system

Abstract

The system according to the embodiment aims to provide real-time learning data and efficiently deliver information desired by advertisers to consumers. [Solution] A system according to an embodiment includes a collection unit, an incorporation unit, a provision unit, and a period setting unit. The collection unit collects data. The incorporation unit incorporates information requested by an advertiser into the data collected by the collection unit. The provision unit provides a service using the data incorporated by the incorporation unit. The period setting unit delivers information within a period specified by the advertiser.

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 technologies have had the problem of making it difficult to provide real-time learning data while efficiently delivering the information advertisers want to consumers.

[0005] The system according to the embodiment aims to provide real-time learning data and efficiently deliver information desired by advertisers to consumers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an incorporation unit, a provision unit, and a period setting unit. The collection unit collects data. The incorporation unit incorporates information requested by the advertiser into the data collected by the collection unit. The provision unit provides a service using the data incorporated by the incorporation unit. The period setting unit delivers the information within a period specified by the advertiser. [Effects of the Invention]

[0007] The system according to the embodiment can provide real-time learning data and efficiently deliver information desired by advertisers to consumers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention provides real-time learning data to an AI and incorporates information desired by an advertiser into that data. The system includes a collection unit that collects data, an integration unit that incorporates information desired by the advertiser into the collected data, a provision unit that provides services using the incorporated data, and a time period setting unit that delivers information within a time period specified by the advertiser. This allows the system to efficiently perform a series of processes from data collection to information delivery. For example, the system can collect the latest information from news sites and social media, incorporate information specified by the advertiser, and deliver the information to consumers. This allows the AI ​​to constantly learn the latest information, allowing advertisers to deliver information to consumers in real time. For example, when generating news articles, the AI ​​can generate articles based on the latest information and incorporate information desired by the advertiser into the articles. This allows consumers to always obtain the latest information, allowing advertisers to deliver their advertisements effectively.

[0029] An information provision system according to an embodiment includes a collection unit, an integration unit, a provision unit, and a period setting unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, image data, and audio data. The collection unit collects the latest information from news sites and social media sites. The collection unit can also collect data in real time using a browsing function. For example, the collection unit collects news articles and social media posts in real time using a web browser. The integration unit integrates information desired by an advertiser into the collected data. Examples of the information desired by an advertiser include, but are not limited to, a target audience and the type of advertising content. For example, the integration unit integrates information about products or services specified by the advertiser into the collected news articles. The provision unit provides a service using the integrated data. Examples of services include, but are not limited to, a news distribution service and an advertising distribution service. For example, when the AI ​​generates a news article, the provision unit generates the article incorporating the information desired by the advertiser. The period setting unit sets the information to be delivered within a period specified by the advertiser. The period may include, but is not limited to, for example, a date and time, a length of the period, etc. For example, the period setting unit sets an advertisement to be displayed during a specific campaign period. This allows the information provision system according to the embodiment to efficiently perform a series of processes from data collection to information provision. This allows the system's AI to constantly learn the latest information, enabling advertisers to deliver information to consumers in real time.

[0030] The collection unit can collect data using a browsing function. Examples of the browsing function include, but are not limited to, the type of web browser and the method of data collection. For example, the collection unit uses a web browser to collect data in real time from news sites and social media. For example, the collection unit uses a web browser to collect the latest news articles and social media posts. The collection unit can also use the browsing function to collect data from specific websites. For example, the collection unit collects the latest articles from specific news sites and blogs. This allows the collection unit to collect data in real time, thereby always providing the latest information.

[0031] The embedding unit can embed information desired by the advertiser into the collected data. Examples of information desired by the advertiser include, but are not limited to, the target audience and the type of advertising content. The embedding unit, for example, embeds information about products and services specified by the advertiser into the collected news articles. For example, the embedding unit embeds information about products and services specified by the advertiser into the collected data, thereby enabling effective advertisement delivery. For example, the embedding unit embeds information about products and services specified by the advertiser into the collected news articles. The embedding unit can also be configured to deliver information within a period specified by the advertiser. For example, the embedding unit configures the device to display an advertisement during a specific campaign period. In this way, the embedding unit embeds information according to the advertiser's request into the data, enabling effective advertisement delivery.

[0032] The provision unit allows the AI ​​to generate news articles, and can generate articles that incorporate information desired by advertisers. Examples of AI include, but are not limited to, machine learning algorithms and natural language processing technologies. For example, when the AI ​​generates a news article, the provision unit generates the article that incorporates information desired by advertisers. For example, when the AI ​​generates a news article, the provision unit incorporates information about products or services specified by advertisers. The provision unit can also distribute the news articles generated by the AI. For example, the provision unit provides the news articles generated by the AI ​​as a news distribution service. In this way, the provision unit can increase advertising effectiveness by generating news articles that include advertiser information.

[0033] The period setting unit can set the information to be delivered within a period specified by the advertiser. The period includes, but is not limited to, for example, a date and time, and the length of the period. For example, the period setting unit sets the advertisement to be displayed during a specific campaign period. For example, the period setting unit sets the information to be delivered within a period specified by the advertiser. The period setting unit can also set a schedule for delivering the information within the period specified by the advertiser. For example, the period setting unit sets a schedule for displaying the advertisement during a specific time period. In this way, the period setting unit can maximize the effectiveness of the advertisement by delivering the information within the period specified by the advertiser.

