System

Abstract

An object of a system according to an embodiment is to generate and provide content for public relations of a company based on the hobbies and preferences of a group.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to the hobby and preference of the user. The analysis unit analyzes the data collected by the collection unit and identifies the hobby and preference of the group. The generation unit generates the content for public relations of the company based on the hobby and preference specified by the analysis unit. The providing unit provides the content generated by the generating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have been too specialized in catering to the hobbies and preferences of individual users, making it difficult to present advertisements to groups of people.

[0005] The system according to the embodiment aims to generate and provide corporate public relations content based on the interests and preferences of a group. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to users' hobbies and preferences. The analysis unit analyzes the data collected by the collection unit and identifies the hobbies and preferences of a group. The generation unit generates public relations content for the company based on the hobbies and preferences identified by the analysis unit. The provision unit provides the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can generate and provide corporate promotional content based on the interests and preferences of a group. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An advertisement generation system according to an embodiment of the present invention is a system for adapting corporate promotional content to the hobbies and preferences of a group. The advertisement generation system collects and analyzes data related to users' hobbies and preferences, and generates and provides promotional content based on the hobbies and preferences of the group. For example, the advertisement generation system generates and presents an anime-style teen drama-style advertisement to users who like anime and youth-related content. In this way, the advertisement is transformed into content that is more easily accepted by the group, thereby maximizing the effectiveness of promotional content. This allows the advertisement generation system to adapt corporate promotional content to the hobbies and preferences of the group, thereby maximizing the effectiveness of promotional content. For example, presenting an anime-style teen drama-style advertisement to users who like anime and youth-related content can increase the viewing rate and click-through rate of the advertisement.

[0029] An advertisement generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to a user's interests and preferences. For example, the collection unit collects data such as videos watched by the user and advertisements clicked by the user. The collection unit can also collect data such as the user's social media activities and search history. The collection unit can also collect user survey results and feedback data. The analysis unit analyzes the data collected by the collection unit to identify the interests and preferences of a group. For example, the analysis unit analyzes the collected data using statistical analysis or a machine learning algorithm. The analysis unit can also use a generation AI to identify the interests and preferences of a group based on the collected data. The analysis unit can also identify the interests and preferences of a group through analysis of a questionnaire survey or behavioral data. The generation unit generates corporate promotional content based on the interests and preferences identified by the analysis unit. For example, the generation unit generates promotional content based on the identified interests and preferences using a generation AI. The generation unit can also generate an anime-style teen drama-style advertisement. Furthermore, the generation unit can change the design and content of the public relations content based on the identified hobbies and preferences. The provision unit provides the content generated by the generation unit. For example, the provision unit provides the generated content to a user. The provision unit can also evaluate the effectiveness of the generated content. Furthermore, the provision unit can provide the generated content by methods such as email distribution or posting on a website. In this way, the advertisement generation system according to the embodiment can generate advertisements for a group based on the hobbies and preferences of users, maximizing the effectiveness of public relations.

[0030] The collection unit can collect data on videos viewed by users or advertisements clicked by users. The collection unit, for example, collects data on videos viewed by users. For example, the collection unit collects viewing histories of streaming videos and downloaded videos. The collection unit can also collect data on advertisements clicked by users. For example, the collection unit collects click histories of banner ads and text ads. Furthermore, the collection unit can collect data for identifying hobbies and preferences based on the user's viewing history and click history. In this way, by collecting the user's viewing history and click history, the hobbies and preferences can be accurately understood. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input viewing history and click history data into a generation AI and cause the generation AI to identify the hobbies and preferences.

[0031] The analysis unit can identify the hobbies and preferences of a group based on the collected data. The analysis unit, for example, identifies the hobbies and preferences of a group based on the collected data. For example, the analysis unit analyzes the collected data using statistical analysis. The analysis unit can also identify the hobbies and preferences of a group based on the collected data using a machine learning algorithm. Furthermore, the analysis unit can identify the hobbies and preferences of a group through analysis of questionnaire surveys and behavioral data. By identifying the hobbies and preferences of a group, targeted advertisements can be generated. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to identify the hobbies and preferences of a group.

[0032] The generation unit can arrange the company's public relations content based on the identified hobbies and preferences. The generation unit arranges the company's public relations content based on, for example, the identified hobbies and preferences. For example, the generation unit uses a generation AI to generate public relations content based on the identified hobbies and preferences. The generation unit can also generate anime-style youth drama-style advertisements. Furthermore, the generation unit can change the design and content of the public relations content based on the identified hobbies and preferences. This increases the effectiveness of the advertisement by generating content tailored to the hobbies and preferences. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input data on the identified hobbies and preferences into the generation AI and cause the generation AI to generate the public relations content.

[0033] The providing unit can provide the generated content to a user. The providing unit, for example, provides the generated content to a user. For example, the providing unit provides the generated content by email distribution, posting on a website, or other methods. The providing unit can also evaluate the effectiveness of the generated content. For example, the providing unit evaluates the view rate and click rate of the generated content. Furthermore, the providing unit can collect feedback on the generated content and reflect it in the next content generation. In this way, by providing the generated content to a user, the view rate and click rate of advertisements can be improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data for evaluating the effectiveness of the generated content to a generation AI and cause the generation AI to perform the evaluation.

[0034] The generation unit can generate anime-style advertisements. The generation unit generates, for example, anime-style advertisements. For example, the generation unit uses a generation AI to generate anime-style teen drama-style advertisements. The generation unit can also generate advertisements using anime-style character designs and storytelling. Furthermore, the generation unit can change the design and content of the anime-style advertisements. In this way, by generating anime-style teen drama-style advertisements, it is possible to provide effective advertisements to groups with specific hobbies and preferences. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input design data for the anime-style advertisement into the generation AI and cause the generation AI to generate the advertisement.

