System

Abstract

An object of a system according to an embodiment is to make a process from production to development of an advertisement efficient and to select an effective advertisement pattern.SOLUTION: A system includes a generation unit, a prediction unit, and a selection unit. The generation unit generates a plurality of advertisement patterns using the generation AI. The prediction unit predicts an effect of the advertisement pattern generated by the generation unit. The selection unit selects an optimum advertisement pattern based on the effect predicted by the prediction unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process from creating to distributing advertisements required time and effort, and it was difficult to find effective advertising patterns.

[0005] The system according to the embodiment aims to improve the efficiency of the process from advertisement creation to distribution, and to select an effective advertisement pattern. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a prediction unit, and a selection unit. The generation unit generates a plurality of advertising patterns using a generation AI. The prediction unit predicts the effectiveness of the advertising patterns generated by the generation unit. The selection unit selects the optimal advertising pattern based on the effectiveness predicted by the prediction unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the process from advertisement creation to distribution, and can select an effective advertisement pattern. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate 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) In the advertising deployment system according to the embodiment of the present invention, a generation AI generates multiple advertising patterns, and an effect prediction system narrows down the patterns. This enables the advertising deployment system to achieve both efficient and high-quality advertising deployment.

[0029] An advertising deployment system according to an embodiment includes a generation unit, a prediction unit, and a selection unit. The generation unit generates multiple advertising patterns using a generation AI. For example, the generation AI generates advertising patterns based on user instructions. The generation AI can also analyze target audience data and generate advertising patterns based on the data. The generation AI can also generate advertising patterns based on past advertising data. For example, when creating an advertisement for a specific target audience, the generation AI analyzes the target's hobbies, preferences, and behavioral patterns and generates multiple advertising designs and messages based on the analysis. The prediction unit predicts the effectiveness of the advertising patterns generated by the generation unit. For example, the prediction unit analyzes past advertising data and market data to quantify the effectiveness of each advertising pattern. The prediction unit can also predict which advertising pattern will be most effective using indicators such as click-through rate and conversion rate. The prediction unit can also predict the effectiveness of the advertising pattern using natural language processing technology. For example, the prediction unit quantifies the effectiveness of each advertising pattern based on past advertising data and selects the most effective pattern. The selection unit selects the optimal advertising pattern based on the effectiveness predicted by the prediction unit. For example, the selection unit selects the most effective advertising pattern based on the effectiveness quantified by the prediction unit. The selection unit can also select the advertising pattern most suitable for a specific target audience. The selection unit can also monitor the effectiveness of advertising patterns in real time and select the optimal advertising pattern. For example, the selection unit selects the most effective advertising pattern based on the effectiveness quantified by the prediction unit and delivers an appropriate message to the target audience. This allows the advertising deployment system according to the embodiment to achieve both efficient and high-quality advertising deployment. For example, the generation AI can analyze data on the target audience and generate customized advertisements based on the data, thereby enabling more effective advertising. Furthermore, by utilizing the effectiveness prediction system, the effectiveness of advertising can be predicted in advance and the optimal advertising pattern can be selected.

[0030] The generation unit can reflect real-time market trend data and generate advertising patterns that are in line with the latest trends. For example, the generation unit inputs real-time market trend data into the generation AI and generates advertising patterns that are in line with the latest trends. For example, it generates advertisements that incorporate currently popular fashions and products. The generation unit also collects market trend data in real time, and the generation AI generates advertising patterns based on that data. For example, it generates advertisements that reflect the latest technologies and services. The generation unit also analyzes trend data, and the generation AI generates advertising patterns that are most suited to market trends. For example, it generates advertising designs that reflect seasonal trends. This makes it possible to generate advertising patterns that are in line with the latest market trends.

[0031] The generation unit can reflect the user's past purchasing history and generate advertising patterns based on individual purchasing tendencies. For example, the generation unit inputs the user's past purchasing history into the generation AI and generates advertising patterns based on individual purchasing tendencies. For example, it generates advertisements related to products purchased in the past. The generation unit also analyzes purchasing history data, and the generation AI generates advertising patterns that are most suited to the user's purchasing tendencies. For example, it generates advertisements based on frequently purchased product categories. The generation unit also collects the user's purchasing history in real time, and the generation AI generates advertising patterns according to individual purchasing tendencies based on that data. For example, it generates advertisements for specific brands or products. This makes it possible to generate advertising patterns based on individual purchasing tendencies.