[0034] When collecting data, the collection unit can analyze the user's past browsing history and select the optimal collection method. For example, the collection unit prioritizes collecting data from sites that the user has frequently visited in the past. The collection unit can also select highly relevant information sources to collect data based on the user's browsing history. The collection unit can also prioritize collecting specific information based on topics in which the user has shown interest in the past. This allows the collection unit to select the optimal data collection method by analyzing the user's past browsing history.

[0035] The collection unit can filter data based on the user's current areas of interest when collecting data. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. The collection unit can also filter and collect related information based on the user's current search keywords. The collection unit can also collect related data based on the content of a page the user is currently viewing. This allows the collection unit to collect highly relevant information by filtering data based on the user's current areas of interest.

[0036] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. Also, if the user is using text input, the collection unit can also prioritize collecting text data. Also, if the user is using image input, the collection unit can also prioritize collecting image data. In this way, the collection unit can efficiently collect data by selecting the optimal collection means depending on the user's input method.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting local news related to the user's current location. The collection unit can also prioritize collecting nearby event information based on the user's location information. The collection unit can also prioritize collecting area-specific advertisements based on the user's location information. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0038] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can prioritize collecting posts from accounts that the user follows on social media. The collection unit can also collect related news based on the user's social media activities. The collection unit can also analyze posts from the user's friends on social media to collect related information. This allows the collection unit to efficiently collect related data by analyzing the user's social media activities.

[0039] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects data from information sources that the user has previously rated highly. The collection unit can also adjust the type of data to be collected based on the user's past feedback. The collection unit can also collect data by avoiding information sources that the user has previously rated poorly. In this way, the collection unit can optimize the collection method by reflecting the user's past feedback.

[0040] The embedding unit can adjust the level of detail of the information based on the advertiser's request when embedding. For example, if the advertiser requests detailed information, the embedding unit embeds detailed advertising information. Also, if the advertiser requests concise information, the embedding unit can embed concise advertising information. Also, if the advertiser wants to emphasize a particular element, the embedding unit can embed the element in a way that makes it stand out. In this way, the embedding unit can maximize the advertising effect by adjusting the level of detail of the information according to the advertiser's request.

[0041] During the integration, the integration unit can apply different integration algorithms depending on the advertiser's industry and product category. For example, if the advertiser is in the food industry, the integration unit can integrate a visually appetizing advertisement. Also, if the advertiser is in the fashion industry, the integration unit can integrate a stylish advertisement. Also, if the advertiser is in the technology industry, the integration unit can integrate an advertisement that includes technical details. This allows the integration unit to deliver effective advertisements by applying an integration algorithm depending on the advertiser's industry and product category.

[0042] During integration, the integration unit can improve the accuracy of integration by referring to past integration results. For example, the integration unit optimizes the integration method based on data from past successful advertising campaigns. The integration unit can also improve the integration method based on data from past unsuccessful advertising campaigns. The integration unit can also improve the accuracy of integration by referring to past user feedback. In this way, the integration unit can improve the accuracy of integration by referring to past integration results.

[0043] At the time of incorporation, the incorporation unit can determine the priority of information based on the advertiser's campaign period. For example, if the advertiser's campaign period is short, the incorporation unit will prioritize incorporation of advertising information. Furthermore, if the advertiser's campaign period is long, the incorporation unit can also incorporate advertising information in stages. Furthermore, the incorporation unit can adjust the display frequency of advertising information in accordance with the advertiser's campaign period. In this way, the incorporation unit can determine the priority of information based on the advertiser's campaign period, thereby enabling effective advertising delivery.

[0044] The embedding unit can customize the content of the information based on the target market of the advertiser when embedding. For example, if the target market of the advertiser is young people, the embedding unit embeds advertising information for young people. Also, if the target market of the advertiser is elderly, the embedding unit can embed advertising information for elderly people. Also, if the target market of the advertiser is a specific region, the embedding unit can embed advertising information related to that region. In this way, the embedding unit can customize the content of the information based on the target market of the advertiser, thereby enabling effective advertisement delivery.

[0045] The incorporation unit can improve the incorporation method by reflecting the advertiser's feedback during incorporation. For example, the incorporation unit improves the method for displaying advertising information based on the advertiser's feedback. The incorporation unit can also adjust the content of the advertising information based on the advertiser's feedback. The incorporation unit can also optimize the timing for displaying the advertising information based on the advertiser's feedback. This allows the incorporation unit to optimize the incorporation method by reflecting the advertiser's feedback.

[0046] At the time of provision, the provision unit can analyze the user's past usage history and select the optimal provision method. For example, the provision unit can preferentially provide services that the user has given high ratings to in the past. The provision unit can also provide related services based on the user's past usage history. The provision unit can also analyze patterns of services that the user has used in the past and select the optimal provision method. In this way, the provision unit can select the optimal service provision method by analyzing the user's past usage history.

[0047] The providing unit can customize the service content based on the user's current areas of interest at the time of providing the service. For example, the providing unit provides a service related to a topic in which the user is currently interested. The providing unit can also customize a related service based on the user's current search keywords. The providing unit can also provide a related service based on the content of a page the user is currently viewing. In this way, the providing unit can provide a more relevant service by customizing the service content based on the user's current areas of interest.