[0035] The providing unit can evaluate the effectiveness of the generated content. The providing unit, for example, evaluates the effectiveness of the generated content. For example, the providing unit evaluates the viewer rate and click rate of the generated content. The providing unit can also evaluate the conversion rate and engagement rate of the generated content. Furthermore, the providing unit can collect feedback on the generated content and reflect it in the next content generation. In this way, by evaluating the effectiveness of the generated content, it is possible to find areas for improvement in advertising. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data for evaluating the effectiveness of the generated content into a generation AI and have the generation AI perform the evaluation.

[0036] The collection unit can analyze a user's past viewing history and click history and select an appropriate data collection method. The collection unit, for example, analyzes a user's past viewing history and click history and selects an appropriate data collection method. For example, the collection unit prioritizes collecting videos of genres frequently watched by the user. The collection unit can also analyze trends in advertisements clicked by the user and collect related data. Furthermore, the collection unit can analyze a user's viewing time period and concentrate data collection on that time period. In this way, the optimal data collection method can be selected by analyzing the past viewing history and click history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on a user's viewing history and click history into a generation AI and have the generation AI select an optimal data collection method.

[0037] The collection unit can filter data based on the user's current areas of interest and activity status when collecting data. For example, the collection unit can filter data based on the user's current areas of interest and activity status when collecting data. For example, the collection unit can collect related data based on the genre of videos the user is currently watching. The collection unit can also analyze the activity status of online communities in which the user participates and collect related data. Furthermore, the collection unit can filter data based on keywords recently searched by the user. In this way, highly relevant data can be collected by filtering data based on the user's current areas of interest and activity status. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the user's areas of interest and activity status to a generation AI and have the generation AI perform data filtering.

[0038] The collection unit can select an appropriate collection means according to the user's input method when collecting data. For example, the collection unit selects an appropriate collection means according to the user's input method when collecting data. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Furthermore, if the user uploads an image, the collection unit can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method to the generation AI and cause the generation AI to select the collection means.

[0039] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data around the user's home. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's geographical location information to the generation AI and cause the generation AI to collect data.

[0040] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect data based on content shared by the user on social media. The collection unit can also analyze the activities of accounts the user follows on social media and collect related data. Furthermore, the collection unit can analyze the activities of groups the user participates in on social media and collect related data. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's social media activities into a generation AI and cause the generation AI to collect data.

[0041] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the user in the past. The collection unit can also prioritize the use of data collection methods that the user previously preferred. Furthermore, the collection unit can eliminate data collection methods that the user previously avoided and use other methods. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0042] The analysis unit can improve the accuracy of the analysis based on the interrelationships between data during analysis. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between data during analysis. For example, the analysis unit analyzes the interrelationships between a user's viewing history and click history. The analysis unit can also analyze the interrelationships between a user's social media activity and viewing history. Furthermore, the analysis unit can analyze the interrelationships between a user's geographic location information and viewing history. This improves the accuracy of the analysis by taking into account the interrelationships between data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can perform the analysis while taking into account the attribute information of the data submitter. For example, the analysis unit performs the analysis while taking into account the attribute information of the data submitter. For example, the analysis unit analyzes the data based on the user's age and gender. The analysis unit can also analyze the data based on the user's occupation and hobbies. Furthermore, the analysis unit can analyze the data based on the user's place of residence and education level. This allows for more accurate analysis by taking into account the attribute information of the submitter. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the submitter's attribute information into the generation AI and have the generation AI perform the analysis.

[0044] The analysis unit can weight the analysis based on the frequency of data submission during analysis. The analysis unit, for example, weights the analysis based on the frequency of data submission during analysis. For example, the analysis unit may assign a higher weight to frequently submitted data during analysis. The analysis unit may also assign a lower weight to infrequently submitted data during analysis. Furthermore, the analysis unit can adjust the weighting of the analysis taking into account fluctuations in submission frequency. Thus, weighting based on submission frequency improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data on the submission frequency to a generation AI and have the generation AI perform the weighting.

[0045] The analysis unit can perform the analysis taking into account the geographical distribution of the data. For example, the analysis unit performs the analysis taking into account the geographical distribution of the data. For example, the analysis unit analyzes the data based on the user's place of residence. The analysis unit can also analyze the data based on the user's travel destinations. Furthermore, the analysis unit can analyze the data based on the user's range of activities. In this way, by taking the geographical distribution into consideration, analysis can be performed according to regional characteristics. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data into the generation AI and have the generation AI perform the analysis.

[0046] The analysis unit can improve the accuracy of the analysis based on literature related to the data during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the data during analysis. For example, the analysis unit analyzes the data by referring to related academic papers. The analysis unit can also analyze the data by referring to related industry reports. Furthermore, the analysis unit can analyze the data by referring to related patent documents. As a result, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data from related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0047] The analysis unit can perform analysis based on the market value of the data during analysis. For example, the analysis unit performs analysis based on the market value of the data during analysis. For example, the analysis unit focuses analysis on data with high market value. The analysis unit can also perform complementary analysis on data with low market value. Furthermore, the analysis unit can adjust the focus of the analysis in consideration of fluctuations in market value. In this way, analysis that is beneficial to business can be performed by taking market value into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market value data into a generation AI and have the generation AI perform the analysis.

[0048] The generation unit can adjust the level of detail of the generation based on the identified hobbies and preferences when generating content. The generation unit, for example, adjusts the level of detail of the generation based on the identified hobbies and preferences when generating content. For example, if the user likes anime, the generation unit can generate detailed anime-style content. Also, if the user likes teenage stuff, the generation unit can generate detailed teen drama-style content. Furthermore, if the user likes science fiction, the generation unit can generate detailed science fiction-style content. In this way, by adjusting the level of detail of the generation based on the hobbies and preferences, it is possible to generate content that is attractive to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the identified hobbies and preferences into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0049] The generation unit can apply different generation algorithms according to different interests and preferences when generating content. For example, the generation unit applies different generation algorithms according to different interests and preferences when generating content. For example, the generation unit applies an anime-style generation algorithm to a user who likes anime. The generation unit can also apply a youth drama-style generation algorithm to a user who likes teenage stuff. The generation unit can also apply a science fiction-style generation algorithm to a user who likes science fiction. In this way, personalized content can be generated by applying generation algorithms according to different interests and preferences. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data of different interests and preferences into the generation AI and cause the generation AI to apply the generation algorithm.

