System

Abstract

An object of a system according to an embodiment is to efficiently provide only information in which a user is interested.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a filtering unit, and a providing unit. The collection unit collects behavior data of the user. The analysis unit analyzes the data collected by the collection unit. The filtering unit filters news based on the data analyzed by the analyzing unit. The providing unit provides the news filtered by the filtering 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] With conventional technologies, it is difficult to efficiently provide only the information that interests the user, and there is a risk of stress due to information overload.

[0005] The system according to the embodiment aims to efficiently provide only information that is of interest to the user. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a filtering unit, and a providing unit. The collection unit collects user behavior data. The analysis unit analyzes the data collected by the collection unit. The filtering unit filters news based on the data analyzed by the analysis unit. The providing unit provides the news filtered by the filtering unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide only information that is of interest to the user. [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) A news delivery system according to an embodiment of the present invention is a system that collects, analyzes, filters, and provides user behavioral data. This news delivery system provides personalized news by collecting, analyzing, and filtering the user behavioral data. For example, the news delivery system collects and analyzes user behavioral data. Next, the news delivery system uses AI to optimize individual news articles based on the collected data and filter out advertisements and negative information. Furthermore, the news delivery system appropriately organizes only information that interests the user, providing a stress-free experience. For example, if a user spends a lot of time on a specific news category, the news delivery system prioritizes providing news related to that category. This reduces stress caused by information overload and enables efficient news consumption. This allows the news delivery system to provide personalized news based on the user's behavioral data, thereby reducing stress caused by information overload. For example, the news delivery system appropriately organizes only information that interests the user, providing a stress-free experience. Furthermore, the news delivery system filters out advertisements and negative information, providing a comfortable news viewing environment for the user.

[0029] A news provision system according to an embodiment includes a collection unit, an analysis unit, a filtering unit, and a provision unit. The collection unit collects user behavioral data. The user behavioral data includes, but is not limited to, website browsing history, click data, and purchase history. The collection unit can track, for example, the user's social media behavior and news viewing time. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, but is not limited to, an algorithm used and a purpose of the analysis. The analysis unit generates data for selecting news based on, for example, the user's interests and providing the news in an optimal order. The filtering unit filters the news based on the data analyzed by the analysis unit. The filtering includes, for example, but is not limited to, identifying and excluding advertisements and negative information. The filtering unit can, for example, automatically identify and exclude advertisements and negative news using AI. The provision unit provides the news filtered by the filtering unit. The provision includes, for example, but is not limited to, displaying news in an optimal order based on the user's interests. The news providing unit can adjust the news display time so that the user can view the news within the time set by the user. As a result, the news providing system according to the embodiment can provide personalized news based on the user's behavioral data, thereby reducing stress caused by information overload.

[0030] The filtering unit can identify and filter out advertisements or specific inappropriate information. Examples of advertisements include, but are not limited to, banner advertisements and text advertisements. The filtering unit can automatically identify and filter out advertisements using, for example, AI. Examples of inappropriate information include, but are not limited to, violent content and discriminatory content. The filtering unit can automatically identify and filter out inappropriate information using, for example, AI. This allows the user to have a stress-free experience by filtering out advertisements and negative information.

[0031] The collection unit can track the user's social media posts or news viewing time. Social media posts include, but are not limited to, Facebook® posts and Twitter® tweets. The collection unit can, for example, track the user's social media behavior and collect the posted content. News viewing time includes, but is not limited to, for example, page stay time and scrolling amount. The collection unit can, for example, track how much time the user spends viewing news. This allows for more accurate personalization by tracking the user's social media behavior and news viewing time.

[0032] The providing unit can provide news in an order based on the user's interests, based on the user's interests. The user's interests are identified, for example, based on past browsing history, survey results, etc., but are not limited to such examples. For example, if the user is interested in sports news, the providing unit can preferentially display sports news. Furthermore, the providing unit can use an algorithm for providing news in an optimal order based on the user's interests. This improves user satisfaction by providing news based on the user's interests.

[0033] The providing unit can change the news display time based on a user setting so that the news can be viewed within the time set by the user. The news display time is adjusted based on, for example, a time limit based on the user setting, optimization of the display time, etc., but is not limited to such examples. The providing unit can adjust the news display time, for example, so that the news can be viewed within the time set by the user. Furthermore, the providing unit can dynamically change the news display time based on the user setting. This enables the news to be viewed within the time set by the user, thereby enabling efficient news consumption.

