system

Abstract

An object of a system according to an embodiment is to appropriately determine a field of interest or a field of dislike of a user and to provide information based on the determination.SOLUTION: A system includes a data collecting part, an interest field determination part, a dislike field determination part, a fake news generation part, and a reproduction part. The data collection unit collects data such as an SNS action history, a moving image browsing history, and a music listening history of the user in the daytime. The interested field determination unit analyzes the data collected by the data collection unit and determines a field or a topic in which the user is highly interested. Based on the fields and topics determined by the interested field determination unit, the disliked field determination unit determines, on a daily basis, a field in which the user is not highly interested at the present time, while avoiding a field in which the user is disliked in the initial setting. The fake news generation unit generates a fake news or a false paper related to the field determined by the disliked field determination unit. The reproduction unit reproduces the fake news and the false paper generated by the fake news generation unit on the application.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of not being able to adequately determine a user's areas of interest and dislike and provide information based on that.

[0005] The system according to the embodiment aims to appropriately determine the areas of interest and dislike of a user and provide information based on the determined areas. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an area of ​​interest determination unit, an area of ​​dislike determination unit, a fake news generation unit, and a playback unit. The data collection unit collects data such as the user's daytime social media activity history, video viewing history, and music listening history. The area of ​​interest determination unit analyzes the data collected by the data collection unit and determines areas and topics in which the user is highly interested. The area of ​​dislike determination unit avoids areas that the user dislikes by default based on the areas and topics determined by the area of ​​interest determination unit and determines areas in which the user is not currently highly interested on a daily basis. The fake news generation unit generates fake news and fake papers related to the areas determined by the area of ​​dislike determination unit. The playback unit plays the fake news and fake papers generated by the fake news generation unit on the app. [Effects of the Invention]

[0007] The system according to the embodiment can appropriately determine the areas of interest and dislike of the user and provide information based on the determined areas. [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) The information provision system according to an embodiment of the present invention determines the fields and topics that a user is highly interested in based on data such as the user's daytime social media activity history, video viewing history, and music listening history, and further determines on a daily basis the fields that the user is not currently interested in, while avoiding fields that the user dislikes by default. As a result, the information provision system can generate fake news and fake papers related to the determined fields and play them on the app.

[0029] An information provision system according to an embodiment includes a data collection unit, an area of ​​interest determination unit, an area of ​​dislike determination unit, a fake news generation unit, and a playback unit. The data collection unit collects data such as a user's daytime social media activity history, video viewing history, and music listening history. For example, the data collection unit collects a history of "likes" and comments on social media. The data collection unit can also record the genres and playback times of videos viewed by the user. The data collection unit also collects a music listening history and identifies the types of music the user repeatedly listens to. The area of ​​interest determination unit analyzes the data collected by the data collection unit to determine areas and topics in which the user is highly interested. For example, the area of ​​interest determination unit analyzes the frequency of "likes" and comments on social media to identify the user's area of ​​interest. The area of ​​interest determination unit can also determine the genres the user frequently watches based on the video viewing history. The area of ​​interest determination unit also analyzes a music listening history to identify the user's music preferences. The disliked field determination unit avoids fields that the user dislikes by default based on the fields and topics determined by the interest field determination unit, and determines fields that the user is not currently interested in on a daily basis. For example, if the user has set "politics" or "sports" as their disliked fields by default, the disliked field determination unit avoids these fields. The disliked field determination unit can also identify fields that the user has not recently shown interest in based on the user's behavioral history. The disliked field determination unit also updates the fields in which the user is not interested on a daily basis. The fake news generation unit generates fake news and fake papers related to the fields determined by the disliked field determination unit. For example, the fake news generation unit generates fake papers related to the "science" field, in which the user has no interest. The fake news generation unit can also create fake news that appears credible based on information about specific fields. The fake news generation unit also uses generation AI to generate fake news and fake papers based on information about the determined fields. The playback unit plays the fake news and fake papers generated by the fake news generation unit on the app. For example, when a user opens the app, the playback unit plays the generated fake news in video format, and also displays the fake paper in text format so that the user can read it.The playback unit can also provide the generated content to the user, allowing the user to obtain information on areas of little interest. This allows the information provision system according to the embodiment to provide information on areas of little interest to the user, thereby attracting the user's interest. For example, fake news or fake papers on "science" or "history," which the user does not usually show interest in, can be generated and played on the app, thereby stimulating new interest. Furthermore, by avoiding areas that the user dislikes, the user's discomfort can be reduced.