[0050] The generation unit can improve the accuracy of generation by referring to the user's past generation results when generating content. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results when generating content. For example, the generation unit generates content based on generation results that the user has previously preferred. The generation unit can also eliminate generation results that the user has previously avoided and use other methods. Furthermore, the generation unit can adjust the generation algorithm based on the user's past feedback. In this way, the accuracy of generation is improved by referring to past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0051] The generation unit can determine the generation priority based on the submission time of the identified hobbies and preferences when generating content. The generation unit, for example, determines the generation priority based on the submission time of the identified hobbies and preferences when generating content. For example, the generation unit prioritizes generating content in a genre in which the user has recently become interested. The generation unit can also prioritize generating content in a genre in which the user has been interested for a long time. Furthermore, the generation unit can quickly generate content in a genre in which the user is temporarily interested. This allows timely content to be provided by determining the generation priority based on the submission time. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission time of the identified hobbies and preferences into the generation AI and have the generation AI determine the generation priority.

[0052] The generation unit can adjust the generation order based on the identified relevance of the hobbies and preferences when generating content. For example, the generation unit can first generate content in the genre in which the user is most interested. Furthermore, if the user is interested in multiple related genres, the generation unit can also generate content in descending order of relevance. Furthermore, the generation unit can prioritize generating content in a genre in which the user has recently become interested. By adjusting the generation order based on relevance, content can be provided in an optimal order for the user. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the identified relevance of the hobbies and preferences into the generation AI and cause the generation AI to adjust the generation order.

[0053] The generation unit can appropriately use technical terminology in the content generation process according to the user's level of expertise. For example, the generation unit can adjust the use of technical terminology in the content generation process according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates content that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can generate easy-to-understand content that avoids technical terminology. Furthermore, the generation unit can adjust the frequency of use of technical terminology according to the user's level of knowledge. This allows for the provision of content that is easy for the user to understand by adjusting the use of technical terminology according to the level of expertise. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0054] The providing unit can select an appropriate delivery method by referring to the user's past viewing history when providing content. For example, the providing unit can select an appropriate delivery method by referring to the user's past viewing history when providing content. For example, the providing unit can prioritize delivery methods that the user has previously preferred. The providing unit can also eliminate delivery methods that the user has previously avoided and use other methods. Furthermore, the providing unit can select an optimal delivery method based on the user's viewing history. In this way, the optimal delivery method can be selected by referring to the past viewing history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the user's viewing history into a generation AI and have the generation AI select a delivery method.

[0055] The providing unit can customize the content to be provided according to the user's current task when providing content. For example, the providing unit customizes the content to be provided according to the user's current task when providing content. For example, when the user is at work, the providing unit provides content that can be viewed in a short time. Furthermore, when the user is on a break, the providing unit can also provide content that allows the user to relax. Furthermore, when the user is on the move, the providing unit can also provide content optimized for mobile devices. In this way, by customizing the content to be provided according to the current task, it is possible to provide optimal content for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input data on the user's current task into a generating AI and cause the generating AI to customize the content to be provided.

[0056] The providing unit can improve the delivery method by reflecting user feedback when providing content. For example, the providing unit improves the delivery method by reflecting user feedback when providing content. For example, if the user provides positive feedback on the delivery method, the providing unit continues that method. Also, if the user provides negative feedback on the delivery method, the providing unit can try another method. Furthermore, the providing unit can customize the delivery method based on user feedback. In this way, the delivery method can be continuously improved by reflecting feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the delivery method.

[0057] The providing unit can select an appropriate delivery method based on the user's geographical location information when providing content. For example, the providing unit selects the optimal delivery method by taking the user's geographical location information into consideration when providing content. For example, when the user is in a specific area, the providing unit can provide content related to that area. Furthermore, when the user is traveling, the providing unit can also provide content related to the travel destination. Furthermore, when the user is at home, the providing unit can also provide content around the user's home. In this way, the optimal delivery method can be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the delivery method.

[0058] The providing unit can customize the content to be provided by analyzing the user's social media activity when providing content. For example, the providing unit customizes the content to be provided by analyzing the user's social media activity when providing content. For example, the providing unit customizes the content to be provided based on content shared by the user on social media. The providing unit can also analyze the activity of accounts the user follows on social media and provide related content. Furthermore, the providing unit can analyze the activity of groups the user participates in on social media and provide related content. In this way, highly relevant content can be provided by analyzing social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to customize the content to be provided.

[0059] The providing unit can customize the delivery method by reflecting the user's past feedback when providing content. For example, the providing unit customizes the delivery method by reflecting the user's past feedback when providing content. For example, the providing unit adjusts the type of content to be provided based on feedback provided by the user in the past. The providing unit can also preferentially use delivery methods that the user has previously preferred. Furthermore, the providing unit can eliminate delivery methods that the user has avoided in the past and use other methods. In this way, the delivery method can be optimized by reflecting past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to customize the delivery method.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The collection unit can collect data taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. Also, if the user is traveling, the collection unit can collect data related to the travel destination. Furthermore, if the user is at home, the collection unit can prioritize collecting data around the home. In this way, by taking the geographical location information into consideration, highly relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect data.

[0062] The providing unit can select an appropriate delivery method by referring to the user's past viewing history. For example, the providing unit can prioritize delivery methods that the user has previously preferred. It can also eliminate delivery methods that the user has previously avoided and use other methods. Furthermore, the providing unit can select an optimal delivery method based on the user's viewing history. In this way, the optimal delivery method can be selected by referring to the past viewing history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's viewing history into a generation AI and have the generation AI select a delivery method.