[0032] The generation unit can incorporate data from different cultural spheres and regions to generate advertising patterns that can be applied globally. For example, the generation unit inputs data from different cultural spheres and regions into the generation AI to generate advertising patterns that can be applied globally. For example, it generates advertisements that are tailored to the culture and customs of each region. The generation unit also analyzes data from cultural spheres and regions, and the generation AI generates advertising patterns based on that data. For example, it generates advertising designs that are suited to different languages ​​and cultures. The generation unit also collects global market data, and the generation AI generates advertising patterns based on that data. For example, it generates advertisements that are tailored to international events and trends. This makes it possible to generate advertising patterns that can be applied globally.

[0033] The generation unit can combine audio or video multimedia elements to generate visually and auditorily appealing advertising patterns. For example, the generation unit inputs audio or video data into a generation AI to generate visually and auditorily appealing advertising patterns. For example, it generates advertisements that combine audio narration and video clips. The generation unit also analyzes multimedia elements, and the generation AI generates advertising patterns based on that data. For example, it generates advertising designs that incorporate music and sound effects. The generation unit also collects audio and video data in real time, and the generation AI generates advertising patterns based on that data. For example, it generates advertisements that combine live footage and interviews. This makes it possible to generate visually and auditorily appealing advertising patterns.

[0034] The prediction unit reflects real-time market data and can predict effects based on the latest market trends. The prediction unit, for example, inputs real-time market data into the effect prediction system and predicts effects based on the latest market trends. For example, it predicts the click-through rate of an advertising pattern in line with current market trends. The prediction unit also collects market data in real time, and the effect prediction system predicts effects based on market trends based on that data. For example, it predicts the conversion rate of an advertisement based on the latest consumer behavior. The prediction unit also analyzes market data, and the effect prediction system predicts the effects of an advertising pattern that is most suitable for market trends. For example, it predicts the effects of an advertisement that reflects seasonal market trends. This makes it possible to predict effects based on the latest market trends.

[0035] The prediction unit can analyze past advertising campaign data and predict effectiveness based on past success patterns. For example, the prediction unit inputs past advertising campaign data into an effectiveness prediction system and predicts effectiveness based on past success patterns. For example, it predicts the effectiveness of advertising patterns that have recorded high click-through rates in the past. The prediction unit also analyzes advertising campaign data, and the effectiveness prediction system predicts effectiveness based on past success patterns. For example, it predicts the effectiveness of advertising patterns that have had high conversion rates in the past. The prediction unit also collects past data in real time, and the effectiveness prediction system uses that data to predict effectiveness based on past success patterns. For example, it predicts the effectiveness of advertising based on past success cases. This makes it possible to predict effectiveness based on past success patterns.

[0036] The prediction unit can input data from different industries and fields and predict effects from a cross-industry perspective. For example, the prediction unit inputs data from different industries and fields into the effect prediction system and predicts effects from a cross-industry perspective. For example, the effect of an advertising pattern is predicted based on success stories from different industries. The prediction unit also analyzes industry data, and the effect prediction system predicts effects based on data from different fields. For example, it predicts effects by combining data from the technology field and the consumer market. The prediction unit also collects data from different industries in real time, and the effect prediction system predicts effects from a cross-industry perspective based on that data. For example, it predicts effects by combining data from the medical and entertainment fields. This makes it possible to predict effects from a cross-industry perspective.

[0037] The prediction unit can import social media data and perform effect predictions that reflect real-time user responses. The prediction unit, for example, inputs social media data into an effect prediction system and performs effect predictions that reflect real-time user responses. For example, the prediction unit predicts the effect of an advertising pattern based on engagement data on social media. The prediction unit also collects social media data in real time, and the effect prediction system performs effect predictions that reflect user responses based on that data. For example, the prediction unit predicts the effect of an advertisement based on data on tweets and comments. The prediction unit also analyzes social media data, and the effect prediction system performs effect predictions based on real-time user responses. For example, the prediction unit predicts the effect of an advertisement based on the number of shares and likes on social media. This makes it possible to perform effect predictions that reflect real-time user responses.