[0034] The collection unit can analyze the user's past behavioral data and select an effective collection method. The collection unit can customize the collection method based on, for example, news categories that the user frequently viewed in the past. The collection unit can also analyze the user's past browsing time and determine the optimal collection timing. The collection unit can also dynamically adjust the collection method based on the user's past behavioral patterns. This enables more effective data collection by analyzing past behavioral data. Past behavioral data includes, for example, past click data, purchase history, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0035] When collecting behavioral data, the collection unit may filter the data based on the user's current areas of interest. For example, the collection unit may prioritize collection of news categories in which the user is currently interested. The collection unit may also collect relevant data based on the user's current search history. The collection unit may also analyze the user's current social media activities and collect data based on the user's areas of interest. By collecting data based on the user's current areas of interest, more relevant data can be obtained. The current areas of interest may be identified based on, for example, real-time search keywords, recent browsing history, etc., but are not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0036] When collecting behavioral data, the collection unit can select an effective collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user is using text input, the collection unit can prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can prioritize collecting image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the input method. User input methods include, but are not limited to, voice input, text input, and image input. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0037] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit can, for example, prioritize collecting news related to the user's current location. The collection unit can also collect relevant data based on the user's past location information. The collection unit can also update the user's current location information in real time and collect relevant data. This allows more relevant data to be collected by taking the geographical location information into account. Geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0038] When collecting behavioral data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect related data based on news shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activities, more relevant data can be collected. Social media activities include, for example, posting frequency, engagement rate, etc., but are not limited to such examples. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0039] When collecting behavioral data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can prioritize collecting news categories that the user has previously rated highly. The collection unit can also dynamically adjust the collection method based on the user's past feedback. The collection unit can also analyze the user's past feedback and select the optimal collection method. This enables more effective data collection by reflecting past feedback. Past feedback includes, for example, user evaluation comments, survey results, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0040] During data analysis, the analysis unit can change the level of detail of the analysis based on the user's level of interest. For example, the analysis unit can perform a detailed analysis on a news category in which the user has a high level of interest. Furthermore, the analysis unit can perform a simplified analysis on a news category in which the user has a low level of interest. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the user's level of interest. This enables more effective data analysis by adjusting the level of detail of the analysis based on the level of interest. The level of detail of the analysis is adjusted based on, for example, the depth of the analysis, the range of data used, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0041] When analyzing data, the analysis unit can apply an effective analysis algorithm depending on the data category. The analysis unit can, for example, select an optimal analysis algorithm for each news category. The analysis unit can also apply different analysis methods depending on the data category. The analysis unit can also dynamically switch between analysis algorithms for each data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category. Analysis algorithms include, but are not limited to, clustering algorithms and regression analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0042] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and select the optimal analysis method. This improves the accuracy of the analysis by referring to the past analysis results. Past analysis results include, for example, past trend data, analysis reports, etc., but are not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0043] When analyzing data, the analysis unit can set analysis priorities based on the time the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also determine the analysis priorities based on past data. The analysis unit can also dynamically adjust the analysis priorities according to the time the data was collected. This enables more effective data analysis by determining the priorities based on the time the data was collected. The time the data was collected includes, for example, the date and time of collection, the frequency of collection, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0044] During data analysis, the analysis unit can change the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. The analysis unit can also determine an optimal analysis order based on the relevance of the data. This enables more effective data analysis by adjusting the order of analysis based on the relevance. The relevance of the data is evaluated based on, for example, correlation analysis, co-occurrence networks, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0045] During data analysis, the analysis unit can change the use of technical terms in the analysis according to the user's level of expertise. The analysis unit can, for example, adjust the use of technical terms according to the user's level of expertise. The analysis unit can also simplify technical terms to make them easier for the user to understand. The analysis unit can also provide optimal analysis results based on the user's level of expertise. By adjusting the use of technical terms according to the level of expertise, analysis results that are easy for the user to understand are provided. The level of expertise is identified based on, for example, questionnaire results, past behavioral data, etc., but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0046] The filtering unit can improve the accuracy of filtering by taking into account the interrelationships between data during filtering. The filtering unit can, for example, analyze the interrelationships between data and preferentially filter highly related information. The filtering unit can also improve the accuracy of filtering based on the interrelationships between data. The filtering unit can also set optimal filtering criteria by taking into account the interrelationships between data. This improves the accuracy of filtering by taking into account the interrelationships between data. The interrelationships between data are evaluated based on, for example, correlation analysis, co-occurrence networks, etc., but are not limited to such examples. Some or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0047] The filtering unit can perform filtering while taking into consideration attribute information of the data submitter. The filtering unit can perform filtering based on, for example, the reliability of the data submitter. The filtering unit can also analyze the attribute information of the data submitter and set filtering criteria. The filtering unit can also perform filtering based on the data submitter's past submission history. In this way, more reliable information is provided by taking into consideration the attribute information of the submitter. The attribute information of the submitter includes, for example, age, gender, occupation, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0048] The filtering unit can weight the filtering based on the frequency of data submission during filtering. For example, the filtering unit can prioritize filtering of data with a high submission frequency. The filtering unit can also adjust the filtering weight according to the submission frequency. The filtering unit can also set optimal filtering criteria based on the submission frequency. As a result, by weighting the filtering based on the submission frequency, more important information is provided preferentially. The submission frequency includes, for example, the number of submissions, the submission interval, etc., but is not limited to such examples. Some or all of the above-described processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0049] The filtering unit can perform filtering while taking into account the geographical distribution of data. For example, the filtering unit can prioritize filtering information related to the area where the user is currently located. The filtering unit can also set filtering criteria based on the geographical distribution of data. The filtering unit can also set optimal filtering criteria taking into account the geographical distribution of data. This allows more relevant information to be provided by taking the geographical distribution into account. Examples of geographical distribution include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0050] The filtering unit can improve the accuracy of filtering by referring to literature related to the data during filtering. The filtering unit can improve the accuracy of filtering, for example, based on literature related to the data. The filtering unit can also set filtering criteria by referring to literature related to the data. The filtering unit can also set optimal filtering criteria taking into account literature related to the data. As a result, the accuracy of filtering is improved by referring to literature related to the data. Examples of literature related to the data include, but are not limited to, academic papers and technical reports. Some or all of the above-described processing in the filtering unit may be performed using AI, for example, or without using AI.