[0063] The analysis unit can improve the accuracy of the analysis based on the correlation of data. For example, the analysis unit analyzes the correlation between a user's viewing history and click history. The analysis unit can also analyze the correlation between a user's social media activity and viewing history. Furthermore, the analysis unit can analyze the correlation between a user's geographic location information and viewing history. This improves the accuracy of the analysis by taking the correlation of data into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the correlation of data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0064] The providing unit can customize the content to be provided according to the user's current task. For example, if the user is at work, the providing unit can provide content that can be viewed in a short time. Also, if the user is on a break, the providing unit can provide content that allows the user to relax. Furthermore, if the user is on the move, the providing unit can provide content optimized for mobile devices. In this way, by customizing the content to be provided according to the current task, it is possible to provide optimal content for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's current task into a generating AI and have the generating AI customize the content to be provided.

[0065] The analysis unit can perform analysis based on the market value of the data. For example, the analysis unit can focus analysis on data with high market value. It can also perform complementary analysis on data with low market value. Furthermore, it can adjust the focus of the analysis taking into account fluctuations in market value. In this way, by taking market value into consideration, analysis that is beneficial to the business can be performed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market value data into a generation AI and have the generation AI perform the analysis.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The collection unit collects data about the user's interests and preferences. For example, the collection unit collects information such as videos the user has watched, ads they have clicked, social media activity, search history, survey results, and feedback data. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the interests and preferences of the group. For example, the analysis unit analyzes the data using statistical analysis, machine learning algorithms, and generative AI, and identifies the interests and preferences of the group through the analysis of questionnaire surveys and behavioral data. Step 3: The generation unit generates promotional content for the company based on the hobbies and preferences identified by the analysis unit. For example, the generation unit can use a generation AI to generate promotional content, such as an anime-style youth drama advertisement, or change the design and content of the promotional content. Step 4: The providing unit provides the content generated by the generating unit. For example, the providing unit provides the generated content to a user and evaluates its effectiveness. The providing unit can also provide the generated content by email distribution, posting on a website, or other methods.

[0068] (Example 2) An advertisement generation system according to an embodiment of the present invention is a system for adapting corporate promotional content to the hobbies and preferences of a group. The advertisement generation system collects and analyzes data related to users' hobbies and preferences, and generates and provides promotional content based on the hobbies and preferences of the group. For example, the advertisement generation system generates and presents an anime-style teen drama-style advertisement to users who like anime and youth-related content. In this way, the advertisement is transformed into content that is more easily accepted by the group, thereby maximizing the effectiveness of promotional content. This allows the advertisement generation system to adapt corporate promotional content to the hobbies and preferences of the group, thereby maximizing the effectiveness of promotional content. For example, presenting an anime-style teen drama-style advertisement to users who like anime and youth-related content can increase the viewing rate and click-through rate of the advertisement.

[0069] An advertisement generation system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a provision unit. The collection unit collects data related to a user's interests and preferences. For example, the collection unit collects data such as videos watched by the user and advertisements clicked by the user. The collection unit can also collect data such as the user's social media activities and search history. The collection unit can also collect user survey results and feedback data. The analysis unit analyzes the data collected by the collection unit to identify the interests and preferences of a group. For example, the analysis unit analyzes the collected data using statistical analysis or a machine learning algorithm. The analysis unit can also use a generation AI to identify the interests and preferences of a group based on the collected data. The analysis unit can also identify the interests and preferences of a group through analysis of a questionnaire survey or behavioral data. The generation unit generates corporate promotional content based on the interests and preferences identified by the analysis unit. For example, the generation unit generates promotional content based on the identified interests and preferences using a generation AI. The generation unit can also generate an anime-style teen drama-style advertisement. Furthermore, the generation unit can change the design and content of the public relations content based on the identified hobbies and preferences. The provision unit provides the content generated by the generation unit. For example, the provision unit provides the generated content to a user. The provision unit can also evaluate the effectiveness of the generated content. Furthermore, the provision unit can provide the generated content by methods such as email distribution or posting on a website. In this way, the advertisement generation system according to the embodiment can generate advertisements for a group based on the hobbies and preferences of users, maximizing the effectiveness of public relations.

[0070] The collection unit can collect data on videos viewed by users or advertisements clicked by users. The collection unit, for example, collects data on videos viewed by users. For example, the collection unit collects viewing histories of streaming videos and downloaded videos. The collection unit can also collect data on advertisements clicked by users. For example, the collection unit collects click histories of banner ads and text ads. Furthermore, the collection unit can collect data for identifying hobbies and preferences based on the user's viewing history and click history. In this way, by collecting the user's viewing history and click history, the hobbies and preferences can be accurately understood. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input viewing history and click history data into a generation AI and cause the generation AI to identify the hobbies and preferences.

[0071] The analysis unit can identify the hobbies and preferences of a group based on the collected data. The analysis unit, for example, identifies the hobbies and preferences of a group based on the collected data. For example, the analysis unit analyzes the collected data using statistical analysis. The analysis unit can also identify the hobbies and preferences of a group based on the collected data using a machine learning algorithm. Furthermore, the analysis unit can identify the hobbies and preferences of a group through analysis of questionnaire surveys and behavioral data. By identifying the hobbies and preferences of a group, targeted advertisements can be generated. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to identify the hobbies and preferences of a group.

[0072] The generation unit can arrange the company's public relations content based on the identified hobbies and preferences. The generation unit arranges the company's public relations content based on, for example, the identified hobbies and preferences. For example, the generation unit uses a generation AI to generate public relations content based on the identified hobbies and preferences. The generation unit can also generate anime-style youth drama-style advertisements. Furthermore, the generation unit can change the design and content of the public relations content based on the identified hobbies and preferences. This increases the effectiveness of the advertisement by generating content tailored to the hobbies and preferences. Some or all of the above-mentioned processing in the generation unit may be performed using, or without, the generation AI. For example, the generation unit can input data on the identified hobbies and preferences into the generation AI and cause the generation AI to generate the public relations content.