[0030] The field of interest determination unit analyzes real-time location information and purchase history in addition to the SNS behavior history, thereby enabling more accurate identification of the user's field of interest. For example, the field of interest determination unit analyzes real-time location information in addition to the user's SNS behavior history to identify the user's field of interest based on the places and events the user visits. For example, it analyzes the genres of cafes and bookstores the user frequently visits. The field of interest determination unit also analyzes the user's purchase history to identify the user's field of interest based on the categories and brands of purchased products. For example, it analyzes the genres of books and movies purchased by the user. The field of interest determination unit also analyzes the SNS behavior history, location information, and purchase history in an integrated manner to identify the user's field of interest with more accuracy. For example, it combines data on posts the user "liked" on the SNS, places visited, and products purchased. This enables more accurate identification of the user's field of interest.

[0031] The interest field determination unit can predict future interests by taking into account changes in the user's past interests. The interest field determination unit, for example, analyzes the user's past SNS activity history and tracks changes in interests. For example, it analyzes the history of "likes" and comments over the past year to identify changes in interests. The interest field determination unit also develops an algorithm to predict future interests based on data on past changes in interests. For example, it predicts areas that the user is likely to become interested in next based on past data. The interest field determination unit also visualizes changes in the user's interests and provides feedback on predicted future interests to the user. For example, it displays changes in past areas of interest in a graph to predict future areas of interest. This makes it possible to predict the user's future interests.

[0032] The disliked area determination unit can analyze the user's past history of negative comments and low ratings to more accurately identify disliked areas. The disliked area determination unit, for example, analyzes the user's past history of negative comments and low ratings to identify disliked areas. For example, it analyzes negative comments on specific topics. The disliked area determination unit also builds a system that dynamically updates the user's disliked areas based on the history of negative comments and low ratings. For example, it updates the disliked areas every time a new negative comment is added. The disliked area determination unit also develops an algorithm that identifies disliked areas based on the user's negative emotion data. For example, it identifies areas with high negative emotion scores as disliked areas. This makes it possible to more accurately identify the user's disliked areas.

[0033] The disliked field determination unit, when determining fields in which the user is not very interested, takes into account not only daily but also weekly and monthly trends, and can track long-term changes in interests. For example, when determining fields in which the user is not very interested, the disliked field determination unit analyzes not only daily but also weekly and monthly trend data. For example, it identifies changes in interests based on behavioral history over the past month. The disliked field determination unit also develops an algorithm to track long-term changes in interests and dynamically updates the fields in which the user is not very interested. For example, it re-evaluates the fields of interest based on weekly and monthly data. Furthermore, when determining fields in which the user is not very interested, the disliked field determination unit visualizes long-term trend data and provides feedback to the user. For example, it displays past changes in fields of interest in a graph. This makes it possible to track long-term changes in the user's interests.

[0034] The fake news generation unit can generate more credible content based on a user's past browsing history and comment history. For example, the fake news generation unit analyzes a user's past browsing history and generates fake news and fake papers based on that data. For example, it imitates the style of news sites and blogs that the user frequently visits. The fake news generation unit also analyzes a user's comment history and generates fake news and fake papers based on that data. For example, it reflects the content and topics of users' past comments. The fake news generation unit also analyzes browsing history and comment history in an integrated manner to generate more credible fake news and fake papers. For example, it combines information related to topics that the user is interested in. This makes it possible to generate more credible fake news and fake papers.

[0035] The fake news generation unit can combine information from different perspectives and positions to generate multifaceted content that will interest users. For example, the fake news generation unit combines information from different perspectives and positions to generate fake news or fake papers. For example, it combines opinions for and against the same topic. The fake news generation unit also collects and integrates data from different sources to generate multifaceted content. For example, it combines posts from news sites, blogs, and social media. The fake news generation unit also builds a system that dynamically updates information from different perspectives and positions to attract users' interest. For example, it changes the combination of information depending on the user's interests. This makes it possible to generate multifaceted fake news or fake papers that will interest users.

