user's friends. In this way, related information can be efficiently collected by analyzing the user's social media activities.

[0079] When collecting information, the collection unit can customize the collection method by reflecting the user's past feedback. The collection unit can, for example, prioritize information from information sources that the user has previously rated highly. For example, the collection unit can prioritize collecting information from news sites or blogs that the user has previously rated highly. The collection unit can also adjust the categories of information to be collected based on the user's past feedback. For example, the collection unit can prioritize collecting information in categories that the user has previously rated highly. Furthermore, the collection unit can also optimize the collection method by reflecting the user's feedback. For example, the collection unit can adjust the collection method based on the user's feedback and improve the efficiency of information collection. In this way, the collection method can be optimized by reflecting the user's past feedback.

[0080] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. For example, if the user is relaxed, the analysis unit can provide analysis results using detailed graphs and charts. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. For example, if the user is in a hurry, the analysis unit can provide analysis results in a concise text format that focuses on the main points. Furthermore, if the user is excited, the analysis unit can provide visually appealing analysis results. For example, if the user is excited, the analysis unit can provide analysis results using colorful graphs and infographics. By adjusting the presentation of the analysis based on the user's emotions, the analysis unit can provide analysis results that are easy for the user to understand. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input facial expression data of the user captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. The analysis unit can, for example, perform a detailed analysis of information of high importance. For example, the analysis unit can perform a detailed analysis of the price and functions of product information of high importance. The analysis unit can also perform a concise analysis of information of low importance. For example, the analysis unit can perform a summary-style analysis of news articles of low importance. Furthermore, the analysis unit can determine the priority of the analysis according to the importance of the information. For example, the analysis unit can prioritize the analysis of information of high importance and postpone the analysis of information of low importance. In this way, by adjusting the level of detail of the analysis based on the importance of the information, efficient analysis is possible.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can perform analysis that emphasizes price and functionality for product information. For example, the analysis unit can apply an analysis algorithm for comparing prices and functions to product information. The analysis unit can also perform analysis that emphasizes fees and availability for parking lot information. For example, the analysis unit can apply an analysis algorithm for comparing fees and analyzing availability to parking lot information. Furthermore, the analysis unit can also perform analysis that emphasizes ratings and menus for restaurant information. For example, the analysis unit can apply an analysis algorithm for analyzing rating scores and menus to restaurant information. This enables more accurate analysis by applying different analysis algorithms depending on the category of information.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can improve the accuracy of the analysis by referring to, for example, analysis results that the user has given high ratings to in the past. For example, the analysis unit can adjust the analysis algorithm based on the analysis results that the user has given high ratings to in the past, thereby improving the accuracy of the analysis. The analysis unit can also adjust the analysis algorithm based on the user's past analysis results. For example, the analysis unit can analyze the user's past analysis results and optimize the parameters of the analysis algorithm. Furthermore, the analysis unit can analyze the user's past analysis results and improve the accuracy of the analysis. For example, the analysis unit can build a feedback loop to improve the accuracy of the analysis based on the user's past analysis results. In this way, the accuracy of the analysis is improved by referring to the user's past analysis results.

[0084] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, if the user is in a hurry, the analysis unit can provide a concise text-format analysis result that focuses on the main points. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide a detailed analysis result using graphs and charts. Furthermore, if the user is excited, the analysis unit can provide a visually appealing analysis result. For example, if the user is excited, the analysis unit can provide a colorful analysis result using graphs and infographics. This allows the length of the analysis to be adjusted based on the user's emotions, thereby providing the optimal analysis result for the user. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the analysis unit can input the user's facial expression data captured by a camera into the generation AI and have the generation AI estimate the user's emotions.