[0051] The filtering unit can perform filtering taking into account the market value of the data. For example, the filtering unit can prioritize filtering of data with high market value. The filtering unit can also set filtering criteria based on the market value of the data. The filtering unit can also set optimal filtering criteria taking into account the market value of the data. This allows more important information to be provided by taking market value into account. Market value includes, for example, sales data, market research results, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0052] The providing unit can change the level of detail of the news provided based on the user's level of interest when providing news. For example, the providing unit can provide detailed information for a news category in which the user has a high level of interest. The providing unit can also provide simplified information for a news category in which the user has a low level of interest. The providing unit can also dynamically adjust the level of detail of the news provided according to the user's level of interest. This enables more effective news provision by adjusting the level of detail of the news provided based on the level of interest. The level of detail of the news provided is adjusted based on, for example, the depth of the information, the range of data to be provided, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0053] When providing news, the providing unit can apply an effective providing algorithm depending on the news category. The providing unit can, for example, select an optimal providing algorithm for each news category. The providing unit can also apply different providing methods depending on the news category. The providing unit can also dynamically switch the providing algorithm for each news category. This improves the accuracy of news provision by applying the optimal providing algorithm depending on the category. Examples of providing algorithms include, but are not limited to, recommendation algorithms and personalization algorithms. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0054] When providing news, the providing unit can improve the accuracy of the news provision by referring to the user's past provision results. The providing unit can, for example, adjust the provision algorithm based on the user's past provision results. The providing unit can also improve the accuracy of the news provision by referring to the user's past provision results. The providing unit can also analyze the user's past provision results and select the optimal provision method. This improves the accuracy of the news provision by referring to the past provision results. Past provision results include, for example, past click data, user feedback, etc., but are not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0055] When providing news, the providing unit can set a priority of provision based on the time when the news was collected. For example, the providing unit can provide the latest news preferentially. Furthermore, the providing unit can determine the priority of provision based on past news. Furthermore, the providing unit can dynamically adjust the priority of provision according to the time when the news was collected. This enables more effective news provision by determining the priority based on the time when the news was collected. The time when the news was collected includes, for example, the date and time of collection, the frequency of collection, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0056] The providing unit can change the order of news provision based on the relevance of the news when providing the news. The providing unit can, for example, provide highly relevant news preferentially. The providing unit can also dynamically adjust the order of provision according to the relevance of the news. The providing unit can also determine an optimal order of provision based on the relevance of the news. This enables more effective news provision by adjusting the order of provision based on the relevance. The relevance of news is evaluated based on, for example, correlation analysis, co-occurrence networks, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0057] When providing news, the providing unit can change the use of technical terms in the provided news depending on the user's level of expertise. The providing unit can, for example, adjust the use of technical terms depending on the user's level of expertise. The providing unit can also simplify technical terms to make them easier for the user to understand. The providing unit can also select an optimal provision method based on the user's level of expertise. As a result, by adjusting the use of technical terms depending on the level of expertise, news that is easy for the user to understand is provided. The use of technical terms is adjusted based on, for example, the definition of the technical terms, frequency of use, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[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 news delivery system may further include a purchase data collection unit that collects and analyzes user purchase data. The purchase data collection unit may collect, for example, the user's online shopping history and in-store purchase history. The analysis unit may estimate the user's interests and preferences based on the collected purchase data and generate data for providing appropriate news. For example, if the user frequently purchases a particular product, news related to that product may be provided. Also, if the user purchases a new product, news related to that product may be provided. This makes it possible to provide personalized news based on the user's purchase data.

[0060] The news delivery system may further include a location information collection unit that collects and analyzes user location information. The location information collection unit may collect, for example, the user's current location and movement history. The analysis unit may estimate the user's interests and behavioral patterns based on the collected location information and generate data for providing appropriate news. For example, if the user frequently visits a specific area, news related to that area may be provided. Also, if the user is traveling, news related to the user's travel destination may be provided. This makes it possible to provide personalized news based on the user's location information.

