System

Abstract

An object of a system according to an embodiment is to integrate data from different devices and extract a behavior pattern in a cross device.SOLUTION: A system according to an embodiment includes a behavior pattern extraction unit. The behavior pattern extraction unit integrates data from different devices and extracts a behavior pattern in a cross device.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 do not adequately integrate data from different devices to extract cross-device behavioral patterns, and there is room for improvement.

[0005] The system according to the embodiment aims to integrate data from different devices and extract cross-device behavioral patterns. [Means for solving the problem]

[0006] The system according to the embodiment includes a behavioral pattern extraction unit that integrates data from different devices and extracts cross-device behavioral patterns. [Effects of the Invention]

[0007] The system according to the embodiment can integrate data from different devices and extract cross-device behavioral patterns. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) The ad delivery optimization system according to an embodiment of the present invention uses AI to analyze web campaign data and propose optimal ad delivery strategies and content improvements based on customer behavior patterns and responses. This enables the ad delivery optimization system to effectively manage campaigns to achieve high ROI.

[0029] An advertisement delivery optimization system according to an embodiment includes a data analysis unit, a behavioral pattern extraction unit, an advertisement delivery strategy generation unit, and a content improvement proposal unit. The data analysis unit analyzes data from web campaigns. For example, the data analysis unit collects data such as which advertisements users clicked, which pages they visited, and which content they showed interest in. The data analysis unit can also analyze user responses in real time. For example, the data analysis unit monitors user click rates and conversion rates in real time. The behavioral pattern extraction unit extracts customer behavioral patterns based on the data analyzed by the data analysis unit. For example, the behavioral pattern extraction unit analyzes user behavioral data and analyzes which advertisements and content are effective. The behavioral pattern extraction unit can also subdivide user behavioral patterns by time of day or day of the week. For example, the behavioral pattern extraction unit analyzes differences in advertisement click rates between daytime and nighttime. The advertisement delivery strategy generation unit generates an optimal advertisement delivery strategy based on the behavioral patterns extracted by the behavioral pattern extraction unit. For example, the advertisement delivery strategy generation unit proposes a strategy for delivering specific advertisements during specific time periods. The ad delivery strategy generation unit can also propose a strategy for delivering specific ads to specific user segments. For example, the ad delivery strategy generation unit selects ads based on user interests. The content improvement proposal unit proposes content improvements based on the behavioral patterns extracted by the behavioral pattern extraction unit. For example, the content improvement proposal unit analyzes how specific content is received by users and proposes improvements. The content improvement proposal unit can also propose content improvements based on user feedback. For example, the content improvement proposal unit analyzes user comments and ratings and proposes changes to content. As a result, the ad delivery optimization system according to the embodiment can analyze web campaign data and propose optimal ad delivery strategies and content improvements based on customer behavioral patterns and responses. For example, the ad delivery optimization system generates optimization measures aimed at increasing click-through rates and conversion rates, and supports effective campaign management to achieve high ROI.

[0030] The behavioral pattern extraction unit can integrate data from different devices and extract cross-device behavioral patterns. The behavioral pattern extraction unit integrates behavioral data from, for example, smartphones, tablets, and PCs and analyzes cross-device behavioral patterns. For example, it analyzes how the same user behaves on different devices. The behavioral pattern extraction unit also clarifies methods for integrating data across different devices and extracting behavioral patterns. For example, it centrally manages data across devices and comprehensively analyzes user behavior patterns. This makes it possible to extract cross-device behavioral patterns by integrating data from different devices.

[0031] The behavioral pattern extraction unit can analyze social media posts and comments in addition to user behavioral data to extract more comprehensive behavioral patterns. The behavioral pattern extraction unit, for example, collects social media posting data and analyzes user behavioral patterns. For example, it analyzes what types of posts users make and what types of comments they leave, and extracts behavioral patterns. The behavioral pattern extraction unit also analyzes social media posts and comments using text analysis technology. For example, it performs sentiment analysis to analyze the association between user emotions and behavior. In this way, by analyzing social media posts and comments, more comprehensive behavioral patterns can be extracted.