[0036] The playback unit can play content at the optimal timing based on the user's browsing history and playback history. The playback unit, for example, analyzes the user's browsing history and playback history and builds a system that plays content at the optimal timing. For example, content is played during the time period when the user is most active. The playback unit also develops an algorithm that plays content at a timing that will interest the user based on the browsing history and playback history. For example, related content is played immediately after the user shows interest in a particular topic. The playback unit also builds a system that plays content at the optimal timing based on user behavior data. For example, if a user frequently uses an app during a specific time period, content is played during that time period. This allows content to be played at the optimal time for the user.

[0037] The playback unit can select the optimal playback method by taking into account the usage status of the user's device. For example, the playback unit analyzes the remaining battery level of the user's device and plays content in low power consumption mode when the battery is low. For example, the playback unit lowers the video resolution. The playback unit also analyzes the network connection status and selects offline playback mode when the connection is unstable. For example, the playback unit plays content that has been downloaded in advance. The playback unit also monitors the device usage status in real time and builds a system that dynamically selects the optimal playback method. For example, the playback method changes depending on the remaining battery level and network connection status. This makes it possible to select the optimal playback method according to the usage status of the user's device.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The information provision system can also collect the user's health data and identify areas of interest based on the user's health condition. For example, the data collection unit collects heart rate and sleep data from the user's smartwatch or fitness tracker. The data collection unit can also collect the user's food records and exercise history to analyze the user's health condition. The area of ​​interest determination unit identifies areas in which the user is likely to be interested in health based on this health data. For example, if the user's heart rate is high, information on stress management can be provided. The information provision system can also analyze the user's sleep data and provide information on improving sleep. This allows the system to identify areas of interest based on the user's health condition and provide more personalized information.

[0040] The information providing system can also collect data on the user's hobbies and special skills to identify areas of interest. For example, the data collecting unit collects the history of online communities and forums in which the user participates. The data collecting unit can also analyze the content of photos and videos posted by the user to identify hobbies and special skills. The area of ​​interest determining unit determines areas that the user is likely to be interested in based on this data. For example, if the user frequently posts photos of cooking, information about cooking can be provided. Also, if the user posts videos of musical performances, information about music can be provided. This makes it possible to identify areas of interest based on the user's hobbies and special skills and provide more personalized information.

[0041] The information providing system can further analyze the user's social connections and identify areas of interest. For example, the data collection unit collects data on the user's friendships and followers on social networking sites. The data collection unit can also collect the history of groups and events in which the user participates. The area of ​​interest determination unit uses this data to identify areas of interest based on the user's social connections. For example, the area of ​​interest can be determined based on topics frequently shared by the user's friends. The area of ​​interest can also be determined by analyzing the genre of events in which the user participates. This makes it possible to identify areas of interest based on the user's social connections and provide more personalized information.

[0042] The information provision system can further analyze the user's purchasing history to identify areas of interest. For example, the data collection unit collects the user's online shopping history. The data collection unit can also analyze the categories and brands of products purchased by the user. The area of ​​interest determination unit identifies areas in which the user is likely to be interested based on this data. For example, the area of ​​interest can be determined based on the product categories that the user frequently purchases. Also, if the user purchases products from a specific brand, information related to that brand can be provided. This makes it possible to identify areas of interest based on the user's purchasing history and provide more personalized information.

[0043] The information providing system can further analyze the user's location information to identify areas of interest. For example, the data collection unit collects the user's real-time location information. The data collection unit can also collect a history of places and events visited by the user. The area of ​​interest determination unit identifies areas that the user is likely to be interested in based on this data. For example, the area of ​​interest can be identified based on the genre of cafes or bookstores that the user frequently visits. The area of ​​interest can also be identified by analyzing the genre of events that the user participates in. This makes it possible to identify areas of interest based on the user's location information and provide more personalized information.