[0061] The news delivery system may further include a calendar data collection unit that collects and analyzes the user's calendar data. The calendar data collection unit may, for example, collect the user's schedule and event information. The analysis unit may generate data for providing news based on the collected calendar data in accordance with the user's interests and schedule. For example, if the user plans to attend a specific event, news related to that event may be provided. Furthermore, if the user has a busy schedule, news that can be read in a short amount of time may be provided. This enables personalized news delivery based on the user's calendar data.

[0062] The news providing system may further include a reading data collection unit that collects and analyzes the user's reading data. The reading data collection unit may, for example, collect a history of books and articles read by the user. The analysis unit may estimate the user's interests and preferences based on the collected reading data and generate data for providing appropriate news. For example, if the user frequently reads books of a particular genre, news related to that genre may be provided. Also, if the user starts reading a new book, news related to that book may be provided. This makes it possible to provide personalized news based on the user's reading data.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The collection unit collects user behavior data, including website browsing history, click data, purchase history, social media behavior, and news browsing time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on the algorithm used and the purpose of the analysis, and generates data to select news based on the user's interests and provide it in the optimal order. Step 3: The filtering unit filters the news based on the data analyzed by the analysis unit. Filtering includes identifying and filtering out advertisements and negative information, and is done automatically using AI. Step 4: The providing unit provides the news filtered by the filtering unit. The providing includes displaying the news in an optimal order based on the user's interests and adjusting the news display time so that the user can view the news within the time period set by the user.

[0065] (Example 2) A news delivery system according to an embodiment of the present invention is a system that collects, analyzes, filters, and provides user behavioral data. This news delivery system provides personalized news by collecting, analyzing, and filtering the user behavioral data. For example, the news delivery system collects and analyzes user behavioral data. Next, the news delivery system uses AI to optimize individual news articles based on the collected data and filter out advertisements and negative information. Furthermore, the news delivery system appropriately organizes only information that interests the user, providing a stress-free experience. For example, if a user spends a lot of time on a specific news category, the news delivery system prioritizes providing news related to that category. This reduces stress caused by information overload and enables efficient news consumption. This allows the news delivery system to provide personalized news based on the user's behavioral data, thereby reducing stress caused by information overload. For example, the news delivery system appropriately organizes only information that interests the user, providing a stress-free experience. Furthermore, the news delivery system filters out advertisements and negative information, providing a comfortable news viewing environment for the user.

[0066] A news provision system according to an embodiment includes a collection unit, an analysis unit, a filtering unit, and a provision unit. The collection unit collects user behavioral data. The user behavioral data includes, but is not limited to, website browsing history, click data, and purchase history. The collection unit can track, for example, the user's social media behavior and news viewing time. The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on, for example, but is not limited to, an algorithm used and a purpose of the analysis. The analysis unit generates data for selecting news based on, for example, the user's interests and providing the news in an optimal order. The filtering unit filters the news based on the data analyzed by the analysis unit. The filtering includes, for example, but is not limited to, identifying and excluding advertisements and negative information. The filtering unit can, for example, automatically identify and exclude advertisements and negative news using AI. The provision unit provides the news filtered by the filtering unit. The provision includes, for example, but is not limited to, displaying news in an optimal order based on the user's interests. The news providing unit can adjust the news display time so that the user can view the news within the time set by the user. As a result, the news providing system according to the embodiment can provide personalized news based on the user's behavioral data, thereby reducing stress caused by information overload.

[0067] The filtering unit can identify and filter out advertisements or specific inappropriate information. Examples of advertisements include, but are not limited to, banner advertisements and text advertisements. The filtering unit can automatically identify and filter out advertisements using, for example, AI. Examples of inappropriate information include, but are not limited to, violent content and discriminatory content. The filtering unit can automatically identify and filter out inappropriate information using, for example, AI. This allows the user to have a stress-free experience by filtering out advertisements and negative information.

[0068] The collection unit can track the user's social media posts or news viewing time. Social media posts include, but are not limited to, Facebook posts, Twitter tweets, etc. The collection unit can, for example, track the user's social media behavior and collect the posted content. News viewing time includes, but is not limited to, page stay time, amount of scrolling, etc. The collection unit can, for example, track how much time the user spends viewing news. This allows for more accurate personalization by tracking the user's social media behavior and news viewing time.

[0069] The providing unit can provide news in an order based on the user's interests, based on the user's interests. The user's interests are identified, for example, based on past browsing history, survey results, etc., but are not limited to such examples. For example, if the user is interested in sports news, the providing unit can preferentially display sports news. Furthermore, the providing unit can use an algorithm for providing news in an optimal order based on the user's interests. This improves user satisfaction by providing news based on the user's interests.

[0070] The providing unit can change the news display time based on a user setting so that the news can be viewed within the time set by the user. The news display time is adjusted based on, for example, a time limit based on the user setting, optimization of the display time, etc., but is not limited to such examples. The providing unit can adjust the news display time, for example, so that the news can be viewed within the time set by the user. Furthermore, the providing unit can dynamically change the news display time based on the user setting. This enables the news to be viewed within the time set by the user, thereby enabling efficient news consumption.