[0032] The behavioral pattern extraction unit subdivides the user's behavioral patterns by time period and day of the week, allowing for detailed analysis of differences in behavior during specific time periods. The behavioral pattern extraction unit, for example, classifies the user's behavioral data by time period and analyzes the behavioral patterns during specific time periods. For example, it analyzes the difference in ad click rates between daytime and nighttime. The behavioral pattern extraction unit also subdivides the behavioral patterns by day of the week and analyzes differences in behavior between weekdays and weekends. For example, it compares the user's behavioral patterns during weekday daytime and weekend nighttime. By subdividing the behavioral patterns by time period and day of the week, it is possible to perform a detailed analysis of differences in behavior during specific time periods.

[0033] The behavioral pattern extraction unit can integrate data from different devices (smartphones, tablets, and PCs) and extract cross-device behavioral patterns. The behavioral pattern extraction unit integrates behavioral data from, for example, smartphones, tablets, and PCs and analyzes cross-device behavioral patterns. For example, it analyzes how the same user behaves on different devices. The behavioral pattern extraction unit also clarifies methods for integrating data across different devices and extracting behavioral patterns. For example, it centrally manages data across devices and comprehensively analyzes user behavior patterns. This makes it possible to extract cross-device behavioral patterns by integrating data from different devices.

[0034] The behavioral pattern extraction unit can apply the results of behavioral pattern analysis to different industries and applications, and propose optimal strategies in each field. For example, the behavioral pattern extraction unit analyzes behavioral patterns in the e-commerce field and proposes optimal advertising distribution strategies. For example, it formulates advertising strategies based on user reactions to specific product categories. The behavioral pattern extraction unit also analyzes behavioral patterns in the education field and proposes optimal content improvements. For example, it proposes improvements based on user reactions to specific educational content. In this way, by applying the results of behavioral pattern analysis to different industries and applications, it is possible to propose optimal strategies in each field.

[0035] The advertisement delivery strategy generation unit can analyze a user's past purchase history and browsing history to generate personalized advertisements. The advertisement delivery strategy generation unit, for example, analyzes a user's past purchase history to generate personalized advertisements. For example, advertisements related to products previously purchased are delivered. The advertisement delivery strategy generation unit also analyzes a user's browsing history to generate advertisements based on the user's interests. For example, advertisements related to pages previously viewed are delivered. In this way, personalized advertisements can be generated by analyzing a user's past purchase history and browsing history.

[0036] The ad delivery strategy generation unit can analyze the user's lifestyle rhythm and behavioral patterns and propose the optimal ad delivery time. The ad delivery strategy generation unit, for example, analyzes the user's lifestyle rhythm and proposes the optimal ad delivery time. For example, it delivers ads during the time periods when the user is most active. The ad delivery strategy generation unit also analyzes the user's behavioral patterns and optimizes the timing of ad delivery. For example, it delivers ads according to the user's online time periods. In this way, by analyzing the user's lifestyle rhythm and behavioral patterns, it is possible to propose the optimal ad delivery time.

[0037] The ad delivery strategy generation unit can apply the ad delivery strategy to different platforms and propose an optimal strategy for each platform. The ad delivery strategy generation unit proposes an ad delivery strategy for, for example, a social media platform. For example, it delivers effective ads to a specific user demographic. The ad delivery strategy generation unit also proposes an ad delivery strategy for a search engine. For example, it displays optimal ads for specific keywords. Furthermore, the ad delivery strategy generation unit proposes an ad delivery strategy for a video site. For example, it delivers ads related to specific video content. In this way, by applying the ad delivery strategy to different platforms, it is possible to propose an optimal strategy for each platform.

[0038] The advertising delivery strategy generation unit can apply the advertising delivery strategy to different regions and cultural areas and propose a strategy that suits the characteristics of each region. The advertising delivery strategy generation unit, for example, proposes an advertising delivery strategy that takes into account the characteristics of different regions. For example, it delivers advertisements that suit the culture and customs of each region. The advertising delivery strategy generation unit also analyzes consumption behavior for each region and proposes an optimal advertising delivery strategy. For example, it delivers advertisements related to products that are popular in a specific region. In this way, by applying the advertising delivery strategy to different regions and cultural areas, it is possible to propose a strategy that suits the characteristics of each region.