[0044] The information provision system can further take into account changes in a user's past interests to predict future interests. For example, the interest area determination unit analyzes the user's past SNS activity history and tracks changes in interests. For example, it analyzes the history of "likes" and comments over the past year to identify changes in interests. The interest area determination unit also develops an algorithm to predict future interests based on data on past changes in interests. For example, it predicts areas that the user is likely to become interested in next based on past data. The interest area determination unit also visualizes changes in the user's interests and provides feedback on predicted future interests to the user. For example, it displays changes in past interest areas in a graph to predict future interest areas. This makes it possible to predict the user's future interests.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: The data collection unit collects data such as the user's daytime social media activity history, video viewing history, music listening history, etc. For example, it collects the user's "like" and comment history on social media, the genre and playback time of videos viewed, and music listening history, and identifies the types of music the user repeatedly listens to. Step 2: The interest area determination unit analyzes the data collected by the data collection unit and determines the areas and topics that the user is most interested in. For example, it analyzes the frequency of "likes" and comments on social media, video viewing history, and music listening history to identify the user's areas of interest and preferences. Step 3: The disliked field determination unit, based on the fields and topics determined by the interest field determination unit, avoids fields that the user dislikes by default and determines fields that the user is not currently interested in on a daily basis. For example, if the user has set "politics" or "sports" as fields that they dislike by default, these fields will be avoided. In addition, the unit identifies fields that the user has not shown interest in recently from the user's behavioral history and updates the list daily. Step 4: The fake news generator generates fake news and fake papers related to the fields determined by the disliked field determination unit. For example, it generates fake papers related to the "science" field, which the user has no interest in, and uses a generation AI to create fake news that appears credible. Step 5: The playback unit plays the fake news and fake papers generated by the fake news generation unit on the app. For example, when a user opens the app, the generated fake news is played in video format and the fake papers are displayed in text format.

[0047] (Example 2) The information provision system according to an embodiment of the present invention determines the fields and topics that a user is highly interested in based on data such as the user's daytime social media activity history, video viewing history, and music listening history, and further determines on a daily basis the fields that the user is not currently interested in, while avoiding fields that the user dislikes by default. As a result, the information provision system can generate fake news and fake papers related to the determined fields and play them on the app.

[0048] An information provision system according to an embodiment includes a data collection unit, an area of ​​interest determination unit, an area of ​​dislike determination unit, a fake news generation unit, and a playback unit. The data collection unit collects data such as a user's daytime social media activity history, video viewing history, and music listening history. For example, the data collection unit collects a history of "likes" and comments on social media. The data collection unit can also record the genres and playback times of videos viewed by the user. The data collection unit also collects a music listening history and identifies the types of music the user repeatedly listens to. The area of ​​interest determination unit analyzes the data collected by the data collection unit to determine areas and topics in which the user is highly interested. For example, the area of ​​interest determination unit analyzes the frequency of "likes" and comments on social media to identify the user's area of ​​interest. The area of ​​interest determination unit can also determine the genres the user frequently watches based on the video viewing history. The area of ​​interest determination unit also analyzes a music listening history to identify the user's music preferences. The disliked field determination unit avoids fields that the user dislikes by default based on the fields and topics determined by the interest field determination unit, and determines fields that the user is not currently interested in on a daily basis. For example, if the user has set "politics" or "sports" as their disliked fields by default, the disliked field determination unit avoids these fields. The disliked field determination unit can also identify fields that the user has not recently shown interest in based on the user's behavioral history. The disliked field determination unit also updates the fields in which the user is not interested on a daily basis. The fake news generation unit generates fake news and fake papers related to the fields determined by the disliked field determination unit. For example, the fake news generation unit generates fake papers related to the "science" field, in which the user has no interest. The fake news generation unit can also create fake news that appears credible based on information about specific fields. The fake news generation unit also uses generation AI to generate fake news and fake papers based on information about the determined fields. The playback unit plays the fake news and fake papers generated by the fake news generation unit on the app. For example, when a user opens the app, the playback unit plays the generated fake news in video format, and also displays the fake paper in text format so that the user can read it.The playback unit can also provide the generated content to the user, allowing the user to obtain information on areas of little interest. This allows the information provision system according to the embodiment to provide information on areas of little interest to the user, thereby attracting the user's interest. For example, fake news or fake papers on "science" or "history," which the user does not usually show interest in, can be generated and played on the app, thereby stimulating new interest. Furthermore, by avoiding areas that the user dislikes, the user's discomfort can be reduced.