[0073] The providing unit can provide the generated content to a user. The providing unit, for example, provides the generated content to a user. For example, the providing unit provides the generated content by email distribution, posting on a website, or other methods. The providing unit can also evaluate the effectiveness of the generated content. For example, the providing unit evaluates the view rate and click rate of the generated content. Furthermore, the providing unit can collect feedback on the generated content and reflect it in the next content generation. In this way, by providing the generated content to a user, the view rate and click rate of advertisements can be improved. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data for evaluating the effectiveness of the generated content to a generation AI and cause the generation AI to perform the evaluation.

[0074] The generation unit can generate anime-style advertisements. The generation unit generates, for example, anime-style advertisements. For example, the generation unit uses a generation AI to generate anime-style teen drama-style advertisements. The generation unit can also generate advertisements using anime-style character designs and storytelling. Furthermore, the generation unit can change the design and content of the anime-style advertisements. In this way, by generating anime-style teen drama-style advertisements, it is possible to provide effective advertisements to groups with specific hobbies and preferences. Some or all of the above-described processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit can input design data for the anime-style advertisement into the generation AI and cause the generation AI to generate the advertisement.

[0075] The providing unit can evaluate the effectiveness of the generated content. The providing unit, for example, evaluates the effectiveness of the generated content. For example, the providing unit evaluates the viewer rate and click rate of the generated content. The providing unit can also evaluate the conversion rate and engagement rate of the generated content. Furthermore, the providing unit can collect feedback on the generated content and reflect it in the next content generation. In this way, by evaluating the effectiveness of the generated content, it is possible to find areas for improvement in advertising. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data for evaluating the effectiveness of the generated content into a generation AI and have the generation AI perform the evaluation.

[0076] The collection unit can estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit estimates a user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, the collection unit collects viewing histories and click histories when the user is relaxed. The collection unit can also refrain from collecting data when the user is feeling stressed and collect it later. Furthermore, the collection unit can collect data in real time when the user is excited and immediately analyze the data. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0077] The collection unit can analyze a user's past viewing history and click history and select an appropriate data collection method. The collection unit, for example, analyzes a user's past viewing history and click history and selects an appropriate data collection method. For example, the collection unit prioritizes collecting videos of genres frequently watched by the user. The collection unit can also analyze trends in advertisements clicked by the user and collect related data. Furthermore, the collection unit can analyze a user's viewing time period and concentrate data collection on that time period. In this way, the optimal data collection method can be selected by analyzing the past viewing history and click history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on a user's viewing history and click history into a generation AI and have the generation AI select an optimal data collection method.

[0078] The collection unit can filter data based on the user's current areas of interest and activity status when collecting data. For example, the collection unit can filter data based on the user's current areas of interest and activity status when collecting data. For example, the collection unit can collect related data based on the genre of videos the user is currently watching. The collection unit can also analyze the activity status of online communities in which the user participates and collect related data. Furthermore, the collection unit can filter data based on keywords recently searched by the user. In this way, highly relevant data can be collected by filtering data based on the user's current areas of interest and activity status. Some or all of the above-described processing in the collection unit can be performed using, or without, AI. For example, the collection unit can input data on the user's areas of interest and activity status to a generation AI and have the generation AI perform data filtering.

[0079] The collection unit can select an appropriate collection means according to the user's input method when collecting data. For example, the collection unit selects an appropriate collection means according to the user's input method when collecting data. For example, if the user uses voice input, the collection unit can prioritize collecting voice data. Also, if the user uses text input, the collection unit can prioritize collecting text data. Furthermore, if the user uploads an image, the collection unit can prioritize collecting image data. This allows efficient data collection by selecting the optimal collection means according to the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the user's input method to the generation AI and cause the generation AI to select the collection means.

[0080] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, the collection unit prioritizes collecting important data when the user is relaxed. The collection unit can also prioritize collecting light data when the user is feeling stressed. Furthermore, the collection unit can collect important data in real time when the user is excited. This allows important data to be collected preferentially by determining the priority of data according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using an AI, for example, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0081] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can also prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can also prioritize collecting data around the user's home. In this way, highly relevant data can be prioritized by taking into account the geographical location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input data of the user's geographical location information to the generation AI and cause the generation AI to collect data.

[0082] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can collect data based on content shared by the user on social media. The collection unit can also analyze the activities of accounts the user follows on social media and collect related data. Furthermore, the collection unit can analyze the activities of groups the user participates in on social media and collect related data. This allows for efficient collection of related data by analyzing social media activities. Some or all of the above-described processing in the collection unit can be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's social media activities into a generation AI and cause the generation AI to collect data.

[0083] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit adjusts the type of data to be collected based on feedback provided by the user in the past. The collection unit can also prioritize the use of data collection methods that the user previously preferred. Furthermore, the collection unit can eliminate data collection methods that the user previously avoided and use other methods. In this way, the collection method can be optimized by reflecting the past feedback. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's feedback data into a generation AI and cause the generation AI to customize the collection method.

[0084] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. The analysis unit can also perform a simplified analysis when the user is stressed. Furthermore, the analysis unit can perform a quick analysis in real time when the user is excited. This allows for more appropriate analysis by adjusting the analysis criteria according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the analysis unit can be performed using an AI, for example, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis criteria.

[0085] The analysis unit can improve the accuracy of the analysis based on the interrelationships between data during analysis. For example, the analysis unit improves the accuracy of the analysis by taking into account the interrelationships between data during analysis. For example, the analysis unit analyzes the interrelationships between a user's viewing history and click history. The analysis unit can also analyze the interrelationships between a user's social media activity and viewing history. Furthermore, the analysis unit can analyze the interrelationships between a user's geographic location information and viewing history. This improves the accuracy of the analysis by taking into account the interrelationships between data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the interrelationships between data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0086] The analysis unit can perform the analysis while taking into account the attribute information of the data submitter. For example, the analysis unit performs the analysis while taking into account the attribute information of the data submitter. For example, the analysis unit analyzes the data based on the user's age and gender. The analysis unit can also analyze the data based on the user's occupation and hobbies. Furthermore, the analysis unit can analyze the data based on the user's place of residence and education level. This allows for more accurate analysis by taking into account the attribute information of the submitter. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of the submitter's attribute information into the generation AI and have the generation AI perform the analysis.