[0085] During analysis, the analysis unit can determine the priority of analysis based on the time when the information was collected. The analysis unit can, for example, prioritize analyzing the latest information. For example, the analysis unit can prioritize analyzing the latest news articles and product information. The analysis unit can also lower the priority of analyzing older information. For example, the analysis unit can lower the priority of analyzing older news articles and product information. Furthermore, the analysis unit can adjust the priority of analysis based on the time when the information was collected. For example, the analysis unit can prioritize analyzing the latest information and postpone analyzing older information based on the time when the information was collected. In this way, by determining the priority of analysis based on the time when the information was collected, the latest information can be analyzed with priority.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. The analysis unit can, for example, prioritize analysis of highly relevant information. For example, the analysis unit can prioritize analysis of highly relevant product information or news articles. The analysis unit can also postpone the order of analysis of less relevant information. For example, the analysis unit can postpone the order of analysis of less relevant product information or news articles. Furthermore, the analysis unit can also adjust the order of analysis based on the relevance of the information. For example, the analysis unit can adjust the order of analysis based on the relevance of the information. This enables efficient analysis by adjusting the order of analysis based on the relevance of the information.

[0087] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make extensive use of technical terms. For example, if the user has technical expertise, the analysis unit can provide detailed analysis results that make extensive use of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide analysis results in simple terms. For example, if the user does not have technical expertise, the analysis unit can provide analysis results explained in simple terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, the analysis unit can define technical terms or replace them with general terms according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, it is possible to provide analysis results that are easy for the user to understand.

[0088] The providing unit can estimate the user's emotions and adjust the display method of the table format to be provided based on the estimated user's emotions. For example, when the user is relaxed, the providing unit can provide a detailed table format. For example, when the user is relaxed, the providing unit can provide a table format including detailed columns and rows. Furthermore, when the user is in a hurry, the providing unit can provide a concise table format. For example, when the user is in a hurry, the providing unit can provide a concise table format that focuses on the main points. Furthermore, when the user is excited, the providing unit can provide a visually appealing table format. For example, when the user is excited, the providing unit can provide a table format including colorful columns and rows. This allows the optimal display method to be provided by adjusting the display method of the table format based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the providing unit can input facial expression data of the user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0089] The providing unit can adjust the level of detail of the information provided based on the importance of the information when providing the information. For example, the providing unit can provide a detailed table format for information of high importance. For example, the providing unit can provide a detailed table format of prices and functions for product information of high importance. The providing unit can also provide a concise table format for information of low importance. For example, the providing unit can provide a summarized table format for news articles of low importance. Furthermore, the providing unit can adjust the level of detail of the information provided based on the importance of the information. For example, the providing unit can provide a table format of an overview level or a detailed level based on the importance of the information. This enables efficient information provision by adjusting the level of detail of the information provided based on the importance of the information.

[0090] The providing unit can apply different providing algorithms depending on the category of information when providing the information. For example, the providing unit can provide product information in a table format that emphasizes price and functionality. For example, the providing unit can apply a providing algorithm for comparing prices and functionality to the product information. The providing unit can also provide parking lot information in a table format that emphasizes fees and availability. For example, the providing unit can apply a providing algorithm for comparing fees and analyzing availability to the parking lot information. Furthermore, the providing unit can also provide restaurant information in a table format that emphasizes ratings and menus. For example, the providing unit can apply a providing algorithm for analyzing rating scores and menus to the restaurant information. This makes it possible to provide more accurate information by applying different providing algorithms depending on the category of information.

[0091] The providing unit can improve the accuracy of the provision by referring to the user's past provision results when providing the results. The providing unit can improve the accuracy of the provision by referring to, for example, provision results that the user has given high ratings to in the past. For example, the providing unit can adjust the provision algorithm based on the provision results that the user has given high ratings to in the past, thereby improving the accuracy of the provision. The providing unit can also adjust the provision algorithm based on the user's past provision results. For example, the providing unit can analyze the user's past provision results and optimize parameters of the provision algorithm. Furthermore, the providing unit can analyze the user's past provision results and improve the accuracy of the provision. For example, the providing unit can build a feedback loop for improving the accuracy of the provision based on the user's past provision results. As a result, the accuracy of the provision is improved by referring to the user's past provision results.