[0048] The providing unit can improve the method of providing the service by reflecting the user's feedback at the time of providing the service. The providing unit improves the method of providing the service based on, for example, the user's feedback. The providing unit can also adjust the content of the service based on the user's feedback. The providing unit can also optimize the timing of providing the service based on the user's feedback. In this way, the providing unit can optimize the method of providing the service by reflecting the user's feedback.

[0049] The providing unit can provide the most appropriate service by taking into consideration the user's geographical location information when providing the service. For example, the providing unit can prioritize providing local services related to the user's current location. The providing unit can also prioritize providing nearby event information based on the user's location information. The providing unit can also prioritize providing area-specific services based on the user's location information. In this way, the providing unit can provide highly relevant services by taking into consideration the user's geographical location information.

[0050] The providing unit can analyze the user's social media activities and provide related services at the time of providing the services. For example, the providing unit can prioritize providing services of accounts that the user follows on social media. The providing unit can also provide related services based on the user's social media activities. The providing unit can also analyze the activities of the user's friends on social media and provide related services. In this way, the providing unit can efficiently provide related services by analyzing the user's social media activities.

[0051] The providing unit can customize the service content by reflecting the user's past feedback when providing the service. For example, the providing unit preferentially provides service content that the user has previously rated highly. The providing unit can also adjust the service content based on the user's past feedback. The providing unit can also avoid providing service content that the user has previously rated poorly. In this way, the providing unit can optimize the service content by reflecting the user's past feedback.

[0052] When setting the period, the period setting unit can optimize the delivery schedule based on the advertiser's campaign period. For example, if the advertiser's campaign period is short, the period setting unit delivers information intensively. Furthermore, if the advertiser's campaign period is long, the period setting unit can deliver information in stages. Furthermore, the period setting unit can adjust the delivery frequency to match the advertiser's campaign period. In this way, the period setting unit can optimize the delivery schedule based on the advertiser's campaign period, thereby enabling effective ad delivery.

[0053] When setting the period, the period setting unit can select the optimal distribution period by referring to the user's past usage history. For example, the period setting unit distributes information according to a period in which the user previously gave a high rating. The period setting unit can also select the optimal distribution period based on the user's past usage history. The period setting unit can also analyze the patterns of periods used by the user in the past and select the optimal distribution period. In this way, the period setting unit can select the optimal distribution period by referring to the user's past usage history.

[0054] The period setting unit can improve the delivery schedule by reflecting the advertiser's feedback when setting the period. The period setting unit improves the delivery schedule based on, for example, the advertiser's feedback. The period setting unit can also adjust the delivery frequency based on the advertiser's feedback. The period setting unit can also optimize the delivery timing based on the advertiser's feedback. In this way, the period setting unit can optimize the delivery schedule by reflecting the advertiser's feedback.

[0055] When setting the period, the period setting unit can set an optimal delivery schedule taking into account the user's geographical location information. For example, the period setting unit can prioritize delivery of local information related to the user's current location. The period setting unit can also prioritize delivery of nearby event information based on the user's location information. The period setting unit can also prioritize delivery of area-specific information based on the user's location information. In this way, the period setting unit can provide highly relevant information by taking into account the user's geographical location information.

[0056] When setting the period, the period setting unit can analyze the user's social media activity and adjust the delivery period. For example, the period setting unit prioritizes delivery of information from accounts the user follows on social media. The period setting unit can also deliver related information based on the user's social media activity. The period setting unit can also analyze the activity of the user's friends on social media and deliver related information. In this way, the period setting unit can efficiently provide related information by analyzing the user's social media activity.

[0057] The period setting unit can customize the delivery schedule by reflecting the user's past feedback when setting the period. For example, the period setting unit prioritizes delivery schedules that the user has previously rated highly. The period setting unit can also adjust the delivery schedule based on the user's past feedback. The period setting unit can also set delivery schedules that avoid delivery schedules that the user has previously rated poorly. In this way, the period setting unit can optimize the delivery schedule by reflecting the user's past feedback.

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

[0059] The collection unit can analyze the user's past purchase history and collect related data preferentially. For example, it collects reviews and ratings related to products the user has purchased in the past. The collection unit can also collect new product information related to products the user has purchased in the past. The collection unit can also collect campaign information related to products the user has purchased in the past. In this way, the collection unit can efficiently collect related data by analyzing the user's past purchase history.

[0060] When collecting data, the collection unit can analyze the user's device usage and select the optimal collection method. For example, if the user is using a smartphone, mobile-friendly data can be collected preferentially. If the user is using a desktop, high-resolution data can be collected. If the user is using a tablet, data suitable for touch operation can be collected. This allows the collection unit to select the optimal data collection method by analyzing the user's device usage.

[0061] When embedding, the embedding unit can adjust the design of the information based on the advertiser's brand image. For example, if the advertiser is a luxury brand, the information can be embedded with a luxurious design. If the advertiser is a casual brand, the information can be embedded with a friendly design. If the advertiser is a technology brand, the information can be embedded with a futuristic design. In this way, the embedding unit can adjust the design of the information based on the advertiser's brand image, enabling effective advertisement delivery.