[0071] The collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can delay the collection timing to collect data in a relaxed state. Furthermore, if the user is relaxed, the collection unit can advance the collection timing to collect more data. Furthermore, if the user is excited, the collection unit can adjust the collection timing to record emotional fluctuations in detail. This enables more appropriate data collection by adjusting the collection timing according to the user's emotions. Emotion estimation is performed using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0072] The collection unit can analyze the user's past behavioral data and select an effective collection method. The collection unit can customize the collection method based on, for example, news categories that the user frequently viewed in the past. The collection unit can also analyze the user's past browsing time and determine the optimal collection timing. The collection unit can also dynamically adjust the collection method based on the user's past behavioral patterns. This enables more effective data collection by analyzing past behavioral data. Past behavioral data includes, for example, past click data, purchase history, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0073] When collecting behavioral data, the collection unit may filter the data based on the user's current areas of interest. For example, the collection unit may prioritize collection of news categories in which the user is currently interested. The collection unit may also collect relevant data based on the user's current search history. The collection unit may also analyze the user's current social media activities and collect data based on the user's areas of interest. By collecting data based on the user's current areas of interest, more relevant data can be obtained. The current areas of interest may be identified based on, for example, real-time search keywords, recent browsing history, etc., but are not limited to such examples. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0074] When collecting behavioral data, the collection unit can select an effective collection means depending on the user's input method. For example, if the user is using voice input, the collection unit can prioritize collecting voice data. Furthermore, if the user is using text input, the collection unit can prioritize collecting text data. Furthermore, if the user is using image input, the collection unit can prioritize collecting image data. This improves the efficiency of data collection by selecting the optimal collection means depending on the input method. User input methods include, but are not limited to, voice input, text input, and image input. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0075] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing content. Furthermore, if the user is relaxed, the collection unit can prioritize collecting interesting news. Furthermore, if the user is excited, the collection unit can prioritize collecting entertaining content. This enables more appropriate data collection by determining the priority of data according to the user's emotions. Emotion estimation is performed using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0076] When collecting behavioral data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. The collection unit can, for example, prioritize collecting news related to the user's current location. The collection unit can also collect relevant data based on the user's past location information. The collection unit can also update the user's current location information in real time and collect relevant data. This allows more relevant data to be collected by taking the geographical location information into account. Geographical location information includes, for example, GPS data, IP address, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0077] When collecting behavioral data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can collect related data based on news shared by the user on social media. The collection unit can also analyze the content of the user's social media posts and collect related data. The collection unit can also collect related data by referring to the activities of the user's friends on social media. In this way, by analyzing social media activities, more relevant data can be collected. Social media activities include, for example, posting frequency, engagement rate, etc., but are not limited to such examples. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without using AI.

[0078] When collecting behavioral data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit can prioritize collecting news categories that the user has previously rated highly. The collection unit can also dynamically adjust the collection method based on the user's past feedback. The collection unit can also analyze the user's past feedback and select the optimal collection method. This enables more effective data collection by reflecting past feedback. Past feedback includes, for example, user evaluation comments, survey results, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed, for example, using AI, or may be performed without using AI.

[0079] The analysis unit can estimate the user's emotions and change the data analysis method based on the estimated user's emotions. For example, if the user is stressed, the analysis unit can use a simple analysis method. If the user is relaxed, the analysis unit can use a detailed analysis method. If the user is excited, the analysis unit can provide a visually stimulating analysis result. This allows for more appropriate data analysis by adjusting the analysis method according to the user's emotions. Emotion estimation is performed using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0080] During data analysis, the analysis unit can change the level of detail of the analysis based on the user's level of interest. For example, the analysis unit can perform a detailed analysis on a news category in which the user has a high level of interest. Furthermore, the analysis unit can perform a simplified analysis on a news category in which the user has a low level of interest. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the user's level of interest. This enables more effective data analysis by adjusting the level of detail of the analysis based on the level of interest. The level of detail of the analysis is adjusted based on, for example, the depth of the analysis, the range of data used, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0081] When analyzing data, the analysis unit can apply an effective analysis algorithm depending on the data category. The analysis unit can, for example, select an optimal analysis algorithm for each news category. The analysis unit can also apply different analysis methods depending on the data category. The analysis unit can also dynamically switch between analysis algorithms for each data category. This improves the accuracy of the analysis by applying the optimal analysis algorithm depending on the category. Analysis algorithms include, but are not limited to, clustering algorithms and regression analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0082] When analyzing data, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can, for example, adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit can also analyze the user's past analysis results and select the optimal analysis method. This improves the accuracy of the analysis by referring to the past analysis results. Past analysis results include, for example, past trend data, analysis reports, etc., but are not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0083] The analysis unit can estimate the user's emotions and change the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating display method. By adjusting the display method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is performed using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0084] When analyzing data, the analysis unit can set analysis priorities based on the time the data was collected. For example, the analysis unit can prioritize the analysis of the most recent data. The analysis unit can also determine the analysis priorities based on past data. The analysis unit can also dynamically adjust the analysis priorities according to the time the data was collected. This enables more effective data analysis by determining the priorities based on the time the data was collected. The time the data was collected includes, for example, the date and time of collection, the frequency of collection, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0085] During data analysis, the analysis unit can change the order of analysis based on the relevance of the data. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also dynamically adjust the order of analysis according to the relevance of the data. The analysis unit can also determine an optimal analysis order based on the relevance of the data. This enables more effective data analysis by adjusting the order of analysis based on the relevance. The relevance of the data is evaluated based on, for example, correlation analysis, co-occurrence networks, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI.