[0039] The content improvement suggestion unit can analyze the user's browsing history and feedback and make personalized improvement suggestions. The content improvement suggestion unit, for example, analyzes the user's browsing history and makes personalized content improvement suggestions. For example, it suggests improvements based on content that has been viewed in the past. The content improvement suggestion unit also analyzes the user's feedback and makes content improvement suggestions. For example, it suggests improvements based on the user's comments and ratings. In this way, by analyzing the user's browsing history and feedback, it is possible to make personalized improvement suggestions.

[0040] The content improvement proposal unit can analyze competitors' content and propose points of differentiation from competitors. The content improvement proposal unit, for example, analyzes competitors' content and proposes points of differentiation. For example, it analyzes competitors' strengths and weaknesses and proposes areas for improvement in its own content. The content improvement proposal unit also compares its own content with competitors' content and clarifies points of differentiation. For example, it proposes unique functions and services that competitors do not offer. In this way, it is possible to propose points of differentiation by analyzing competitors' content.

[0041] The content improvement proposal unit can apply content improvement proposals to different media formats and make improvement proposals that are optimal for each media format. The content improvement proposal unit, for example, makes improvement proposals for text content. For example, it proposes optimizing sentence structure and keywords. The content improvement proposal unit also makes improvement proposals for image content. For example, it proposes improvements to image layout and design. Furthermore, the content improvement proposal unit makes improvement proposals for video content. For example, it proposes improvements to video editing and content. In this way, by applying content improvement proposals to different media formats, it is possible to make improvement proposals that are optimal for each media format.

[0042] The content improvement proposal unit can apply content improvement proposals to different industries and applications, and make optimal improvement proposals in each field. The content improvement proposal unit makes content improvement proposals in the e-commerce field, for example. For example, it proposes optimizing product descriptions and reviews. The content improvement proposal unit also makes content improvement proposals in the education field. For example, it proposes improvements to the structure and content of educational content. Furthermore, the content improvement proposal unit makes content improvement proposals in the healthcare field. For example, it proposes improvements to health information and medical content. In this way, by applying content improvement proposals to different industries and applications, it is possible to make optimal improvement proposals in each field.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The ad delivery optimization system can also obtain users' geographical location information and propose ad delivery strategies tailored to the characteristics of each region. For example, it can deliver ads related to products and services that are popular in a particular region. It can also analyze consumer behavior in each region based on the geographical location information and propose optimal ad delivery times. For example, it can formulate an ad delivery strategy that takes into account differences in consumer behavior between urban and suburban areas. This allows for more effective ad delivery by proposing ad delivery strategies tailored to the characteristics of each region.

[0045] The ad delivery optimization system can also analyze a user's purchase history and propose an ad delivery strategy based on past purchasing behavior. For example, it can deliver ads related to products previously purchased. It can also predict a user's interests based on the purchase history and generate personalized ads. For example, it can deliver information about new products and sales related to a specific category to a user who frequently purchases products in that category. This allows it to propose an ad delivery strategy based on the user's purchase history, thereby achieving a higher conversion rate.

[0046] The ad delivery optimization system can also analyze users' social media posts and comments to extract behavioral patterns on social media. For example, it can analyze the types of posts and comments users make and extract behavioral patterns. It can also predict users' interests based on social media data and generate personalized advertisements. For example, it can deliver advertisements related to a specific topic to users who post frequently on that topic. This allows for more effective ad delivery by proposing ad delivery strategies that utilize social media data.

[0047] The ad delivery optimization system can also analyze a user's lifestyle rhythm and suggest optimal ad delivery times. For example, it can deliver ads during the times when the user is most active. It can also predict a user's behavioral patterns based on their lifestyle rhythm and optimize ad delivery strategies. For example, it can deliver ads during their morning commute or their evening relaxation time. This allows it to suggest ad delivery strategies based on the user's lifestyle rhythm, thereby achieving higher engagement.

[0048] The ad delivery optimization system can further analyze user feedback and make content improvement suggestions based on the feedback. For example, it can suggest improvements based on user comments and ratings. It can also predict user needs and expectations based on the feedback and make personalized content improvement suggestions. For example, for content that has received a lot of specific feedback, it can make improvement suggestions that reflect that feedback. In this way, content improvement suggestions based on user feedback can be made, thereby achieving higher satisfaction.