[0087] The analysis unit can weight the analysis based on the frequency of data submission during analysis. The analysis unit, for example, weights the analysis based on the frequency of data submission during analysis. For example, the analysis unit may assign a higher weight to frequently submitted data during analysis. The analysis unit may also assign a lower weight to infrequently submitted data during analysis. Furthermore, the analysis unit can adjust the weighting of the analysis taking into account fluctuations in submission frequency. Thus, weighting based on submission frequency improves the accuracy of the analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data on the submission frequency to a generation AI and have the generation AI perform the weighting.

[0088] The analysis unit can estimate the user's emotions and adjust the display order of the analysis results based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the display order of the analysis results based on the estimated user emotions. For example, when the user is relaxed, the analysis unit can prioritize displaying detailed analysis results. Furthermore, when the user is stressed, the analysis unit can prioritize displaying simplified analysis results. Furthermore, when the user is excited, the analysis unit can prioritize displaying important analysis results in real time. This allows optimal information to be provided to the user by adjusting the display order of the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display order.

[0089] The analysis unit can perform the analysis taking into account the geographical distribution of the data. For example, the analysis unit performs the analysis taking into account the geographical distribution of the data. For example, the analysis unit analyzes the data based on the user's place of residence. The analysis unit can also analyze the data based on the user's travel destinations. Furthermore, the analysis unit can analyze the data based on the user's range of activities. In this way, by taking the geographical distribution into consideration, analysis can be performed according to regional characteristics. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the geographical distribution data into the generation AI and have the generation AI perform the analysis.

[0090] The analysis unit can improve the accuracy of the analysis based on literature related to the data during analysis. For example, the analysis unit improves the accuracy of the analysis by referring to literature related to the data during analysis. For example, the analysis unit analyzes the data by referring to related academic papers. The analysis unit can also analyze the data by referring to related industry reports. Furthermore, the analysis unit can analyze the data by referring to related patent documents. As a result, the accuracy of the analysis is improved by referring to related literature. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input data from related literature into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0091] The analysis unit can perform analysis based on the market value of the data during analysis. For example, the analysis unit performs analysis based on the market value of the data during analysis. For example, the analysis unit focuses analysis on data with high market value. The analysis unit can also perform complementary analysis on data with low market value. Furthermore, the analysis unit can adjust the focus of the analysis in consideration of fluctuations in market value. In this way, analysis that is beneficial to business can be performed by taking market value into consideration. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input market value data into a generation AI and have the generation AI perform the analysis.

[0092] The generation unit can estimate the user's emotions and adjust the content generation method based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the content generation method based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate content that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate content that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can also generate content that adds visually stimulating effects. This allows for more effective content to be generated by adjusting the content generation method according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or without the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content generation method.

[0093] The generation unit can adjust the level of detail of the generation based on the identified hobbies and preferences when generating content. The generation unit, for example, adjusts the level of detail of the generation based on the identified hobbies and preferences when generating content. For example, if the user likes anime, the generation unit can generate detailed anime-style content. Also, if the user likes teenage stuff, the generation unit can generate detailed teen drama-style content. Furthermore, if the user likes science fiction, the generation unit can generate detailed science fiction-style content. In this way, by adjusting the level of detail of the generation based on the hobbies and preferences, it is possible to generate content that is attractive to the user. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the identified hobbies and preferences into the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0094] The generation unit can apply different generation algorithms according to different interests and preferences when generating content. For example, the generation unit applies different generation algorithms according to different interests and preferences when generating content. For example, the generation unit applies an anime-style generation algorithm to a user who likes anime. The generation unit can also apply a youth drama-style generation algorithm to a user who likes teenage stuff. The generation unit can also apply a science fiction-style generation algorithm to a user who likes science fiction. In this way, personalized content can be generated by applying generation algorithms according to different interests and preferences. Some or all of the above-described processing in the generation unit may be performed using, or without, a generation AI. For example, the generation unit can input data of different interests and preferences into the generation AI and cause the generation AI to apply the generation algorithm.

[0095] The generation unit can improve the accuracy of generation by referring to the user's past generation results when generating content. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results when generating content. For example, the generation unit generates content based on generation results that the user has previously preferred. The generation unit can also eliminate generation results that the user has previously avoided and use other methods. Furthermore, the generation unit can adjust the generation algorithm based on the user's past feedback. In this way, the accuracy of generation is improved by referring to past generation results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data of the user's past generation results into the generation AI and cause the generation AI to improve the accuracy of generation.

[0096] The generation unit can estimate the user's emotion and adjust the length of the generated content based on the estimated user emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the generated content based on the estimated user emotion. For example, the generation unit generates longer content when the user is relaxed. The generation unit can also generate shorter content when the user is in a hurry. Furthermore, the generation unit can generate short content with visually stimulating effects when the user is excited. By adjusting the length of the content according to the user's emotion, optimal content can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI. For example, the generation unit can input user emotion data into the generation AI and cause the generation AI to adjust the length of the content.

[0097] The generation unit can determine the generation priority based on the submission time of the identified hobbies and preferences when generating content. The generation unit, for example, determines the generation priority based on the submission time of the identified hobbies and preferences when generating content. For example, the generation unit prioritizes generating content in a genre in which the user has recently become interested. The generation unit can also prioritize generating content in a genre in which the user has been interested for a long time. Furthermore, the generation unit can quickly generate content in a genre in which the user is temporarily interested. This allows timely content to be provided by determining the generation priority based on the submission time. Some or all of the above-described processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input data on the submission time of the identified hobbies and preferences into the generation AI and have the generation AI determine the generation priority.