[0049] The field of interest determination unit analyzes real-time location information and purchase history in addition to the SNS behavior history, thereby enabling more accurate identification of the user's field of interest. For example, the field of interest determination unit analyzes real-time location information in addition to the user's SNS behavior history to identify the user's field of interest based on the places and events the user visits. For example, it analyzes the genres of cafes and bookstores the user frequently visits. The field of interest determination unit also analyzes the user's purchase history to identify the user's field of interest based on the categories and brands of purchased products. For example, it analyzes the genres of books and movies purchased by the user. The field of interest determination unit also analyzes the SNS behavior history, location information, and purchase history in an integrated manner to identify the user's field of interest with more accuracy. For example, it combines data on posts the user "liked" on the SNS, places visited, and products purchased. This enables more accurate identification of the user's field of interest.

[0050] The interest field determination unit can predict future interests by taking into account changes in the user's past interests. The interest field determination unit, for example, analyzes the user's past SNS activity history and tracks changes in interests. For example, it analyzes the history of "likes" and comments over the past year to identify changes in interests. The interest field determination unit also develops an algorithm to predict future interests based on data on past changes in interests. For example, it predicts areas that the user is likely to become interested in next based on past data. The interest field determination unit also visualizes changes in the user's interests and provides feedback on predicted future interests to the user. For example, it displays changes in past areas of interest in a graph to predict future areas of interest. This makes it possible to predict the user's future interests.

[0051] The field of interest determination unit uses the emotion estimation function to analyze the emotions expressed when a user "likes" or comments on an SNS, and can identify a field of interest based on the emotions. The field of interest determination unit, for example, analyzes the emotions expressed when a user "likes" or comments on an SNS, and identifies a field of interest based on the emotions. For example, it prioritizes analysis of posts with strong positive emotions. The field of interest determination unit also uses the emotion estimation function to analyze the user's emotional state in real time, and identifies a field of interest based on the emotions. For example, it analyzes the user's facial expressions and voice when entering comments. The field of interest determination unit also builds a system that dynamically updates the user's field of interest based on the emotion estimation data. For example, it reevaluates the user's field of interest every time the user's emotions change. This makes it possible to identify a field of interest based on the user's emotions.

[0052] The disliked area determination unit can analyze the user's past history of negative comments and low ratings to more accurately identify disliked areas. The disliked area determination unit, for example, analyzes the user's past history of negative comments and low ratings to identify disliked areas. For example, it analyzes negative comments on specific topics. The disliked area determination unit also builds a system that dynamically updates the user's disliked areas based on the history of negative comments and low ratings. For example, it updates the disliked areas every time a new negative comment is added. The disliked area determination unit also develops an algorithm that identifies disliked areas based on the user's negative emotion data. For example, it identifies areas with high negative emotion scores as disliked areas. This makes it possible to more accurately identify the user's disliked areas.

[0053] The disliked field determination unit, when determining fields in which the user is not very interested, takes into account not only daily but also weekly and monthly trends, and can track long-term changes in interests. For example, when determining fields in which the user is not very interested, the disliked field determination unit analyzes not only daily but also weekly and monthly trend data. For example, it identifies changes in interests based on behavioral history over the past month. The disliked field determination unit also develops an algorithm to track long-term changes in interests and dynamically updates the fields in which the user is not very interested. For example, it re-evaluates the fields of interest based on weekly and monthly data. Furthermore, when determining fields in which the user is not very interested, the disliked field determination unit visualizes long-term trend data and provides feedback to the user. For example, it displays past changes in fields of interest in a graph. This makes it possible to track long-term changes in the user's interests.

[0054] The dislike field determination unit can use the emotion estimation function to analyze the negative emotions a user has toward a specific field and dynamically update the dislike field. The dislike field determination unit, for example, uses the emotion estimation function to build a system that analyzes the negative emotions a user has toward a specific field. For example, it analyzes the user's facial expressions and voice. The dislike field determination unit also develops an algorithm that dynamically updates the dislike field based on the user's negative emotion data. For example, it identifies fields with high negative emotion scores as dislike fields. The dislike field determination unit also builds a system that collects emotion estimation data in real time and uses it to update the dislike field. For example, it reevaluates the dislike field every time the user's emotion changes. This allows the user's dislike field to be dynamically updated.