[0049] The ad delivery optimization system can also analyze a user's past browsing history and propose an ad delivery strategy based on that browsing history. For example, it can deliver ads related to pages previously viewed. It can also predict a user's interests based on the browsing history and generate personalized ads. For example, it can deliver new product and sale information related to a specific category to a user who frequently views pages in that category. This allows it to propose an ad delivery strategy based on the user's browsing history, thereby achieving a higher conversion rate.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The behavioral pattern extraction unit extracts customer behavioral patterns based on the data analyzed by the data analysis unit. For example, it analyzes user behavioral data and analyzes what types of advertisements and content are effective. The behavioral pattern extraction unit can also subdivide user behavioral patterns by time of day or day of the week. For example, it analyzes the difference in ad click rates between daytime and nighttime. Step 2: The behavioral pattern extraction unit integrates data from different devices and extracts cross-device behavioral patterns, allowing us to understand consistent behavioral patterns even when a user uses multiple devices.

[0052] (Example 2) The ad delivery optimization system according to an embodiment of the present invention uses AI to analyze web campaign data and propose optimal ad delivery strategies and content improvements based on customer behavior patterns and responses. This enables the ad delivery optimization system to effectively manage campaigns to achieve high ROI.

[0053] An advertisement delivery optimization system according to an embodiment includes a data analysis unit, a behavioral pattern extraction unit, an advertisement delivery strategy generation unit, and a content improvement proposal unit. The data analysis unit analyzes data from web campaigns. For example, the data analysis unit collects data such as which advertisements users clicked, which pages they visited, and which content they showed interest in. The data analysis unit can also analyze user responses in real time. For example, the data analysis unit monitors user click rates and conversion rates in real time. The behavioral pattern extraction unit extracts customer behavioral patterns based on the data analyzed by the data analysis unit. For example, the behavioral pattern extraction unit analyzes user behavioral data and analyzes which advertisements and content are effective. The behavioral pattern extraction unit can also subdivide user behavioral patterns by time of day or day of the week. For example, the behavioral pattern extraction unit analyzes differences in advertisement click rates between daytime and nighttime. The advertisement delivery strategy generation unit generates an optimal advertisement delivery strategy based on the behavioral patterns extracted by the behavioral pattern extraction unit. For example, the advertisement delivery strategy generation unit proposes a strategy for delivering specific advertisements during specific time periods. The ad delivery strategy generation unit can also propose a strategy for delivering specific ads to specific user segments. For example, the ad delivery strategy generation unit selects ads based on user interests. The content improvement proposal unit proposes content improvements based on the behavioral patterns extracted by the behavioral pattern extraction unit. For example, the content improvement proposal unit analyzes how specific content is received by users and proposes improvements. The content improvement proposal unit can also propose content improvements based on user feedback. For example, the content improvement proposal unit analyzes user comments and ratings and proposes changes to content. As a result, the ad delivery optimization system according to the embodiment can analyze web campaign data and propose optimal ad delivery strategies and content improvements based on customer behavioral patterns and responses. For example, the ad delivery optimization system generates optimization measures aimed at increasing click-through rates and conversion rates, and supports effective campaign management to achieve high ROI.

[0054] The behavioral pattern extraction unit can integrate data from different devices and extract cross-device behavioral patterns. The behavioral pattern extraction unit integrates behavioral data from, for example, smartphones, tablets, and PCs and analyzes cross-device behavioral patterns. For example, it analyzes how the same user behaves on different devices. The behavioral pattern extraction unit also clarifies methods for integrating data across different devices and extracting behavioral patterns. For example, it centrally manages data across devices and comprehensively analyzes user behavior patterns. This makes it possible to extract cross-device behavioral patterns by integrating data from different devices.

[0055] The behavioral pattern extraction unit can estimate a user's emotions and extract behavioral patterns based on changes in emotions. For example, the behavioral pattern extraction unit uses AI to analyze a user's emotions in real time and analyze the impact of changes in emotions on behavior. For example, it analyzes the emotions felt when a user views an advertisement and investigates how those emotions affect clicks and conversions. The behavioral pattern extraction unit also uses an emotion estimation algorithm to detect changes in emotions and reflect them in behavioral patterns. For example, it tracks changes in a user's emotions over time and analyzes changes in behavioral patterns. This allows for the extraction of behavioral patterns based on changes in a user's emotions, enabling more accurate advertising delivery strategies and content improvement proposals.