[0062] The providing unit can analyze the user's interaction history at the time of provision and select the optimal provision method. For example, it can analyze the pattern of links the user has clicked in the past and provide related services. It can also provide related content based on the content of videos the user has watched in the past. It can also analyze the frequency of functions the user has used in the past and select the optimal provision method. In this way, the providing unit can select the optimal service provision method by analyzing the user's interaction history.

[0063] When setting the period, the period setting unit can set a delivery schedule taking into consideration the user's lifestyle. For example, if the user is a morning person, information can be delivered in the morning. If the user is a night owl, information can be delivered in the evening. If the user is active on weekends, information can be delivered concentrated on weekends. In this way, the period setting unit can set an optimal delivery schedule by taking into consideration the user's lifestyle.

[0064] When collecting data, the collection unit can select the optimal collection method taking into account the user's network connection status. For example, if the user is using a high-speed Wi-Fi connection, a large amount of data can be collected. Also, if the user is using a mobile data connection, a collection method that reduces the amount of data can be selected. Also, if the user is offline, data can be collected the next time the user is online. This allows the collection unit to select the optimal data collection method by taking into account the user's network connection status.

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

[0066] Step 1: The collection unit collects data. This data includes text data, image data, and audio data. The collection unit collects the latest information from news sites and social media. The collection unit can also collect data in real time using a browsing function. For example, a web browser can be used to collect news articles and social media posts in real time. Step 2: The embedding unit embeds the advertiser's desired information into the collected data. The advertiser's desired information includes the target audience and the type of advertising content. For example, information about the advertiser's specified products or services may be embedded into the collected news articles. Step 3: The provider uses the embedded data to provide services. Services include news distribution services and advertising distribution services. For example, when the AI ​​generates news articles, it incorporates information desired by advertisers. Step 4: The period setting unit sets the information to be delivered within the period specified by the advertiser. The period includes the date, time, and duration of the period. For example, it sets the advertisement to be displayed during a specific campaign period.

[0067] (Example 2) A system according to an embodiment of the present invention provides real-time learning data to an AI and incorporates information desired by an advertiser into that data. The system includes a collection unit that collects data, an integration unit that incorporates information desired by the advertiser into the collected data, a provision unit that provides services using the incorporated data, and a time period setting unit that delivers information within a time period specified by the advertiser. This allows the system to efficiently perform a series of processes from data collection to information delivery. For example, the system can collect the latest information from news sites and social media, incorporate information specified by the advertiser, and deliver the information to consumers. This allows the AI ​​to constantly learn the latest information, allowing advertisers to deliver information to consumers in real time. For example, when generating news articles, the AI ​​can generate articles based on the latest information and incorporate information desired by the advertiser into the articles. This allows consumers to always obtain the latest information, allowing advertisers to deliver their advertisements effectively.

[0068] An information provision system according to an embodiment includes a collection unit, an integration unit, a provision unit, and a period setting unit. The collection unit collects data. Examples of the data include, but are not limited to, text data, image data, and audio data. The collection unit collects the latest information from news sites and social media sites. The collection unit can also collect data in real time using a browsing function. For example, the collection unit collects news articles and social media posts in real time using a web browser. The integration unit integrates information desired by an advertiser into the collected data. Examples of the information desired by an advertiser include, but are not limited to, a target audience and the type of advertising content. For example, the integration unit integrates information about products or services specified by the advertiser into the collected news articles. The provision unit provides a service using the integrated data. Examples of services include, but are not limited to, a news distribution service and an advertising distribution service. For example, when the AI ​​generates a news article, the provision unit generates the article incorporating the information desired by the advertiser. The period setting unit sets the information to be delivered within a period specified by the advertiser. The period may include, but is not limited to, for example, a date and time, a length of the period, etc. For example, the period setting unit sets an advertisement to be displayed during a specific campaign period. This allows the information provision system according to the embodiment to efficiently perform a series of processes from data collection to information provision. This allows the system's AI to constantly learn the latest information, enabling advertisers to deliver information to consumers in real time.

[0069] The collection unit can collect data using a browsing function. Examples of the browsing function include, but are not limited to, the type of web browser and the method of data collection. For example, the collection unit uses a web browser to collect data in real time from news sites and social media. For example, the collection unit uses a web browser to collect the latest news articles and social media posts. The collection unit can also use the browsing function to collect data from specific websites. For example, the collection unit collects the latest articles from specific news sites and blogs. This allows the collection unit to collect data in real time, thereby always providing the latest information.

[0070] The embedding unit can embed information desired by the advertiser into the collected data. Examples of information desired by the advertiser include, but are not limited to, the target audience and the type of advertising content. The embedding unit, for example, embeds information about products and services specified by the advertiser into the collected news articles. For example, the embedding unit embeds information about products and services specified by the advertiser into the collected data, thereby enabling effective advertisement delivery. For example, the embedding unit embeds information about products and services specified by the advertiser into the collected news articles. The embedding unit can also be configured to deliver information within a period specified by the advertiser. For example, the embedding unit configures the device to display an advertisement during a specific campaign period. In this way, the embedding unit embeds information according to the advertiser's request into the data, enabling effective advertisement delivery.