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

[0039] The generation unit can take in user behavior data and generate advertising patterns based on the behavior. For example, it can analyze data on websites and apps frequently visited by the user and generate advertising patterns based on that data. The generation unit can also use the user's location information to generate advertisements related to specific regions or locations. For example, when a user approaches a specific store or event venue, it can display advertisements related to that location. The generation unit can also analyze the user's device usage patterns and display advertisements at optimal times. For example, it can generate advertisements based on the time of day when the user is using their smartphone. This makes it possible to generate advertising patterns based on user behavior.

[0040] The generation unit can reflect real-time market trend data and generate advertising patterns that are in line with the latest trends. For example, real-time market trend data is input into the generation AI to generate advertising patterns that are in line with the latest trends. For example, advertisements that incorporate currently popular fashions and products are generated. The generation unit also collects market trend data in real time, and the generation AI generates advertising patterns based on that data. For example, advertisements that reflect the latest technologies and services are generated. The generation unit also analyzes trend data, and the generation AI generates advertising patterns that are most suited to market trends. For example, advertising designs that reflect seasonal trends are generated. This makes it possible to generate advertising patterns that are in line with the latest market trends.

[0041] The generation unit can reflect the user's past purchasing history and generate advertising patterns based on individual purchasing tendencies. For example, the user's past purchasing history is input into the generation AI to generate advertising patterns based on individual purchasing tendencies. For example, advertisements related to products purchased in the past are generated. The generation unit also analyzes the purchasing history data, and the generation AI generates advertising patterns that are most suited to the user's purchasing tendencies. For example, advertisements are generated based on frequently purchased product categories. The generation unit also collects the user's purchasing history in real time, and the generation AI generates advertising patterns based on individual purchasing tendencies based on that data. For example, advertisements are generated for specific brands or products. This makes it possible to generate advertising patterns based on individual purchasing tendencies.

[0042] The generation unit can incorporate data from different cultural spheres and regions to generate advertising patterns that can be applied globally. For example, data from different cultural spheres and regions is input into the generation AI to generate advertising patterns that can be applied globally. For example, advertisements that are tailored to the culture and customs of each region are generated. The generation unit also analyzes data from cultural spheres and regions, and the generation AI generates advertising patterns based on that data. For example, advertising designs that are suited to different languages ​​and cultures are generated. The generation unit also collects global market data, and the generation AI generates advertising patterns based on that data. For example, advertisements that are tailored to international events and trends are generated. This makes it possible to generate advertising patterns that can be applied globally.

[0043] The generation unit can combine audio or video multimedia elements to generate visually and auditorily appealing advertising patterns. For example, audio or video data can be input into the generation AI to generate visually and auditorily appealing advertising patterns. For example, an advertisement that combines audio narration and video clips can be generated. The generation unit can also analyze multimedia elements, and the generation AI can generate advertising patterns based on that data. For example, it can generate advertising designs that incorporate music and sound effects. The generation unit can also collect audio or video data in real time, and the generation AI can generate advertising patterns based on that data. For example, it can generate advertisements that combine live footage and interviews. This makes it possible to generate visually and auditorily appealing advertising patterns.

[0044] The prediction unit reflects real-time market data and can predict effects based on the latest market trends. For example, real-time market data is input into the effect prediction system, and an effect prediction is made based on the latest market trends. For example, the click-through rate of an advertising pattern in line with current market trends is predicted. The prediction unit also collects market data in real time, and the effect prediction system makes an effect prediction in accordance with market trends based on that data. For example, the prediction unit predicts the conversion rate of an advertisement based on the latest consumer behavior. The prediction unit also analyzes the market data, and the effect prediction system predicts the effect of an advertising pattern that is most suitable for market trends. For example, the effect of an advertisement that reflects seasonal market trends is predicted. This makes it possible to make effect predictions based on the latest market trends.