[0056] The behavioral pattern extraction unit can analyze social media posts and comments in addition to user behavioral data to extract more comprehensive behavioral patterns. The behavioral pattern extraction unit, for example, collects social media posting data and analyzes user behavioral patterns. For example, it analyzes what types of posts users make and what types of comments they leave, and extracts behavioral patterns. The behavioral pattern extraction unit also analyzes social media posts and comments using text analysis technology. For example, it performs sentiment analysis to analyze the association between user emotions and behavior. In this way, by analyzing social media posts and comments, more comprehensive behavioral patterns can be extracted.

[0057] The behavioral pattern extraction unit subdivides the user's behavioral patterns by time period and day of the week, allowing for detailed analysis of differences in behavior during specific time periods. The behavioral pattern extraction unit, for example, classifies the user's behavioral data by time period and analyzes the behavioral patterns during specific time periods. For example, it analyzes the difference in ad click rates between daytime and nighttime. The behavioral pattern extraction unit also subdivides the behavioral patterns by day of the week and analyzes differences in behavior between weekdays and weekends. For example, it compares the user's behavioral patterns during weekday daytime and weekend nighttime. By subdividing the behavioral patterns by time period and day of the week, it is possible to perform a detailed analysis of differences in behavior during specific time periods.

[0058] The behavioral pattern extraction unit can integrate data from different devices (smartphones, tablets, and PCs) and extract cross-device behavioral patterns. The behavioral pattern extraction unit integrates behavioral data from, for example, smartphones, tablets, and PCs and analyzes cross-device behavioral patterns. For example, it analyzes how the same user behaves on different devices. The behavioral pattern extraction unit also clarifies methods for integrating data across different devices and extracting behavioral patterns. For example, it centrally manages data across devices and comprehensively analyzes user behavior patterns. This makes it possible to extract cross-device behavioral patterns by integrating data from different devices.

[0059] The behavioral pattern extraction unit can apply the results of behavioral pattern analysis to different industries and applications, and propose optimal strategies in each field. For example, the behavioral pattern extraction unit analyzes behavioral patterns in the e-commerce field and proposes optimal advertising distribution strategies. For example, it formulates advertising strategies based on user reactions to specific product categories. The behavioral pattern extraction unit also analyzes behavioral patterns in the education field and proposes optimal content improvements. For example, it proposes improvements based on user reactions to specific educational content. In this way, by applying the results of behavioral pattern analysis to different industries and applications, it is possible to propose optimal strategies in each field.

[0060] The behavioral pattern extraction unit uses the emotion estimation function to extract behavioral patterns based on the user's emotions and can propose a personalized advertising delivery strategy according to the emotions. The behavioral pattern extraction unit, for example, uses the emotion estimation function to extract behavioral patterns based on the user's emotions. For example, it delivers specific advertisements to users with positive emotions. The behavioral pattern extraction unit also uses an emotion estimation algorithm to detect changes in emotions and reflect them in the behavioral patterns. For example, it tracks changes in the user's emotions over time and analyzes changes in the behavioral patterns. In this way, by using the emotion estimation function, it is possible to extract behavioral patterns based on the user's emotions and propose a personalized advertising delivery strategy according to the emotions.

[0061] The ad delivery strategy generation unit can estimate user emotions and optimize the ad delivery strategy based on the emotions. The ad delivery strategy generation unit, for example, uses an emotion engine to analyze user emotions in real time and deliver ads that elicit positive emotions. For example, it prioritizes the delivery of ads that make the user feel happy. The ad delivery strategy generation unit also detects changes in emotions using an emotion estimation algorithm and reflects these changes in the ad delivery strategy. For example, it tracks changes in the user's emotions over time and adjusts the ad delivery strategy. This enables more effective ad delivery by optimizing the ad delivery strategy based on the user's emotions.