[0071] The provision unit allows the AI ​​to generate news articles, and can generate articles that incorporate information desired by advertisers. Examples of AI include, but are not limited to, machine learning algorithms and natural language processing technologies. For example, when the AI ​​generates a news article, the provision unit generates the article that incorporates information desired by advertisers. For example, when the AI ​​generates a news article, the provision unit incorporates information about products or services specified by advertisers. The provision unit can also distribute the news articles generated by the AI. For example, the provision unit provides the news articles generated by the AI ​​as a news distribution service. In this way, the provision unit can increase advertising effectiveness by generating news articles that include advertiser information.

[0072] The period setting unit can set the information to be delivered within a period specified by the advertiser. The period includes, but is not limited to, for example, a date and time, and the length of the period. For example, the period setting unit sets the advertisement to be displayed during a specific campaign period. For example, the period setting unit sets the information to be delivered within a period specified by the advertiser. The period setting unit can also set a schedule for delivering the information within the period specified by the advertiser. For example, the period setting unit sets a schedule for displaying the advertisement during a specific time period. In this way, the period setting unit can maximize the effectiveness of the advertisement by delivering the information within the period specified by the advertiser.

[0073] The collection unit can estimate the user's emotions and adjust the timing of data collection. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden on the user. Furthermore, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more information. Furthermore, if the user is excited, the collection unit can collect data in real time and immediately reflect the data. In this way, the collection unit can reduce the burden on the user by adjusting the timing of data collection according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0074] When collecting data, the collection unit can analyze the user's past browsing history and select the optimal collection method. For example, the collection unit prioritizes collecting data from sites that the user has frequently visited in the past. The collection unit can also select highly relevant information sources to collect data based on the user's browsing history. The collection unit can also prioritize collecting specific information based on topics in which the user has shown interest in the past. This allows the collection unit to select the optimal data collection method by analyzing the user's past browsing history.

[0075] The collection unit can filter data based on the user's current areas of interest when collecting data. For example, the collection unit preferentially collects data related to topics in which the user is currently interested. The collection unit can also filter and collect related information based on the user's current search keywords. The collection unit can also collect related data based on the content of a page the user is currently viewing. This allows the collection unit to collect highly relevant information by filtering data based on the user's current areas of interest.

[0076] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user is using voice input, the collection unit prioritizes collecting voice data. Also, if the user is using text input, the collection unit can also prioritize collecting text data. Also, if the user is using image input, the collection unit can also prioritize collecting image data. In this way, the collection unit can efficiently collect data by selecting the optimal collection means depending on the user's input method.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected. For example, if the user is feeling stressed, the collection unit can prioritize collecting relaxing content. Furthermore, if the user is relaxed, the collection unit can also prioritize collecting interesting content. Furthermore, if the user is excited, the collection unit can also prioritize collecting information that is immediately useful. In this way, the collection unit can prioritize collecting information that is useful to the user by determining the priority of data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0078] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, the collection unit prioritizes collecting local news related to the user's current location. The collection unit can also prioritize collecting nearby event information based on the user's location information. The collection unit can also prioritize collecting area-specific advertisements based on the user's location information. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information.

[0079] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can prioritize collecting posts from accounts that the user follows on social media. The collection unit can also collect related news based on the user's social media activities. The collection unit can also analyze posts from the user's friends on social media to collect related information. This allows the collection unit to efficiently collect related data by analyzing the user's social media activities.

[0080] When collecting data, the collection unit can customize the collection method by reflecting the user's past feedback. For example, the collection unit preferentially collects data from information sources that the user has previously rated highly. The collection unit can also adjust the type of data to be collected based on the user's past feedback. The collection unit can also collect data by avoiding information sources that the user has previously rated poorly. In this way, the collection unit can optimize the collection method by reflecting the user's past feedback.

[0081] The embedding unit can estimate the user's emotions and adjust the method of incorporating information. For example, if the user is relaxed, the embedding unit embeds advertising information naturally. Furthermore, if the user is in a hurry, the embedding unit can embed advertising information in a way that makes it stand out. Furthermore, if the user is excited, the embedding unit can embed visually stimulating advertising information. This allows the embedding unit to adjust the method of incorporating information according to the user's emotions, thereby enabling effective information provision. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0082] The embedding unit can adjust the level of detail of the information based on the advertiser's request when embedding. For example, if the advertiser requests detailed information, the embedding unit embeds detailed advertising information. Also, if the advertiser requests concise information, the embedding unit can embed concise advertising information. Also, if the advertiser wants to emphasize a particular element, the embedding unit can embed the element in a way that makes it stand out. In this way, the embedding unit can maximize the advertising effect by adjusting the level of detail of the information according to the advertiser's request.

[0083] During the integration, the integration unit can apply different integration algorithms depending on the advertiser's industry and product category. For example, if the advertiser is in the food industry, the integration unit can integrate a visually appetizing advertisement. Also, if the advertiser is in the fashion industry, the integration unit can integrate a stylish advertisement. Also, if the advertiser is in the technology industry, the integration unit can integrate an advertisement that includes technical details. This allows the integration unit to deliver effective advertisements by applying an integration algorithm depending on the advertiser's industry and product category.