[0045] The prediction unit can analyze past advertising campaign data and predict effectiveness based on past success patterns. For example, past advertising campaign data is input into the effectiveness prediction system, and effectiveness prediction is performed based on past success patterns. For example, the effectiveness of advertising patterns that have recorded high click-through rates in the past is predicted. The prediction unit also analyzes the advertising campaign data, and the effectiveness prediction system predicts effectiveness based on past success patterns. For example, the effectiveness of advertising patterns that have had high conversion rates in the past is predicted. The prediction unit also collects past data in real time, and the effectiveness prediction system uses that data to predict effectiveness based on past success patterns. For example, the effectiveness of advertising is predicted based on past success cases. This makes it possible to predict effectiveness based on past success patterns.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The generation unit uses generation AI to generate multiple advertising patterns. For example, the generation AI generates advertising patterns based on user instructions. The generation AI can also analyze target audience data and generate advertising patterns based on that data. Furthermore, the generation AI can generate advertising patterns based on past advertising data. For example, when creating an advertisement for a specific target audience, the target's hobbies, preferences, and behavioral patterns are analyzed, and multiple advertising designs and messages are generated based on that information. Step 2: The prediction unit predicts the effectiveness of the advertising patterns generated by the generation unit. For example, the prediction unit analyzes past advertising data and market data to quantify the effectiveness of each advertising pattern. It can also predict which advertising pattern will be most effective using indicators such as click-through rate and conversion rate. It can also predict the effectiveness of advertising patterns using natural language processing technology. For example, it can quantify the effectiveness of each advertising pattern based on past advertising data and select the most effective pattern. Step 3: The selection unit selects the optimal advertising pattern based on the effects predicted by the prediction unit. For example, the selection unit selects the most effective advertising pattern based on the effects quantified by the prediction unit. It can also select the advertising pattern that is most suitable for a specific target audience. It can also monitor the effects of advertising patterns in real time and select the optimal advertising pattern. For example, the selection unit selects the most effective advertising pattern based on the effects quantified by the prediction unit, and delivers an appropriate message to the target audience.

[0048] (Example 2) In the advertising deployment system according to the embodiment of the present invention, a generation AI generates multiple advertising patterns, and an effect prediction system narrows down the patterns. This enables the advertising deployment system to achieve both efficient and high-quality advertising deployment.

[0049] An advertising deployment system according to an embodiment includes a generation unit, a prediction unit, and a selection unit. The generation unit generates multiple advertising patterns using a generation AI. For example, the generation AI generates advertising patterns based on user instructions. The generation AI can also analyze target audience data and generate advertising patterns based on the data. The generation AI can also generate advertising patterns based on past advertising data. For example, when creating an advertisement for a specific target audience, the generation AI analyzes the target's hobbies, preferences, and behavioral patterns and generates multiple advertising designs and messages based on the analysis. The prediction unit predicts the effectiveness of the advertising patterns generated by the generation unit. For example, the prediction unit analyzes past advertising data and market data to quantify the effectiveness of each advertising pattern. The prediction unit can also predict which advertising pattern will be most effective using indicators such as click-through rate and conversion rate. The prediction unit can also predict the effectiveness of the advertising pattern using natural language processing technology. For example, the prediction unit quantifies the effectiveness of each advertising pattern based on past advertising data and selects the most effective pattern. The selection unit selects the optimal advertising pattern based on the effectiveness predicted by the prediction unit. For example, the selection unit selects the most effective advertising pattern based on the effectiveness quantified by the prediction unit. The selection unit can also select the advertising pattern most suitable for a specific target audience. The selection unit can also monitor the effectiveness of advertising patterns in real time and select the optimal advertising pattern. For example, the selection unit selects the most effective advertising pattern based on the effectiveness quantified by the prediction unit and delivers an appropriate message to the target audience. This allows the advertising deployment system according to the embodiment to achieve both efficient and high-quality advertising deployment. For example, the generation AI can analyze data on the target audience and generate customized advertisements based on the data, thereby enabling more effective advertising. Furthermore, by utilizing the effectiveness prediction system, the effectiveness of advertising can be predicted in advance and the optimal advertising pattern can be selected.