[0062] The advertisement delivery strategy generation unit can analyze a user's past purchase history and browsing history to generate personalized advertisements. The advertisement delivery strategy generation unit, for example, analyzes a user's past purchase history to generate personalized advertisements. For example, advertisements related to products previously purchased are delivered. The advertisement delivery strategy generation unit also analyzes a user's browsing history to generate advertisements based on the user's interests. For example, advertisements related to pages previously viewed are delivered. In this way, personalized advertisements can be generated by analyzing a user's past purchase history and browsing history.

[0063] The ad delivery strategy generation unit can analyze the user's lifestyle rhythm and behavioral patterns and propose the optimal ad delivery time. The ad delivery strategy generation unit, for example, analyzes the user's lifestyle rhythm and proposes the optimal ad delivery time. For example, it delivers ads during the time periods when the user is most active. The ad delivery strategy generation unit also analyzes the user's behavioral patterns and optimizes the timing of ad delivery. For example, it delivers ads according to the user's online time periods. In this way, by analyzing the user's lifestyle rhythm and behavioral patterns, it is possible to propose the optimal ad delivery time.

[0064] The ad delivery strategy generation unit can apply the ad delivery strategy to different platforms and propose an optimal strategy for each platform. The ad delivery strategy generation unit proposes an ad delivery strategy for, for example, a social media platform. For example, it delivers effective ads to a specific user demographic. The ad delivery strategy generation unit also proposes an ad delivery strategy for a search engine. For example, it displays optimal ads for specific keywords. Furthermore, the ad delivery strategy generation unit proposes an ad delivery strategy for a video site. For example, it delivers ads related to specific video content. In this way, by applying the ad delivery strategy to different platforms, it is possible to propose an optimal strategy for each platform.

[0065] The advertising delivery strategy generation unit can apply the advertising delivery strategy to different regions and cultural areas and propose a strategy that suits the characteristics of each region. The advertising delivery strategy generation unit, for example, proposes an advertising delivery strategy that takes into account the characteristics of different regions. For example, it delivers advertisements that suit the culture and customs of each region. The advertising delivery strategy generation unit also analyzes consumption behavior for each region and proposes an optimal advertising delivery strategy. For example, it delivers advertisements related to products that are popular in a specific region. In this way, by applying the advertising delivery strategy to different regions and cultural areas, it is possible to propose a strategy that suits the characteristics of each region.

[0066] The advertisement delivery strategy generation unit uses the emotion estimation function to propose an advertisement delivery strategy based on the user's emotions and can adjust advertisement content in real time according to the emotions. The advertisement delivery strategy generation unit, for example, uses the emotion estimation function to propose an advertisement delivery strategy based on the user's emotions. For example, it delivers specific advertisements to users with positive emotions. The advertisement delivery strategy generation unit also uses an emotion estimation algorithm to detect changes in emotions and adjust advertisement content in real time. For example, it instantly changes advertisement content according to changes in the user's emotions. In this way, by using the emotion estimation function, it is possible to propose an advertisement delivery strategy based on the user's emotions and adjust advertisement content in real time according to the emotions.

[0067] The content improvement suggestion unit can estimate the user's emotions and make content improvement suggestions based on the emotions. The content improvement suggestion unit, for example, uses an emotion engine to analyze the user's emotions in real time and suggest content that elicits positive emotions. For example, it prioritizes suggesting content that makes the user feel happy. The content improvement suggestion unit also detects changes in emotions using an emotion estimation algorithm and reflects these in content improvement suggestions. For example, it tracks changes in the user's emotions over time and suggests changes to the content. This enables more effective content improvement by making content improvement suggestions based on the user's emotions.

[0068] The content improvement suggestion unit can analyze the user's browsing history and feedback and make personalized improvement suggestions. The content improvement suggestion unit, for example, analyzes the user's browsing history and makes personalized content improvement suggestions. For example, it suggests improvements based on content that has been viewed in the past. The content improvement suggestion unit also analyzes the user's feedback and makes content improvement suggestions. For example, it suggests improvements based on the user's comments and ratings. In this way, by analyzing the user's browsing history and feedback, it is possible to make personalized improvement suggestions.