[0086] During data analysis, the analysis unit can change the use of technical terms in the analysis according to the user's level of expertise. The analysis unit can, for example, adjust the use of technical terms according to the user's level of expertise. The analysis unit can also simplify technical terms to make them easier for the user to understand. The analysis unit can also provide optimal analysis results based on the user's level of expertise. By adjusting the use of technical terms according to the level of expertise, analysis results that are easy for the user to understand are provided. The level of expertise is identified based on, for example, questionnaire results, past behavioral data, etc., but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI.

[0087] The filtering unit can estimate the user's emotions and change the filtering criteria based on the estimated user's emotions. For example, if the user is feeling stressed, the filtering unit can preferentially filter negative information. Furthermore, if the user is relaxed, the filtering unit can preferentially filter interesting information. Furthermore, if the user is excited, the filtering unit can preferentially filter entertaining information. In this way, by adjusting the filtering criteria according to the user's emotions, more appropriate information can be provided. Emotion estimation is performed using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The filtering unit can improve the accuracy of filtering by taking into account the interrelationships between data during filtering. The filtering unit can, for example, analyze the interrelationships between data and preferentially filter highly related information. The filtering unit can also improve the accuracy of filtering based on the interrelationships between data. The filtering unit can also set optimal filtering criteria by taking into account the interrelationships between data. This improves the accuracy of filtering by taking into account the interrelationships between data. The interrelationships between data are evaluated based on, for example, correlation analysis, co-occurrence networks, etc., but are not limited to such examples. Some or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0089] The filtering unit can perform filtering while taking into consideration attribute information of the data submitter. The filtering unit can perform filtering based on, for example, the reliability of the data submitter. The filtering unit can also analyze the attribute information of the data submitter and set filtering criteria. The filtering unit can also perform filtering based on the data submitter's past submission history. In this way, more reliable information is provided by taking into consideration the attribute information of the submitter. The attribute information of the submitter includes, for example, age, gender, occupation, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0090] The filtering unit can weight the filtering based on the frequency of data submission during filtering. For example, the filtering unit can prioritize filtering of data with a high submission frequency. The filtering unit can also adjust the filtering weight according to the submission frequency. The filtering unit can also set optimal filtering criteria based on the submission frequency. As a result, by weighting the filtering based on the submission frequency, more important information is provided preferentially. The submission frequency includes, for example, the number of submissions, the submission interval, etc., but is not limited to such examples. Some or all of the above-described processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0091] The filtering unit can estimate the user's emotions and change the order in which the filtering results are displayed based on the estimated user's emotions. For example, if the user is feeling stressed, the filtering unit can prioritize displaying relaxing information. Furthermore, if the user is relaxed, the filtering unit can prioritize displaying interesting information. Furthermore, if the user is excited, the filtering unit can prioritize displaying highly entertaining information. In this way, by adjusting the display order according to the user's emotions, more appropriate information is provided. The emotion estimation is performed using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0092] The filtering unit can perform filtering while taking into account the geographical distribution of data. For example, the filtering unit can prioritize filtering information related to the area where the user is currently located. The filtering unit can also set filtering criteria based on the geographical distribution of data. The filtering unit can also set optimal filtering criteria taking into account the geographical distribution of data. This allows more relevant information to be provided by taking the geographical distribution into account. Examples of geographical distribution include, but are not limited to, GPS data and IP addresses. Some or all of the above-described processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0093] The filtering unit can improve the accuracy of filtering by referring to literature related to the data during filtering. The filtering unit can improve the accuracy of filtering, for example, based on literature related to the data. The filtering unit can also set filtering criteria by referring to literature related to the data. The filtering unit can also set optimal filtering criteria taking into account literature related to the data. As a result, the accuracy of filtering is improved by referring to literature related to the data. Examples of literature related to the data include, but are not limited to, academic papers and technical reports. Some or all of the above-described processing in the filtering unit may be performed using AI, for example, or without using AI.