[0092] The providing unit can estimate the user's emotions and adjust the length of the table format to be provided based on the estimated user's emotions. For example, if the user is in a hurry, the providing unit can provide a short and concise table format. For example, if the user is in a hurry, the providing unit can provide a concise table format that focuses on the main points. The providing unit can also provide a detailed table format if the user is relaxed. For example, if the user is relaxed, the providing unit can provide a table format with detailed columns and rows. Furthermore, if the user is excited, the providing unit can provide a visually appealing table format. For example, if the user is excited, the providing unit can provide a table format with colorful columns and rows. This allows optimal information to be provided to the user by adjusting the length of the table format based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the providing unit can input the user's facial expression data captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0093] The providing unit can determine the priority of provision based on the time when the information is collected at the time of provision. The providing unit can, for example, provide the latest information with priority. For example, the providing unit can provide the latest news articles or product information with priority. The providing unit can also lower the priority of older information when providing it. For example, the providing unit can lower the priority of older news articles or product information when providing it. Furthermore, the providing unit can adjust the priority of provision according to the time when the information was collected. For example, the providing unit can provide the latest information with priority and postpone providing older information according to the time when the information was collected. In this way, by determining the priority of provision based on the time when the information was collected, the latest information can be provided with priority.

[0094] The providing unit can adjust the order of provision based on the relevance of the information when providing the information. The providing unit can, for example, provide highly relevant information preferentially. For example, the providing unit can provide highly relevant product information or news articles preferentially. The providing unit can also postpone the order of provision of less relevant information. For example, the providing unit can postpone the order of provision of less relevant product information or news articles. Furthermore, the providing unit can also adjust the order of provision based on the relevance of the information. For example, the providing unit can adjust the order of provision based on the relevance of the information. This enables efficient information provision by adjusting the order of provision based on the relevance of the information.

[0095] The providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise when providing the information. For example, if the user has technical expertise, the providing unit can provide a table format that uses a lot of technical terminology. For example, if the user has technical expertise, the providing unit can provide a detailed table format that uses a lot of technical terminology. Furthermore, if the user does not have technical expertise, the providing unit can provide a table format in simple language. For example, if the user does not have technical expertise, the providing unit can provide a table format explained in simple language. Furthermore, the providing unit can adjust the use of technical terminology in the provided information according to the user's level of expertise. For example, the providing unit can define technical terms or replace them with general terms according to the user's level of expertise. This makes it possible to provide information that is easy for the user to understand by adjusting the use of technical terminology in the provided information according to the user's level of expertise.

[0096] The learning unit can estimate the user's emotions and select training data based on the estimated user emotions. For example, if the user is relaxed, the learning unit can select detailed training data. For example, if the user is relaxed, the learning unit can select training data including detailed columns and rows. Furthermore, if the user is in a hurry, the learning unit can select concise training data. For example, if the user is in a hurry, the learning unit can select concise training data that focuses on the main points. Furthermore, if the user is excited, the learning unit can select visually appealing training data. For example, if the user is excited, the learning unit can select training data including colorful columns and rows. This improves the accuracy of learning by selecting training data based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the learning unit can input user facial expression data captured with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0097] During learning, the learning unit can optimize the learning algorithm by referring to past learning data. The learning unit can adjust the learning algorithm based on, for example, the user's past preferences. For example, the learning unit can optimize the parameters of the learning algorithm based on the user's past preferences. The learning unit can also analyze past learning data to improve the accuracy of the learning algorithm. For example, the learning unit can tune the learning algorithm based on the past learning data. Furthermore, the learning unit can optimize the parameters of the learning algorithm by referring to the past learning data. For example, the learning unit can adjust the parameters of the learning algorithm based on the past learning data to improve the accuracy of learning. In this way, the accuracy of the learning algorithm is improved by referring to the past learning data.