[0098] The generation unit can adjust the generation order based on the identified relevance of the hobbies and preferences when generating content. For example, the generation unit can first generate content in the genre in which the user is most interested. Furthermore, if the user is interested in multiple related genres, the generation unit can also generate content in descending order of relevance. Furthermore, the generation unit can prioritize generating content in a genre in which the user has recently become interested. By adjusting the generation order based on relevance, content can be provided in an optimal order for the user. Some or all of the above-described processing in the generation unit can be performed, for example, using a generation AI, or can be performed without using a generation AI. For example, the generation unit can input data on the identified relevance of the hobbies and preferences into the generation AI and cause the generation AI to adjust the generation order.

[0099] The generation unit can appropriately use technical terminology in the content generation process according to the user's level of expertise. For example, the generation unit can adjust the use of technical terminology in the content generation process according to the user's level of expertise. For example, if the user has specialized knowledge, the generation unit generates content that uses a lot of technical terminology. Furthermore, if the user is a beginner, the generation unit can generate easy-to-understand content that avoids technical terminology. Furthermore, the generation unit can adjust the frequency of use of technical terminology according to the user's level of knowledge. This allows for the provision of content that is easy for the user to understand by adjusting the use of technical terminology according to the level of expertise. Some or all of the above-described processing in the generation unit can be performed using, or without, a generation AI. For example, the generation unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terminology.

[0100] The providing unit can estimate the user's emotions and adjust the content provision method based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and adjusts the content provision method based on the estimated user emotions. For example, if the user is relaxed, the providing unit can provide content at a leisurely pace. Furthermore, if the user is in a hurry, the providing unit can provide content quickly. Furthermore, if the user is excited, the providing unit can provide content in a visually stimulating manner. This allows for more effective content provision by adjusting the content provision method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content provision method.

[0101] The providing unit can select an appropriate delivery method by referring to the user's past viewing history when providing content. For example, the providing unit can select an appropriate delivery method by referring to the user's past viewing history when providing content. For example, the providing unit can prioritize delivery methods that the user has previously preferred. The providing unit can also eliminate delivery methods that the user has previously avoided and use other methods. Furthermore, the providing unit can select an optimal delivery method based on the user's viewing history. In this way, the optimal delivery method can be selected by referring to the past viewing history. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the user's viewing history into a generation AI and have the generation AI select a delivery method.

[0102] The providing unit can customize the content to be provided according to the user's current task when providing content. For example, the providing unit customizes the content to be provided according to the user's current task when providing content. For example, when the user is at work, the providing unit provides content that can be viewed in a short time. Furthermore, when the user is on a break, the providing unit can also provide content that allows the user to relax. Furthermore, when the user is on the move, the providing unit can also provide content optimized for mobile devices. In this way, by customizing the content to be provided according to the current task, it is possible to provide optimal content for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input data on the user's current task into a generating AI and cause the generating AI to customize the content to be provided.

[0103] The providing unit can improve the delivery method by reflecting user feedback when providing content. For example, the providing unit improves the delivery method by reflecting user feedback when providing content. For example, if the user provides positive feedback on the delivery method, the providing unit continues that method. Also, if the user provides negative feedback on the delivery method, the providing unit can try another method. Furthermore, the providing unit can customize the delivery method based on user feedback. In this way, the delivery method can be continuously improved by reflecting feedback. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to improve the delivery method.

[0104] The providing unit can estimate the user's emotions and determine the priority of content provision based on the estimated user emotions. The providing unit, for example, estimates the user's emotions and determines the priority of content provision based on the estimated user emotions. For example, the providing unit can prioritize providing important content when the user is relaxed. The providing unit can also prioritize providing light content when the user is stressed. Furthermore, the providing unit can provide important content in real time when the user is excited. This allows important content to be prioritized by determining the priority of content provision based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the providing unit may be performed using an AI, for example, or without an AI. For example, the providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of content provision.

[0105] The providing unit can select an appropriate delivery method based on the user's geographical location information when providing content. For example, the providing unit selects the optimal delivery method by taking the user's geographical location information into consideration when providing content. For example, when the user is in a specific area, the providing unit can provide content related to that area. Furthermore, when the user is traveling, the providing unit can also provide content related to the travel destination. Furthermore, when the user is at home, the providing unit can also provide content around the user's home. In this way, the optimal delivery method can be selected by taking the geographical location information into consideration. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to select the delivery method.

[0106] The providing unit can customize the content to be provided by analyzing the user's social media activity when providing content. For example, the providing unit customizes the content to be provided by analyzing the user's social media activity when providing content. For example, the providing unit customizes the content to be provided based on content shared by the user on social media. The providing unit can also analyze the activity of accounts the user follows on social media and provide related content. Furthermore, the providing unit can analyze the activity of groups the user participates in on social media and provide related content. In this way, highly relevant content can be provided by analyzing social media activity. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's social media activity into a generation AI and cause the generation AI to customize the content to be provided.

[0107] The providing unit can customize the delivery method by reflecting the user's past feedback when providing content. For example, the providing unit customizes the delivery method by reflecting the user's past feedback when providing content. For example, the providing unit adjusts the type of content to be provided based on feedback provided by the user in the past. The providing unit can also preferentially use delivery methods that the user has previously preferred. Furthermore, the providing unit can eliminate delivery methods that the user has avoided in the past and use other methods. In this way, the delivery method can be optimized by reflecting past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input user feedback data into a generation AI and cause the generation AI to customize the delivery method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision unit, 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 data on users' hobbies and preferences using the camera 42 and microphone 38B of the smart device 14 and analyzes the data using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the hobbies and preferences of a group. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates promotional content based on the identified hobbies and preferences. The provision unit is realized, for example, by the control unit 46A of the smart device 14 and provides the generated content to the user. The collection unit can, for example, estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 data related to users' hobbies and preferences using the camera 42 and microphone 238 of the smart glasses 214 and analyzes the data using the control unit 46A. The analysis unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the hobbies and preferences of a group. The generation unit, for example, is realized by the identification processing unit 290 of the data processing device 12 and generates promotional content based on the identified hobbies and preferences. The provision unit, for example, is realized by the control unit 46A of the smart glasses 214 and provides the generated content to the user. The collection unit, for example, can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 data on users' hobbies and preferences using the camera 42 and microphone 238 of the headset-type terminal 314 and analyzes the data using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the hobbies and preferences of a group. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates promotional content based on the identified hobbies and preferences. The provision unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and provides the generated content to the user. The collection unit can, for example, estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and provision 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 data on users' hobbies and preferences using the camera 42 and microphone 238 of the robot 414 and analyzes the data using the control unit 46A. The analysis unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and analyzes the collected data to identify the hobbies and preferences of a group. The generation unit is realized, for example, by the identification processing unit 290 of the data processing device 12 and generates promotional content based on the identified hobbies and preferences. The provision unit is realized, for example, by the control unit 46A of the robot 414 and provides the generated content to the user. The collection unit can, for example, estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated user emotions. For example, the analysis unit can perform a detailed analysis when the user is relaxed. Alternatively, the analysis unit can perform a simplified analysis when the user is stressed. Furthermore, the analysis unit can perform a quick analysis in real time when the user is excited. This allows for more appropriate analysis by adjusting the accuracy of the analysis according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis criteria.