[0094] The filtering unit can perform filtering taking into account the market value of the data. For example, the filtering unit can prioritize filtering of data with high market value. The filtering unit can also set filtering criteria based on the market value of the data. The filtering unit can also set optimal filtering criteria taking into account the market value of the data. This allows more important information to be provided by taking market value into account. Market value includes, for example, sales data, market research results, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the filtering unit may be performed using, for example, AI, or may be performed without using AI.

[0095] The providing unit can estimate the user's emotions and change the news providing method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can prioritize providing relaxing news. Furthermore, if the user is relaxed, the providing unit can prioritize providing interesting news. Furthermore, if the user is excited, the providing unit can prioritize providing entertaining news. In this way, by adjusting the providing method according to the user's emotions, more appropriate news is provided. The emotion estimation is performed using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0096] The providing unit can change the level of detail of the news provided based on the user's level of interest when providing news. For example, the providing unit can provide detailed information for a news category in which the user has a high level of interest. The providing unit can also provide simplified information for a news category in which the user has a low level of interest. The providing unit can also dynamically adjust the level of detail of the news provided according to the user's level of interest. This enables more effective news provision by adjusting the level of detail of the news provided based on the level of interest. The level of detail of the news provided is adjusted based on, for example, the depth of the information, the range of data to be provided, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0097] When providing news, the providing unit can apply an effective providing algorithm depending on the news category. The providing unit can, for example, select an optimal providing algorithm for each news category. The providing unit can also apply different providing methods depending on the news category. The providing unit can also dynamically switch the providing algorithm for each news category. This improves the accuracy of news provision by applying the optimal providing algorithm depending on the category. Examples of providing algorithms include, but are not limited to, recommendation algorithms and personalization algorithms. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0098] When providing news, the providing unit can improve the accuracy of the news provision by referring to the user's past provision results. The providing unit can, for example, adjust the provision algorithm based on the user's past provision results. The providing unit can also improve the accuracy of the news provision by referring to the user's past provision results. The providing unit can also analyze the user's past provision results and select the optimal provision method. This improves the accuracy of the news provision by referring to the past provision results. Past provision results include, for example, past click data, user feedback, etc., but are not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0099] The providing unit can estimate the user's emotions and change the news display method based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the providing unit can provide a display method including detailed information. Furthermore, if the user is excited, the providing unit can provide a visually stimulating display method. In this way, by adjusting the display method according to the user's emotions, more appropriate news is provided. Emotion estimation is performed using techniques such as facial expression recognition and text analysis. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI.

[0100] When providing news, the providing unit can set a priority of provision based on the time when the news was collected. For example, the providing unit can provide the latest news preferentially. Furthermore, the providing unit can determine the priority of provision based on past news. Furthermore, the providing unit can dynamically adjust the priority of provision according to the time when the news was collected. This enables more effective news provision by determining the priority based on the time when the news was collected. The time when the news was collected includes, for example, the date and time of collection, the frequency of collection, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0101] The providing unit can change the order of news provision based on the relevance of the news when providing the news. The providing unit can, for example, provide highly relevant news preferentially. The providing unit can also dynamically adjust the order of provision according to the relevance of the news. The providing unit can also determine an optimal order of provision based on the relevance of the news. This enables more effective news provision by adjusting the order of provision based on the relevance. The relevance of news is evaluated based on, for example, correlation analysis, co-occurrence networks, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI.

[0102] When providing news, the providing unit can change the use of technical terms in the provided news depending on the user's level of expertise. The providing unit can, for example, adjust the use of technical terms depending on the user's level of expertise. The providing unit can also simplify technical terms to make them easier for the user to understand. The providing unit can also select an optimal provision method based on the user's level of expertise. As a result, by adjusting the use of technical terms depending on the level of expertise, news that is easy for the user to understand is provided. The use of technical terms is adjusted based on, for example, the definition of the technical terms, frequency of use, etc., but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed, for example, using AI, or may be performed without using AI. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, filtering unit, and provision unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and filters news based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the filtered news to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, filtering unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and filters news based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides the filtered news to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, filtering unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and filters news based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the headset type terminal 314 and provides the filtered news to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, filtering unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The filtering unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and filters news based on the analyzed data. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the filtered news to the user.

[0103] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0104] The news delivery system may further include a health data collection unit that collects and analyzes the user's health data. The health data collection unit may collect, for example, the user's heart rate, sleep patterns, and amount of exercise. The analysis unit may evaluate the user's health condition based on the collected health data and generate data for providing appropriate news. For example, if the user is feeling stressed, relaxing news may be provided preferentially. Also, if the user is relaxed after exercising, interesting news may be provided. This makes it possible to provide personalized news according to the user's health condition.

[0105] The news delivery system may further include a voice data collection unit that collects and analyzes the user's voice data. The voice data collection unit may collect, for example, the content of the user's conversation and the tone of voice. The analysis unit may estimate the user's emotions and interests based on the collected voice data and generate data for providing appropriate news. For example, if the user is excited, it may be possible to provide entertaining news. On the other hand, if the user is calm, it may be possible to provide detailed news. This makes it possible to provide personalized news based on the user's voice data.