[0098] The learning unit can update the learning data by reflecting user feedback during learning. For example, the learning unit can update the learning data based on feedback provided by the user. For example, the learning unit can add or modify the learning data based on feedback provided by the user. The learning unit can also improve the accuracy of the learning data by reflecting user feedback. For example, the learning unit can improve the quality of the learning data based on user feedback. Furthermore, the learning unit can analyze user feedback and optimize the learning data. For example, the learning unit can optimize the structure and content of the learning data based on user feedback. In this way, the accuracy of the learning data is improved by reflecting user feedback.

[0099] During learning, the learning unit can analyze the user's past preferences and reflect them in subsequent searches. The learning unit can, for example, optimize subsequent search results based on the user's past preferences. For example, the learning unit can adjust subsequent search algorithms based on the user's past preferences. The learning unit can also analyze the user's past preferences and customize subsequent search results. For example, the learning unit can adjust subsequent search results to suit the user's preferences based on the user's past preferences. Furthermore, the learning unit can reflect the user's past preferences to improve the accuracy of subsequent search results. For example, the learning unit can improve the quality of subsequent search results based on the user's past preferences. In this way, by analyzing the user's past preferences, subsequent search results will better match the user's preferences.

[0100] The learning unit can estimate the user's emotions and adjust the frequency of learning based on the estimated user emotions. For example, the learning unit can increase the frequency of learning when the user is relaxed. For example, the learning unit can increase the frequency of learning and perform detailed learning when the user is relaxed. The learning unit can also decrease the frequency of learning when the user is in a hurry. For example, the learning unit can reduce the frequency of learning and perform brief learning when the user is in a hurry. Furthermore, the learning unit can adjust the frequency of learning when the user is excited. For example, the learning unit can adjust the frequency of learning when the user is excited and perform visually appealing learning. This improves learning efficiency by adjusting the frequency of learning based on the user's emotions. Emotion estimation is performed using technologies such as facial expression recognition and voice analysis. For example, the learning unit can input user facial expression data captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0101] During learning, the learning unit can weight the learning data based on the time when the information was collected. For example, the learning unit can weight the learning data by emphasizing the most recent information. For example, the learning unit can weight the learning data by emphasizing the most recent news articles or product information. The learning unit can also lower the weighting for older information during learning. For example, the learning unit can lower the weighting for older news articles or product information during learning. Furthermore, the learning unit can adjust the weighting of the learning data depending on the time when the information was collected. For example, the learning unit can emphasize the most recent information and lower the weighting for older information depending on the time when the information was collected. In this way, weighting the learning data based on the time when the information was collected enables learning that emphasizes the most recent information.

[0102] During learning, the learning unit can integrate information from different data sources to expand the learning data. For example, the learning unit can integrate information collected from multiple sites to expand the learning data. For example, the learning unit can integrate information collected from multiple news sites and blogs to expand the learning data. The learning unit can also integrate information from different categories to expand the learning data. For example, the learning unit can integrate information from the news category and the technology category to expand the learning data. Furthermore, the learning unit can also integrate information in different formats (text, images, audio, etc.) to expand the learning data. For example, the learning unit can integrate text data, image data, and audio data to expand the learning data. In this way, by integrating information from different data sources, the comprehensiveness of the learning data is improved.