[0069] The content improvement proposal unit can analyze competitors' content and propose points of differentiation from competitors. The content improvement proposal unit, for example, analyzes competitors' content and proposes points of differentiation. For example, it analyzes competitors' strengths and weaknesses and proposes areas for improvement in its own content. The content improvement proposal unit also compares its own content with competitors' content and clarifies points of differentiation. For example, it proposes unique functions and services that competitors do not offer. In this way, it is possible to propose points of differentiation by analyzing competitors' content.

[0070] The content improvement proposal unit can apply content improvement proposals to different media formats and make improvement proposals that are optimal for each media format. The content improvement proposal unit, for example, makes improvement proposals for text content. For example, it proposes optimizing sentence structure and keywords. The content improvement proposal unit also makes improvement proposals for image content. For example, it proposes improvements to image layout and design. Furthermore, the content improvement proposal unit makes improvement proposals for video content. For example, it proposes improvements to video editing and content. In this way, by applying content improvement proposals to different media formats, it is possible to make improvement proposals that are optimal for each media format.

[0071] The content improvement proposal unit can apply content improvement proposals to different industries and applications, and make optimal improvement proposals in each field. The content improvement proposal unit makes content improvement proposals in the e-commerce field, for example. For example, it proposes optimizing product descriptions and reviews. The content improvement proposal unit also makes content improvement proposals in the education field. For example, it proposes improvements to the structure and content of educational content. Furthermore, the content improvement proposal unit makes content improvement proposals in the healthcare field. For example, it proposes improvements to health information and medical content. In this way, by applying content improvement proposals to different industries and applications, it is possible to make optimal improvement proposals in each field.

[0072] The content improvement suggestion unit uses the emotion estimation function to make content improvement suggestions based on the user's emotions and can adjust the content in real time according to the emotions. The content improvement suggestion unit, for example, uses the emotion estimation function to make content improvement suggestions based on the user's emotions. For example, it suggests content that elicits positive emotions. The content improvement suggestion unit also uses an emotion estimation algorithm to detect changes in emotions and adjust the content in real time. For example, it instantly changes the content in response to changes in the user's emotions. In this way, by using the emotion estimation function, it is possible to make content improvement suggestions based on the user's emotions and adjust the content in real time according to the emotions.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] The ad delivery optimization system can also obtain users' geographical location information and propose ad delivery strategies tailored to the characteristics of each region. For example, it can deliver ads related to products and services that are popular in a particular region. It can also analyze consumer behavior in each region based on the geographical location information and propose optimal ad delivery times. For example, it can formulate an ad delivery strategy that takes into account differences in consumer behavior between urban and suburban areas. This allows for more effective ad delivery by proposing ad delivery strategies tailored to the characteristics of each region.

[0075] The ad delivery optimization system can also analyze a user's purchase history and propose an ad delivery strategy based on past purchasing behavior. For example, it can deliver ads related to products previously purchased. It can also predict a user's interests based on the purchase history and generate personalized ads. For example, it can deliver information about new products and sales related to a specific category to a user who frequently purchases products in that category. This allows it to propose an ad delivery strategy based on the user's purchase history, thereby achieving a higher conversion rate.

[0076] The ad delivery optimization system can also analyze users' social media posts and comments to extract behavioral patterns on social media. For example, it can analyze the types of posts and comments users make and extract behavioral patterns. It can also predict users' interests based on social media data and generate personalized advertisements. For example, it can deliver advertisements related to a specific topic to users who post frequently on that topic. This allows for more effective ad delivery by proposing ad delivery strategies that utilize social media data.

[0077] The ad delivery optimization system can further estimate user emotions and propose ad delivery strategies based on those emotions. For example, it can deliver specific ads to users with positive emotions. It can also detect changes in emotions using an emotion estimation algorithm and reflect them in the ad delivery strategy. For example, it can track changes in a user's emotions over time and adjust the ad delivery strategy. This allows for more effective ad delivery by proposing an ad delivery strategy based on the user's emotions.

[0078] The ad delivery optimization system can also analyze a user's lifestyle rhythm and suggest optimal ad delivery times. For example, it can deliver ads during the times when the user is most active. It can also predict a user's behavioral patterns based on their lifestyle rhythm and optimize ad delivery strategies. For example, it can deliver ads during their morning commute or their evening relaxation time. This allows it to suggest ad delivery strategies based on the user's lifestyle rhythm, thereby achieving higher engagement.