[0055] The fake news generation unit can generate more credible content based on a user's past browsing history and comment history. For example, the fake news generation unit analyzes a user's past browsing history and generates fake news and fake papers based on that data. For example, it imitates the style of news sites and blogs that the user frequently visits. The fake news generation unit also analyzes a user's comment history and generates fake news and fake papers based on that data. For example, it reflects the content and topics of users' past comments. The fake news generation unit also analyzes browsing history and comment history in an integrated manner to generate more credible fake news and fake papers. For example, it combines information related to topics that the user is interested in. This makes it possible to generate more credible fake news and fake papers.

[0056] The fake news generation unit can combine information from different perspectives and positions to generate multifaceted content that will interest users. For example, the fake news generation unit combines information from different perspectives and positions to generate fake news or fake papers. For example, it combines opinions for and against the same topic. The fake news generation unit also collects and integrates data from different sources to generate multifaceted content. For example, it combines posts from news sites, blogs, and social media. The fake news generation unit also builds a system that dynamically updates information from different perspectives and positions to attract users' interest. For example, it changes the combination of information depending on the user's interests. This makes it possible to generate multifaceted fake news or fake papers that will interest users.

[0057] The fake news generation unit uses the emotion estimation function to analyze the emotions of users when viewing fake news or fake papers, and can generate content based on those emotions. For example, the fake news generation unit uses the emotion estimation function to build a system that analyzes the emotions of users when viewing fake news or fake papers. For example, it analyzes the user's facial expressions and voice. The fake news generation unit also develops an algorithm that generates emotion-based fake news and fake papers based on the user's emotion data. For example, it generates content that elicits positive emotions. The fake news generation unit also collects emotion estimation data in real time and builds a system that dynamically updates the content of fake news and fake papers. For example, it regenerates content every time the user's emotion changes. This makes it possible to generate fake news and fake papers based on the user's emotion.

[0058] The playback unit can play content at the optimal timing based on the user's browsing history and playback history. The playback unit, for example, analyzes the user's browsing history and playback history and builds a system that plays content at the optimal timing. For example, content is played during the time period when the user is most active. The playback unit also develops an algorithm that plays content at a timing that will interest the user based on the browsing history and playback history. For example, related content is played immediately after the user shows interest in a particular topic. The playback unit also builds a system that plays content at the optimal timing based on user behavior data. For example, if a user frequently uses an app during a specific time period, content is played during that time period. This allows content to be played at the optimal time for the user.

[0059] The playback unit can select the optimal playback method by taking into account the usage status of the user's device. For example, the playback unit analyzes the remaining battery level of the user's device and plays content in low power consumption mode when the battery is low. For example, the playback unit lowers the video resolution. The playback unit also analyzes the network connection status and selects offline playback mode when the connection is unstable. For example, the playback unit plays content that has been downloaded in advance. The playback unit also monitors the device usage status in real time and builds a system that dynamically selects the optimal playback method. For example, the playback method changes depending on the remaining battery level and network connection status. This makes it possible to select the optimal playback method according to the usage status of the user's device.

[0060] The playback unit uses the emotion estimation function to analyze the emotion of the user when playing content, and can propose a playback order and a playback method based on the emotion. The playback unit, for example, uses the emotion estimation function to build a system that analyzes the emotion of the user when playing content. For example, it analyzes the user's facial expressions and voice. The playback unit also develops an algorithm that proposes a playback order and a playback method based on the emotion, based on the user's emotion data. For example, it plays content in an order that elicits positive emotions. The playback unit also collects emotion estimation data in real time and builds a system that dynamically proposes a playback order and a playback method. For example, it reevaluates the playback order every time the user's emotion changes. This makes it possible to propose a playback order and a playback method based on the user's emotion.

[0061] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0062] The information provision system can also collect the user's health data and identify areas of interest based on the user's health condition. For example, the data collection unit collects heart rate and sleep data from the user's smartwatch or fitness tracker. The data collection unit can also collect the user's food records and exercise history to analyze the user's health condition. The area of ​​interest determination unit identifies areas in which the user is likely to be interested in health based on this health data. For example, if the user's heart rate is high, information on stress management can be provided. The information provision system can also analyze the user's sleep data and provide information on improving sleep. This allows the system to identify areas of interest based on the user's health condition and provide more personalized information.