[0103] The learning unit can adjust the learning algorithm by reflecting user feedback during learning. The learning unit can adjust the learning algorithm, for example, based on user feedback. For example, the learning unit can optimize parameters of the learning algorithm based on user feedback. The learning unit can also improve the accuracy of the learning algorithm by reflecting user feedback. For example, the learning unit can tune the learning algorithm based on user feedback. Furthermore, the learning unit can analyze user feedback and optimize the learning algorithm. For example, the learning unit can optimize the structure and content of the learning algorithm based on user feedback. In this way, the accuracy of the learning algorithm is improved by reflecting user feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and learning 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 the user's facial expressions and voice using the camera 42 and microphone 38B of the smart device 14 and analyze them using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the information collected by the specific processing unit 290 of the data processing device 12 and generate a comparison result. For example, the provision unit can provide the analysis result in a table format using the control unit 46A of the smart device 14. For example, the learning unit can learn the user's preferences using the specific processing unit 290 of the data processing device 12 and reflect them in subsequent searches. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and learning unit described above 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 the user's facial expressions and voice using the camera 42 and microphone 238 of the smart glasses 214, and analyze the collected information by the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the collected information by the specific processing unit 290 of the data processing device 12 and generate a comparison result. For example, the provision unit can provide the analysis result in a table format by the control unit 46A of the smart glasses 214. For example, the learning unit can learn the user's preferences by the specific processing unit 290 of the data processing device 12 and reflect the learned preferences in subsequent searches. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and learning unit described above 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 the user's facial expressions and voice using the camera 42 and microphone 238 of the headset type terminal 314, and analyze them using the specific processing unit 290 of the data processing device 12. For example, the analysis unit analyzes the information collected by the specific processing unit 290 of the data processing device 12 and generates a comparison result. For example, the provision unit can provide the analysis result in a table format using the control unit 46A of the headset type terminal 314. For example, the learning unit can learn the user's preferences using the specific processing unit 290 of the data processing device 12 and reflect them in subsequent searches. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, provision unit, and learning unit described above 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 the user's facial expressions and voice using the camera 42 and microphone 238 of the robot 414, and analyze them using the specific processing unit 290 of the data processing device 12. For example, the analysis unit can analyze the information collected by the specific processing unit 290 of the data processing device 12 and generate a comparison result. For example, the provision unit can provide the analysis result in a table format using the control unit 46A of the robot 414. For example, the learning unit can learn the user's preferences using the specific processing unit 290 of the data processing device 12 and reflect them in subsequent searches.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The analysis unit can improve the accuracy of search results based on the user's past search history. For example, the analysis unit can analyze keywords that the user has searched for in the past and prioritize analysis of related information. The analysis unit can also prioritize analysis of similar information based on information that the user has previously given a high rating. Furthermore, the analysis unit can customize the way search results are displayed based on the user's past search history. In this way, by utilizing the user's past search history, the accuracy of search results can be improved and more useful information can be provided to the user.

[0106] The collection unit can prioritize collection of relevant information based on the user's current geographical location information. For example, the collection unit can collect nearby store information and event information based on the user's current location. The collection unit can also prioritize collection of area-limited special offers and sales information based on the user's location information. Furthermore, the collection unit can analyze the user's movement history and collect relevant information. In this way, by utilizing the user's geographical location information, more relevant information can be provided.

[0107] The providing unit can estimate the user's emotions and adjust the priority of information to be provided based on the estimated user's emotions. For example, if the user is feeling stressed, it can provide preferentially information that helps the user relax. Also, if the user is excited, it can provide preferentially information that piques the user's interest. Furthermore, if the user is tired, it can provide preferentially information that is easy to understand. In this way, by adjusting the priority of information based on the user's emotions, it is possible to provide the user with the most suitable information.

[0108] The learning unit can optimize subsequent search results based on the user's past preferences. For example, the learning unit can analyze data on products and services selected by the user in the past and reflect this in subsequent search results. The learning unit can also update the learning data by reflecting user feedback. Furthermore, the learning unit can customize the way search results are displayed based on the user's past preferences. In this way, by utilizing the user's past preferences, subsequent search results will be more in line with the user's preferences.

[0109] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user's emotions. For example, if the user is feeling stressed, the frequency of information collection can be reduced and collection can be performed when the user is relaxed. Also, if the user is excited, information can be collected in real time and provided immediately. Furthermore, if the user is tired, information collection can be temporarily stopped and resumed after the user has rested. In this way, the timing of information collection can be adjusted according to the user's emotions, thereby reducing the user's stress.