[0079] The ad delivery optimization system can also estimate user emotions and make content improvement suggestions based on those emotions. For example, it can suggest content that elicits positive emotions. It can also detect changes in emotions using an emotion estimation algorithm and reflect them in content improvement suggestions. For example, it can track changes in a user's emotions over time and suggest changes to the content. This allows for more effective content improvement by making content improvement suggestions based on the user's emotions.

[0080] The ad delivery optimization system can further analyze user feedback and make content improvement suggestions based on the feedback. For example, it can suggest improvements based on user comments and ratings. It can also predict user needs and expectations based on the feedback and make personalized content improvement suggestions. For example, for content that has received a lot of specific feedback, it can make improvement suggestions that reflect that feedback. In this way, content improvement suggestions based on user feedback can be made, thereby achieving higher satisfaction.

[0081] The ad delivery optimization system can further estimate user emotions and generate personalized ads based on those emotions. For example, it can deliver specific ads to users with positive emotions. It can also use emotion estimation algorithms to detect changes in emotions and adjust ad content in real time. For example, it can instantly change ad content in response to changes in user emotions. This allows for the generation of personalized ads based on user emotions, thereby achieving higher engagement.

[0082] The ad delivery optimization system can also analyze a user's past browsing history and propose an ad delivery strategy based on that browsing history. For example, it can deliver ads related to pages previously viewed. It can also predict a user's interests based on the browsing history and generate personalized ads. For example, it can deliver new product and sale information related to a specific category to a user who frequently views pages in that category. This allows it to propose an ad delivery strategy based on the user's browsing history, thereby achieving a higher conversion rate.

[0083] The ad delivery optimization system can further estimate a user's emotions, make suggestions for content improvement based on those emotions, and adjust the content in real time according to those emotions. For example, it can suggest content that elicits positive emotions. It can also use an emotion estimation algorithm to detect changes in emotions and adjust the content in real time. For example, it can instantly change the content according to changes in the user's emotions. As a result, by using the emotion estimation function, it can make suggestions for content improvement based on the user's emotions and adjust the content in real time according to those emotions.

[0084] The processing flow of the second embodiment will be briefly explained below.

[0085] Step 1: The behavioral pattern extraction unit extracts customer behavioral patterns based on the data analyzed by the data analysis unit. For example, it analyzes user behavioral data and analyzes what types of advertisements and content are effective. The behavioral pattern extraction unit can also subdivide user behavioral patterns by time of day or day of the week. For example, it analyzes the difference in ad click rates between daytime and nighttime. Step 2: The behavioral pattern extraction unit integrates data from different devices and extracts cross-device behavioral patterns, allowing us to understand consistent behavioral patterns even when a user uses multiple devices.

[0086] 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.

[0087] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0088] 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.

[0089] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0090] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0099] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0112] 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.

[0113] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0114] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0129] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0130] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0131] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0132] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0134] The data processing system 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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."

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0153] 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. The behavioral pattern extraction unit Integrate data from different devices to extract cross-device behavioral patterns 2. The system of claim 1.

2. The behavioral pattern extraction unit Estimating the user's emotions and extracting behavioral patterns based on changes in the emotions.

2. The system of claim 1.

3. The behavioral pattern extraction unit Integrate data from different devices (smartphones, tablets, PCs) to extract cross-device behavioral patterns 2. The system of claim 1.

4. The advertisement delivery strategy generation unit Inferring the user's emotions and optimizing an advertisement delivery strategy based on the emotions.

2. The system of claim 1.

5. The content improvement suggestion unit Estimating the user's feelings and making suggestions for improving the content based on the feelings 2. The system of claim 1.

6. The behavioral pattern extraction unit Using the emotion estimation function, behavioral patterns based on the user's emotions are extracted, and a personalized advertising delivery strategy is proposed according to the emotions.

2. The system of claim 1.

7. The advertisement delivery strategy generation unit Using the emotion estimation function, an advertisement distribution strategy based on the user's emotions is proposed, and advertisement content is adjusted in real time according to the emotions.

2. The system of claim 1.

8. The content improvement suggestion unit Using the emotion estimation function, content improvement suggestions are made based on the user's emotions, and the content is adjusted in real time according to the emotions.

2. The system of claim 1.

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