[0063] The information providing system can also collect data on the user's hobbies and special skills to identify areas of interest. For example, the data collecting unit collects the history of online communities and forums in which the user participates. The data collecting unit can also analyze the content of photos and videos posted by the user to identify hobbies and special skills. The area of ​​interest determining unit determines areas that the user is likely to be interested in based on this data. For example, if the user frequently posts photos of cooking, information about cooking can be provided. Also, if the user posts videos of musical performances, information about music can be provided. This makes it possible to identify areas of interest based on the user's hobbies and special skills and provide more personalized information.

[0064] The information providing system can further analyze the user's social connections and identify areas of interest. For example, the data collection unit collects data on the user's friendships and followers on social networking sites. The data collection unit can also collect the history of groups and events in which the user participates. The area of ​​interest determination unit uses this data to identify areas of interest based on the user's social connections. For example, the area of ​​interest can be determined based on topics frequently shared by the user's friends. The area of ​​interest can also be determined by analyzing the genre of events in which the user participates. This makes it possible to identify areas of interest based on the user's social connections and provide more personalized information.

[0065] The information provision system can further estimate a user's emotions and identify areas of interest based on the emotions. For example, the area of ​​interest determination unit analyzes the emotions expressed when a user "likes" or comments on a social networking site, and identifies areas of interest based on the emotion score. For example, it prioritizes analysis of posts with strong positive emotions. The area of ​​interest determination unit also uses an emotion estimation function to analyze the user's emotional state in real time and identify areas of interest based on the emotions. For example, it analyzes the user's facial expressions and voice when entering comments. The area of ​​interest determination unit also builds a system that dynamically updates the user's areas of interest based on the emotion estimation data. For example, it reevaluates the user's areas of interest every time the user's emotions change. This makes it possible to identify areas of interest based on the user's emotions.

[0066] The information provision system can further estimate the user's emotions and identify disliked areas based on the emotions. For example, the disliked area determination unit uses the emotion estimation function to build a system that analyzes the negative emotions a user has toward a specific area. For example, it analyzes the user's facial expressions and voice. The disliked area determination unit also develops an algorithm that dynamically updates the disliked areas based on the user's negative emotion data. For example, it identifies areas with high negative emotion scores as disliked areas. The disliked area determination unit also builds a system that collects emotion estimation data in real time and uses it to update the disliked areas. For example, it reevaluates the disliked areas every time the user's emotions change. This allows the user's disliked areas to be dynamically updated.

[0067] The information provision system can further estimate user emotions and generate fake news and fake papers based on those emotions. For example, the fake news generation unit uses the emotion estimation function to build a system that analyzes the emotions of users when viewing fake news and fake papers. For example, it analyzes the user's facial expressions and voice. The fake news generation unit also develops an algorithm that generates emotion-based fake news and fake papers based on the user's emotion data. For example, it generates content that elicits positive emotions. The fake news generation unit also collects emotion estimation data in real time and builds a system that dynamically updates the content of fake news and fake papers. For example, it regenerates content every time the user's emotions change. This makes it possible to generate fake news and fake papers based on the user's emotions.

[0068] The information provision system can further estimate the user's emotions and suggest playback orders and methods based on the emotions. For example, the playback unit uses the emotion estimation function to build a system that analyzes the emotions of the user when playing content. For example, it analyzes the user's facial expressions and voice. The playback unit also develops an algorithm that suggests emotion-based playback orders and methods based on the user's emotion data. For example, it plays content in an order that elicits positive emotions. The playback unit also collects emotion estimation data in real time and builds a system that dynamically suggests playback orders and methods. For example, it reevaluates the playback order every time the user's emotions change. This makes it possible to suggest playback orders and methods based on the user's emotions.

[0069] The information provision system can further analyze the user's purchasing history to identify areas of interest. For example, the data collection unit collects the user's online shopping history. The data collection unit can also analyze the categories and brands of products purchased by the user. The area of ​​interest determination unit identifies areas in which the user is likely to be interested based on this data. For example, the area of ​​interest can be determined based on the product categories that the user frequently purchases. Also, if the user purchases products from a specific brand, information related to that brand can be provided. This makes it possible to identify areas of interest based on the user's purchasing history and provide more personalized information.