[0084] During integration, the integration unit can improve the accuracy of integration by referring to past integration results. For example, the integration unit optimizes the integration method based on data from past successful advertising campaigns. The integration unit can also improve the integration method based on data from past unsuccessful advertising campaigns. The integration unit can also improve the accuracy of integration by referring to past user feedback. In this way, the integration unit can improve the accuracy of integration by referring to past integration results.

[0085] The embedding unit can estimate the user's emotions and adjust the order in which information is incorporated. For example, if the user is relaxed, the embedding unit embeds advertising information later. Also, if the user is in a hurry, the embedding unit can embed advertising information first. Also, if the user is excited, the embedding unit can embed advertising information in the middle. This allows the embedding unit to adjust the order in which information is incorporated according to the user's emotions, thereby enabling effective information provision. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0086] At the time of incorporation, the incorporation unit can determine the priority of information based on the advertiser's campaign period. For example, if the advertiser's campaign period is short, the incorporation unit will prioritize incorporation of advertising information. Furthermore, if the advertiser's campaign period is long, the incorporation unit can also incorporate advertising information in stages. Furthermore, the incorporation unit can adjust the display frequency of advertising information in accordance with the advertiser's campaign period. In this way, the incorporation unit can determine the priority of information based on the advertiser's campaign period, thereby enabling effective advertising delivery.

[0087] The embedding unit can customize the content of the information based on the target market of the advertiser when embedding. For example, if the target market of the advertiser is young people, the embedding unit embeds advertising information for young people. Also, if the target market of the advertiser is elderly, the embedding unit can embed advertising information for elderly people. Also, if the target market of the advertiser is a specific region, the embedding unit can embed advertising information related to that region. In this way, the embedding unit can customize the content of the information based on the target market of the advertiser, thereby enabling effective advertisement delivery.

[0088] The incorporation unit can improve the incorporation method by reflecting the advertiser's feedback during incorporation. For example, the incorporation unit improves the method for displaying advertising information based on the advertiser's feedback. The incorporation unit can also adjust the content of the advertising information based on the advertiser's feedback. The incorporation unit can also optimize the timing for displaying the advertising information based on the advertiser's feedback. This allows the incorporation unit to optimize the incorporation method by reflecting the advertiser's feedback.

[0089] The providing unit can estimate the user's emotions and adjust the method of providing the service. For example, if the user is relaxed, the providing unit can provide the service at a leisurely pace. Also, if the user is in a hurry, the providing unit can provide the service quickly. Also, if the user is excited, the providing unit can provide a visually stimulating service. In this way, the providing unit can adjust the method of providing the service according to the user's emotions, thereby providing a more appropriate service. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0090] At the time of provision, the provision unit can analyze the user's past usage history and select the optimal provision method. For example, the provision unit can preferentially provide services that the user has given high ratings to in the past. The provision unit can also provide related services based on the user's past usage history. The provision unit can also analyze patterns of services that the user has used in the past and select the optimal provision method. In this way, the provision unit can select the optimal service provision method by analyzing the user's past usage history.

[0091] The providing unit can customize the service content based on the user's current areas of interest at the time of providing the service. For example, the providing unit provides a service related to a topic in which the user is currently interested. The providing unit can also customize a related service based on the user's current search keywords. The providing unit can also provide a related service based on the content of a page the user is currently viewing. In this way, the providing unit can provide a more relevant service by customizing the service content based on the user's current areas of interest.

[0092] The providing unit can improve the method of providing the service by reflecting the user's feedback at the time of providing the service. The providing unit improves the method of providing the service based on, for example, the user's feedback. The providing unit can also adjust the content of the service based on the user's feedback. The providing unit can also optimize the timing of providing the service based on the user's feedback. In this way, the providing unit can optimize the method of providing the service by reflecting the user's feedback.

[0093] The providing unit can estimate the user's emotions and determine the priority of services. For example, if the user is feeling stressed, the providing unit can prioritize providing services that help the user relax. Furthermore, if the user is relaxed, the providing unit can also prioritize providing services that attract the user's attention. Furthermore, if the user is excited, the providing unit can also prioritize providing services that are immediately useful. In this way, the providing unit can provide more appropriate services by determining the priority of services according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0094] The providing unit can provide the most appropriate service by taking into consideration the user's geographical location information when providing the service. For example, the providing unit can prioritize providing local services related to the user's current location. The providing unit can also prioritize providing nearby event information based on the user's location information. The providing unit can also prioritize providing area-specific services based on the user's location information. In this way, the providing unit can provide highly relevant services by taking into consideration the user's geographical location information.

[0095] The providing unit can analyze the user's social media activities and provide related services at the time of providing the services. For example, the providing unit can prioritize providing services of accounts that the user follows on social media. The providing unit can also provide related services based on the user's social media activities. The providing unit can also analyze the activities of the user's friends on social media and provide related services. In this way, the providing unit can efficiently provide related services by analyzing the user's social media activities.

[0096] The providing unit can customize the service content by reflecting the user's past feedback when providing the service. For example, the providing unit preferentially provides service content that the user has previously rated highly. The providing unit can also adjust the service content based on the user's past feedback. The providing unit can also avoid providing service content that the user has previously rated poorly. In this way, the providing unit can optimize the service content by reflecting the user's past feedback.