[0110] The analysis unit can apply different analysis algorithms depending on the category of information. For example, for product information, analysis can be performed with emphasis on price and functionality. For parking information, analysis can be performed with emphasis on fees and availability. For restaurant information, analysis can be performed with emphasis on ratings and menus. By applying different analysis algorithms depending on the category of information, more accurate analysis is possible.

[0111] The providing unit can estimate the user's emotion and adjust the display method of the table format to be provided based on the estimated user's emotion. For example, if the user is relaxed, a detailed table format can be provided. If the user is in a hurry, a concise table format can be provided. Furthermore, if the user is excited, a visually appealing table format can be provided. In this way, by adjusting the display method of the table format based on the user's emotion, it is possible to provide an optimal display method for the user.

[0112] The learning unit can estimate the user's emotions and select learning data based on the estimated user's emotions. For example, if the user is relaxed, detailed learning data can be selected. If the user is in a hurry, concise learning data can be selected. Furthermore, if the user is excited, visually appealing learning data can be selected. Thus, by selecting learning data based on the user's emotions, the accuracy of learning is improved.

[0113] The collection unit can analyze the user's social media activities and collect related information. For example, it can collect information about brands the user follows on social media. It can also analyze the content of the user's social media posts and collect related product information. It can also collect related information based on information shared by the user's friends. In this way, it is possible to efficiently collect related information by analyzing the user's social media activities.

[0114] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, it can provide a short and to-the-point analysis result. If the user is relaxed, it can provide a detailed analysis result. Furthermore, if the user is excited, it can provide a visually appealing analysis result. In this way, by adjusting the length of the analysis based on the user's emotions, it is possible to provide the optimal analysis result for the user.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The collection unit collects information from multiple sites based on the information entered by the user. For example, the collection unit can use web scraping technology to collect information from news sites and social media sites. The collection unit can also obtain data using an API. Furthermore, the collection unit can select the optimal collection method depending on the user's input method (voice, text, image, etc.). For example, if the user uses voice input, the collection unit can collect information using voice recognition technology. Step 2: The analysis unit analyzes the information collected by the collection unit and generates a comparison result. For example, the analysis unit can apply statistical analysis or machine learning algorithms to analyze the collected information. The analysis unit can also apply different analysis algorithms depending on the category of information. For example, for product information, the analysis can focus on price and functionality. Step 3: The providing unit provides the results analyzed by the analyzing unit in a table format. For example, the providing unit can provide the generated comparison results in Excel format or HTML table format. The providing unit can also estimate the user's emotion and adjust the display method of the table format to be provided based on the estimated user's emotion. For example, if the user is relaxed, a detailed table format can be provided. Step 4: The learning unit learns the user's preferences based on the results provided by the providing unit and reflects them in subsequent searches. For example, the learning unit can optimize subsequent search results based on the user's past preferences. The learning unit can also update the learning data to reflect user feedback.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

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

[0124] The 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.

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

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

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

[0128] Fig. 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.

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

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

[0131] In the 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.

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

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0135] The data processing system 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

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

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

[0147] In the 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.

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

[0149] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0151] The data processing system 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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 AI 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 analysis unit that analyzes the information collected by the collection unit; a providing unit that provides the results analyzed by the analyzing unit in a table format; a learning unit that learns user preferences based on the results provided by the providing unit; Equipped with A system characterized by:

2. The collecting unit Collect information from multiple sites 2. The system of claim 1.

3. The analysis unit Analyze the collected information and generate comparison results 2. The system of claim 1.

4. The providing unit Present the generated comparison results in a tabular format 2. The system of claim 1.

5. The learning unit Learns your past preferences and applies them to future searches 2. The system of claim 1.

6. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze users' past search history and select the most appropriate method of collecting information 2. The system of claim 1.

8. The collecting unit As information is collected, it is filtered based on the user's current interests.

2. The system of claim 1.

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