[0106] The news delivery system may further include a purchase data collection unit that collects and analyzes user purchase data. The purchase data collection unit may collect, for example, the user's online shopping history and in-store purchase history. The analysis unit may estimate the user's interests and preferences based on the collected purchase data and generate data for providing appropriate news. For example, if the user frequently purchases a particular product, news related to that product may be provided. Also, if the user purchases a new product, news related to that product may be provided. This makes it possible to provide personalized news based on the user's purchase data.

[0107] The news delivery system may further include a location information collection unit that collects and analyzes user location information. The location information collection unit may collect, for example, the user's current location and movement history. The analysis unit may estimate the user's interests and behavioral patterns based on the collected location information and generate data for providing appropriate news. For example, if the user frequently visits a specific area, news related to that area may be provided. Also, if the user is traveling, news related to the user's travel destination may be provided. This makes it possible to provide personalized news based on the user's location information.

[0108] The news delivery system may further include a calendar data collection unit that collects and analyzes the user's calendar data. The calendar data collection unit may, for example, collect the user's schedule and event information. The analysis unit may generate data for providing news based on the collected calendar data in accordance with the user's interests and schedule. For example, if the user plans to attend a specific event, news related to that event may be provided. Furthermore, if the user has a busy schedule, news that can be read in a short amount of time may be provided. This enables personalized news delivery based on the user's calendar data.

[0109] The news providing system may further include a reading data collection unit that collects and analyzes the user's reading data. The reading data collection unit may, for example, collect a history of books and articles read by the user. The analysis unit may estimate the user's interests and preferences based on the collected reading data and generate data for providing appropriate news. For example, if the user frequently reads books of a particular genre, news related to that genre may be provided. Also, if the user starts reading a new book, news related to that book may be provided. This makes it possible to provide personalized news based on the user's reading data.

[0110] The news delivery system can further estimate the user's emotions and change the news display format based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display format can be provided. If the user is relaxed, a display format including detailed information can be provided. If the user is excited, a visually stimulating display format can be provided. This makes it possible to optimally display news according to the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and voice analysis.

[0111] The news providing system can further estimate the user's emotions and adjust the news content based on the estimated user emotions. For example, if the user is feeling stressed, it can provide relaxing news. If the user is relaxed, it can provide interesting news. If the user is excited, it can provide entertaining news. In this way, optimal news content is provided according to the user's emotions. Emotions are estimated using technologies such as facial expression recognition and voice analysis.

[0112] The news delivery system can further estimate the user's emotions and adjust the timing of news delivery based on the estimated user emotions. For example, if the user is feeling stressed, news can be delivered at a time when the user is able to relax. If the user is relaxed, interesting news can be delivered immediately. If the user is excited, entertaining news can be delivered in real time. This makes it possible to deliver optimal news according to the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and voice analysis.

[0113] The news provision system can further estimate the user's emotions and adjust the news filtering criteria based on the estimated user emotions. For example, if the user is feeling stressed, negative news can be filtered out. If the user is relaxed, interesting news can be displayed preferentially. If the user is excited, entertaining news can be displayed preferentially. This enables optimal news filtering according to the user's emotions. Emotions can be estimated using technologies such as facial expression recognition and voice analysis.

[0114] The processing flow of the second embodiment will be briefly explained below.

[0115] Step 1: The collection unit collects user behavior data, including website browsing history, click data, purchase history, social media behavior, and news browsing time. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis is performed based on the algorithm used and the purpose of the analysis, and generates data to select news based on the user's interests and provide it in the optimal order. Step 3: The filtering unit filters the news based on the data analyzed by the analysis unit. Filtering includes identifying and filtering out advertisements and negative information, and is done automatically using AI. Step 4: The providing unit provides the news filtered by the filtering unit. The providing includes displaying the news in an optimal order based on the user's interests and adjusting the news display time so that the user can view the news within the time period set by the user.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0137] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0150] 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.

[0151] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0152] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0153] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] 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.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0174] 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."

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] [Explanation of symbols]

[0188] 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 user behavior data; an analysis unit that analyzes the data collected by the collection unit; a filtering unit that filters news based on the data analyzed by the analysis unit; a providing unit that provides the news filtered by the filtering unit. A system characterized by:

2. The filtering unit Identify and filter out advertising or specific inappropriate information 2. The system of claim 1.

3. The collecting unit Track users' social media posts or news viewing time 2. The system of claim 1.

4. The providing unit Providing news based on user interests and in an order based on user interests 2. The system of claim 1.

5. The providing unit Change the news display time based on user settings so that users can read news within the time they set 2. The system of claim 1.

6. The collecting unit The system estimates user emotions and adjusts the timing of behavioral data collection based on the estimated user emotions.

2. The system of claim 1.

7. The collecting unit Analyze users' past behavioral data and select effective collection methods 2. The system of claim 1.

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

2. The system of claim 1.

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