[0110] The collection unit can collect data taking into account the user's geographical location information. For example, if the user is in a specific area, the collection unit can prioritize collecting data related to that area. Also, if the user is traveling, the collection unit can collect data related to the travel destination. Furthermore, if the user is at home, the collection unit can prioritize collecting data around the home. In this way, by taking the geographical location information into consideration, highly relevant data can be collected. Some or all of the above-mentioned processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data into the generation AI and cause the generation AI to collect data.

[0111] The generation unit can estimate the user's emotions and adjust the design of the content based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate content using calm colors and designs. If the user is stressed, the generation unit can generate content using a simple and intuitive design. Furthermore, if the user is excited, the generation unit can generate content using a visually stimulating design. This allows for more effective content to be provided by adjusting the content design according to the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the design.

[0112] The providing unit can select an appropriate delivery method by referring to the user's past viewing history. For example, the providing unit can prioritize delivery methods that the user has previously preferred. It can also eliminate delivery methods that the user has previously avoided and use other methods. Furthermore, the providing unit can select an optimal delivery method based on the user's viewing history. In this way, the optimal delivery method can be selected by referring to the past viewing history. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input data on the user's viewing history into a generation AI and have the generation AI select a delivery method.

[0113] The collection unit can estimate a user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit collects viewing history and click history when the user is relaxed. Furthermore, when the user is feeling stressed, the collection unit can refrain from collecting data and collect it later. Furthermore, when the user is excited, data can be collected in real time and immediately analyzed. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, an AI, or without an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0114] The analysis unit can improve the accuracy of the analysis based on the correlation of data. For example, the analysis unit analyzes the correlation between a user's viewing history and click history. The analysis unit can also analyze the correlation between a user's social media activity and viewing history. Furthermore, the analysis unit can analyze the correlation between a user's geographic location information and viewing history. This improves the accuracy of the analysis by taking the correlation of data into consideration. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the correlation of data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0115] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate longer content. If the user is in a hurry, the generation unit can generate shorter content. Furthermore, if the user is excited, the generation unit can generate short content with visually stimulating effects. By adjusting the length of the content according to the user's emotions, optimal content can be provided to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the content.

[0116] The providing unit can customize the content to be provided according to the user's current task. For example, if the user is at work, the providing unit can provide content that can be viewed in a short time. Also, if the user is on a break, the providing unit can provide content that allows the user to relax. Furthermore, if the user is on the move, the providing unit can provide content optimized for mobile devices. In this way, by customizing the content to be provided according to the current task, it is possible to provide optimal content for the user. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input data on the user's current task into a generating AI and have the generating AI customize the content to be provided.

[0117] The analysis unit can perform analysis based on the market value of the data. For example, the analysis unit can focus analysis on data with high market value. It can also perform complementary analysis on data with low market value. Furthermore, it can adjust the focus of the analysis taking into account fluctuations in market value. In this way, by taking market value into consideration, analysis that is beneficial to the business can be performed. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market value data into a generation AI and have the generation AI perform the analysis.

[0118] The providing unit can estimate the user's emotions and adjust the content provision method based on the estimated user's emotions. For example, if the user is relaxed, the providing unit can provide content at a leisurely pace. If the user is in a hurry, the providing unit can provide content quickly. Furthermore, if the user is excited, the providing unit can provide content in a visually stimulating manner. This allows for more effective content provision by adjusting the content provision method according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the content provision method.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects data about the user's interests and preferences. For example, the collection unit collects information such as videos the user has watched, ads they have clicked, social media activity, search history, survey results, and feedback data. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the interests and preferences of the group. For example, the analysis unit analyzes the data using statistical analysis, machine learning algorithms, and generative AI, and identifies the interests and preferences of the group through the analysis of questionnaire surveys and behavioral data. Step 3: The generation unit generates promotional content for the company based on the hobbies and preferences identified by the analysis unit. For example, the generation unit can use a generation AI to generate promotional content, such as an anime-style youth drama advertisement, or change the design and content of the promotional content. Step 4: The providing unit provides the content generated by the generating unit. For example, the providing unit provides the generated content to a user and evaluates its effectiveness. The providing unit can also provide the generated content by email distribution, posting on a website, or other methods.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 on the user's hobbies and preferences; an analysis unit that analyzes the data collected by the collection unit and identifies the hobbies and preferences of a group; a generation unit that generates public relations content for a company based on the hobbies and preferences identified by the analysis unit; a providing unit that provides the content generated by the generating unit. A system characterized by:

2. The collecting unit Collect data on the videos you watch or the ads you click 2. The system of claim 1.

3. The analysis unit Identifying the tastes and preferences of a group based on collected data 2. The system of claim 1.

4. The generation unit Tailor corporate promotional content based on identified interests and preferences 2. The system of claim 1.

5. The providing unit Providing generated content to users 2. The system of claim 1.

6. The generation unit Generate anime-style ads 2. The system of claim 1.

7. The providing unit Evaluate the effectiveness of the content generated 2. The system of claim 1.

8. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

Images