[0097] The period setting unit can estimate the user's emotions and adjust the timing of information delivery. For example, if the user is feeling stressed, the period setting unit can provide information with a reduced delivery frequency. Furthermore, if the user is relaxed, the period setting unit can also provide information with an increased delivery frequency. Furthermore, if the user is excited, the period setting unit can deliver information in real time. In this way, the period setting unit can provide information at a more appropriate timing by adjusting the timing of information delivery according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0098] When setting the period, the period setting unit can optimize the delivery schedule based on the advertiser's campaign period. For example, if the advertiser's campaign period is short, the period setting unit delivers information intensively. Furthermore, if the advertiser's campaign period is long, the period setting unit can deliver information in stages. Furthermore, the period setting unit can adjust the delivery frequency to match the advertiser's campaign period. In this way, the period setting unit can optimize the delivery schedule based on the advertiser's campaign period, thereby enabling effective ad delivery.

[0099] When setting the period, the period setting unit can select the optimal distribution period by referring to the user's past usage history. For example, the period setting unit distributes information according to a period in which the user previously gave a high rating. The period setting unit can also select the optimal distribution period based on the user's past usage history. The period setting unit can also analyze the patterns of periods used by the user in the past and select the optimal distribution period. In this way, the period setting unit can select the optimal distribution period by referring to the user's past usage history.

[0100] The period setting unit can improve the delivery schedule by reflecting the advertiser's feedback when setting the period. The period setting unit improves the delivery schedule based on, for example, the advertiser's feedback. The period setting unit can also adjust the delivery frequency based on the advertiser's feedback. The period setting unit can also optimize the delivery timing based on the advertiser's feedback. In this way, the period setting unit can optimize the delivery schedule by reflecting the advertiser's feedback.

[0101] The period setting unit can estimate the user's emotions and determine the priority of the delivery period. For example, if the user is feeling stressed, the period setting unit can prioritize the delivery of relaxing information. Furthermore, if the user is relaxed, the period setting unit can also prioritize the delivery of interesting information. Furthermore, if the user is excited, the period setting unit can also prioritize the delivery of immediately useful information. In this way, the period setting unit can provide more appropriate information by determining the priority of the delivery period according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0102] When setting the period, the period setting unit can set an optimal delivery schedule taking into account the user's geographical location information. For example, the period setting unit can prioritize delivery of local information related to the user's current location. The period setting unit can also prioritize delivery of nearby event information based on the user's location information. The period setting unit can also prioritize delivery of area-specific information based on the user's location information. In this way, the period setting unit can provide highly relevant information by taking into account the user's geographical location information.

[0103] When setting the period, the period setting unit can analyze the user's social media activity and adjust the delivery period. For example, the period setting unit prioritizes delivery of information from accounts the user follows on social media. The period setting unit can also deliver related information based on the user's social media activity. The period setting unit can also analyze the activity of the user's friends on social media and deliver related information. In this way, the period setting unit can efficiently provide related information by analyzing the user's social media activity.

[0104] The period setting unit can customize the delivery schedule by reflecting the user's past feedback when setting the period. For example, the period setting unit prioritizes delivery schedules that the user has previously rated highly. The period setting unit can also adjust the delivery schedule based on the user's past feedback. The period setting unit can also set delivery schedules that avoid delivery schedules that the user has previously rated poorly. In this way, the period setting unit can optimize the delivery schedule by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, incorporation unit, provision unit, and period setting unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects the latest information from news sites and social networking sites using the camera 42 and communication I / F 44 of the smart device 14. The incorporation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and incorporates information desired by the advertiser into the collected data. The provision unit provides the incorporated data to consumers, for example, by the output device 40 of the smart device 14. The period setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets the information to be delivered within a period specified by the advertiser. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, incorporation unit, provision unit, and period setting unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects the latest information from news sites and SNS using the camera 42 and communication I / F 44 of the smart glasses 214. The incorporation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and incorporates information desired by the advertiser into the collected data. The provision unit provides the incorporated data to consumers using, for example, the speaker 240 of the smart glasses 214. The period setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and sets the information to be delivered within a period specified by the advertiser. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, incorporation unit, provision unit, and period setting unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects the latest information from news sites and social networking sites using the camera 42 and communication I / F 44 of the headset type terminal 314. The incorporation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and incorporates information desired by the advertiser into the collected data. The provision unit provides the incorporated data to consumers using, for example, the display 343 of the headset type terminal 314. The period setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets the information to be delivered within a period specified by the advertiser. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, incorporation unit, provision unit, and period setting unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects the latest information from news sites and SNS using the camera 42 and communication I / F 44 of the robot 414. The incorporation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and incorporates information desired by the advertiser into the collected data. The provision unit provides the incorporated data to consumers, for example, by the speaker 240 of the robot 414. The period setting unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and sets the information to be delivered within a period specified by the advertiser.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The collection unit can analyze the user's past purchase history and collect related data preferentially. For example, it collects reviews and ratings related to products the user has purchased in the past. The collection unit can also collect new product information related to products the user has purchased in the past. The collection unit can also collect campaign information related to products the user has purchased in the past. In this way, the collection unit can efficiently collect related data by analyzing the user's past purchase history.