[0070] The information providing system can further analyze the user's location information to identify areas of interest. For example, the data collection unit collects the user's real-time location information. The data collection unit can also collect a history of places and events visited by the user. The area of ​​interest determination unit identifies areas that the user is likely to be interested in based on this data. For example, the area of ​​interest can be identified based on the genre of cafes or bookstores that the user frequently visits. The area of ​​interest can also be identified by analyzing the genre of events that the user participates in. This makes it possible to identify areas of interest based on the user's location information and provide more personalized information.

[0071] The information provision system can further take into account changes in a user's past interests to predict future interests. For example, the interest area determination unit analyzes the user's past SNS activity history and tracks changes in interests. For example, it analyzes the history of "likes" and comments over the past year to identify changes in interests. The interest area determination unit also develops an algorithm to predict future interests based on data on past changes in interests. For example, it predicts areas that the user is likely to become interested in next based on past data. The interest area determination unit also visualizes changes in the user's interests and provides feedback on predicted future interests to the user. For example, it displays changes in past interest areas in a graph to predict future interest areas. This makes it possible to predict the user's future interests.

[0072] The processing flow of the second embodiment will be briefly explained below.

[0073] Step 1: The data collection unit collects data such as the user's daytime social media activity history, video viewing history, music listening history, etc. For example, it collects the user's "like" and comment history on social media, the genre and playback time of videos viewed, and music listening history, and identifies the types of music the user repeatedly listens to. Step 2: The interest area determination unit analyzes the data collected by the data collection unit and determines the areas and topics that the user is most interested in. For example, it analyzes the frequency of "likes" and comments on social media, video viewing history, and music listening history to identify the user's areas of interest and preferences. Step 3: The disliked field determination unit, based on the fields and topics determined by the interest field determination unit, avoids fields that the user dislikes by default and determines fields that the user is not currently interested in on a daily basis. For example, if the user has set "politics" or "sports" as fields that they dislike by default, these fields will be avoided. In addition, the unit identifies fields that the user has not shown interest in recently from the user's behavioral history and updates the list daily. Step 4: The fake news generator generates fake news and fake papers related to the fields determined by the disliked field determination unit. For example, it generates fake papers related to the "science" field, which the user has no interest in, and uses a generation AI to create fake news that appears credible. Step 5: The playback unit plays the fake news and fake papers generated by the fake news generation unit on the app. For example, when a user opens the app, the generated fake news is played in video format and the fake papers are displayed in text format.

[0074] 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.

[0075] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0076] 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.

[0077] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] 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.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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).

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] In the robot 414, 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 robot 414 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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."

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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. [Explanation of symbols]

[0141] 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 data collection unit that collects data such as a user's daytime SNS activity history, video viewing history, and music listening history; an interest field determination unit that analyzes the data collected by the data collection unit and determines fields and topics in which the user is highly interested; A disliked field determination unit that avoids fields that the user dislikes by default based on the fields and topics determined by the interest field determination unit and determines fields that the user is not currently interested in on a daily basis; A fake news generation unit that generates fake news and fake papers related to the field determined by the dislike field determination unit; A playback unit that plays back the fake news and fake papers generated by the fake news generation unit on the app. A system characterized by:

2. The field of interest determination unit In addition to the social media activity history, real-time location information and purchase history are analyzed to identify areas of interest with greater precision.

2. The system of claim 1.

3. The field of interest determination unit Taking into account the user's past interest transitions, predict future interests 2. The system of claim 1.

4. The field of interest determination unit Analyzing the emotions of the user when he / she likes or comments on the SNS, and identifying the area of ​​interest based on the emotions.

2. The system of claim 1.

5. The dislike field determination unit Analyze the user's past negative comments and low ratings to identify more accurate areas of dislike 2. The system of claim 1.

6. The dislike field determination unit When determining the user's areas of low interest, consider not only the daily but also weekly and monthly trends to track long-term changes in interest.

2. The system of claim 1.

7. The dislike field determination unit Analyze the negative feelings the user has about a specific area and dynamically update the areas they dislike.

2. The system of claim 1.

8. The fake news generation unit Generate more reliable content based on the user's past browsing history and comment history 2. The system of claim 1.

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