[0107] The embedding unit can estimate the user's emotions and adjust the display format of information. For example, if the user is relaxed, the embedding unit can display information in a visually calm design. If the user is in a hurry, the embedding unit can display information in a concise and easy-to-understand format. If the user is excited, the embedding unit can display information in a visually stimulating design. This allows the embedding unit to provide information effectively by adjusting the display format of information according to the user's emotions.

[0108] The providing unit can estimate the user's emotions and adjust the frequency of service provision. For example, if the user is feeling stressed, the frequency of service provision can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of service provision can be increased to provide more information. Also, if the user is excited, the service can be provided in real time. In this way, the providing unit can provide more appropriate services by adjusting the frequency of service provision according to the user's emotions.

[0109] The period setting unit can estimate the user's emotions and adjust the information delivery method. For example, if the user is feeling stressed, the information delivery frequency can be reduced. If the user is relaxed, the information delivery frequency can be increased. If the user is excited, the information can be delivered in real time. In this way, the period setting unit can provide information at more appropriate times by adjusting the information delivery method according to the user's emotions.

[0110] The collection unit can estimate the user's emotions and adjust the type of data to be collected. For example, if the user is feeling stressed, it can prioritize collecting relaxing content. Also, if the user is relaxed, it can prioritize collecting interesting content. Also, if the user is excited, it can prioritize collecting information that is immediately useful. In this way, the collection unit can prioritize collecting information that is useful to the user by adjusting the type of data to be collected according to the user's emotions.

[0111] When collecting data, the collection unit can analyze the user's device usage and select the optimal collection method. For example, if the user is using a smartphone, mobile-friendly data can be collected preferentially. If the user is using a desktop, high-resolution data can be collected. If the user is using a tablet, data suitable for touch operation can be collected. This allows the collection unit to select the optimal data collection method by analyzing the user's device usage.

[0112] When embedding, the embedding unit can adjust the design of the information based on the advertiser's brand image. For example, if the advertiser is a luxury brand, the information can be embedded with a luxurious design. If the advertiser is a casual brand, the information can be embedded with a friendly design. If the advertiser is a technology brand, the information can be embedded with a futuristic design. In this way, the embedding unit can adjust the design of the information based on the advertiser's brand image, enabling effective advertisement delivery.

[0113] The providing unit can analyze the user's interaction history at the time of provision and select the optimal provision method. For example, it can analyze the pattern of links the user has clicked in the past and provide related services. It can also provide related content based on the content of videos the user has watched in the past. It can also analyze the frequency of functions the user has used in the past and select the optimal provision method. In this way, the providing unit can select the optimal service provision method by analyzing the user's interaction history.

[0114] When setting the period, the period setting unit can set a delivery schedule taking into consideration the user's lifestyle. For example, if the user is a morning person, information can be delivered in the morning. If the user is a night owl, information can be delivered in the evening. If the user is active on weekends, information can be delivered concentrated on weekends. In this way, the period setting unit can set an optimal delivery schedule by taking into consideration the user's lifestyle.

[0115] When collecting data, the collection unit can select the optimal collection method taking into account the user's network connection status. For example, if the user is using a high-speed Wi-Fi connection, a large amount of data can be collected. Also, if the user is using a mobile data connection, a collection method that reduces the amount of data can be selected. Also, if the user is offline, data can be collected the next time the user is online. This allows the collection unit to select the optimal data collection method by taking into account the user's network connection status.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The collection unit collects data. This data includes text data, image data, and audio data. The collection unit collects the latest information from news sites and social media. The collection unit can also collect data in real time using a browsing function. For example, a web browser can be used to collect news articles and social media posts in real time. Step 2: The embedding unit embeds the advertiser's desired information into the collected data. The advertiser's desired information includes the target audience and the type of advertising content. For example, information about the advertiser's specified products or services may be embedded into the collected news articles. Step 3: The provider uses the embedded data to provide services. Services include news distribution services and advertising distribution services. For example, when the AI ​​generates news articles, it incorporates information desired by advertisers. Step 4: The period setting unit sets the information to be delivered within the period specified by the advertiser. The period includes the date, time, and duration of the period. For example, it sets the advertisement to be displayed during a specific campaign period.

[0118] 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.

[0119] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an incorporating unit that incorporates information desired by the advertiser into the data collected by the collecting unit; a providing unit that provides a service using the data incorporated by the incorporating unit; Equipped with a time setting unit that delivers information within the period specified by the advertiser A system characterized by:

2. The collecting unit Collect data using browsing features 2. The system of claim 1.

3. The embedding portion is Incorporating advertiser-requested information into the collected data 2. The system of claim 1.

4. The providing unit AI generates news articles and incorporates information advertisers want.

2. The system of claim 1.

5. The period setting unit Set the information to be delivered within the period specified by the advertiser 2. The system of claim 1.

6. The collecting unit Infer user emotions and adjust data collection timing 2. The system of claim 1.

7. The collecting unit When collecting data, analyze the user's past browsing history and select the most appropriate collection method.

2. The system of claim 1.

8. The collecting unit Filtering data collection based on the user's current interests 2. The system of claim 1.

Images