[0050] The generation unit can take in user emotional data as input and generate advertising patterns based on the emotions. For example, the generation unit inputs the user emotional data into a generation AI and generates advertising patterns based on the emotions. For example, it generates advertising designs and messages that make the user feel happy. The generation unit also collects user emotional data in real time, and based on that data, the generation AI generates advertising patterns that correspond to the emotions. For example, it generates an advertisement that makes the user feel surprised. The generation unit also analyzes the emotional data, and the generation AI generates an advertising pattern that best suits the user's emotions. For example, it generates an advertising design that makes the user feel reassured. This makes it possible to generate advertising patterns based on the user's emotions.

[0051] The generation unit can reflect real-time market trend data and generate advertising patterns that are in line with the latest trends. For example, the generation unit inputs real-time market trend data into the generation AI and generates advertising patterns that are in line with the latest trends. For example, it generates advertisements that incorporate currently popular fashions and products. The generation unit also collects market trend data in real time, and the generation AI generates advertising patterns based on that data. For example, it generates advertisements that reflect the latest technologies and services. The generation unit also analyzes trend data, and the generation AI generates advertising patterns that are most suited to market trends. For example, it generates advertising designs that reflect seasonal trends. This makes it possible to generate advertising patterns that are in line with the latest market trends.

[0052] The generation unit can reflect the user's past purchasing history and generate advertising patterns based on individual purchasing tendencies. For example, the generation unit inputs the user's past purchasing history into the generation AI and generates advertising patterns based on individual purchasing tendencies. For example, it generates advertisements related to products purchased in the past. The generation unit also analyzes purchasing history data, and the generation AI generates advertising patterns that are most suited to the user's purchasing tendencies. For example, it generates advertisements based on frequently purchased product categories. The generation unit also collects the user's purchasing history in real time, and the generation AI generates advertising patterns according to individual purchasing tendencies based on that data. For example, it generates advertisements for specific brands or products. This makes it possible to generate advertising patterns based on individual purchasing tendencies.

[0053] The generation unit can incorporate data from different cultural spheres and regions to generate advertising patterns that can be applied globally. For example, the generation unit inputs data from different cultural spheres and regions into the generation AI to generate advertising patterns that can be applied globally. For example, it generates advertisements that are tailored to the culture and customs of each region. The generation unit also analyzes data from cultural spheres and regions, and the generation AI generates advertising patterns based on that data. For example, it generates advertising designs that are suited to different languages ​​and cultures. The generation unit also collects global market data, and the generation AI generates advertising patterns based on that data. For example, it generates advertisements that are tailored to international events and trends. This makes it possible to generate advertising patterns that can be applied globally.

[0054] The generation unit can combine audio or video multimedia elements to generate visually and auditorily appealing advertising patterns. For example, the generation unit inputs audio or video data into a generation AI to generate visually and auditorily appealing advertising patterns. For example, it generates advertisements that combine audio narration and video clips. The generation unit also analyzes multimedia elements, and the generation AI generates advertising patterns based on that data. For example, it generates advertising designs that incorporate music and sound effects. The generation unit also collects audio and video data in real time, and the generation AI generates advertising patterns based on that data. For example, it generates advertisements that combine live footage and interviews. This makes it possible to generate visually and auditorily appealing advertising patterns.

[0055] The generation unit uses the emotion estimation function to provide feedback on the user's emotional response to the advertising pattern to be generated in real time, thereby generating an optimal advertising pattern. For example, the generation unit uses the emotion estimation function to collect the user's emotional response to the advertising pattern generated by the generation AI in real time, and generates an optimal advertising pattern based on that data. For example, the generation unit analyzes the user's facial expressions and voice and calculates an emotional score. The generation unit also builds a system in which the generation AI adjusts the advertising pattern in real time based on the user's emotional response data. For example, it prioritizes generating designs that evoke a high number of positive emotional responses. The generation unit also collects emotion estimation data in real time, and the generation AI generates advertising patterns based on that data. For example, it adjusts the advertising message in response to changes in the user's emotions. This allows for feedback on the user's emotional response in real time, and generates an optimal advertising pattern.

[0056] The prediction unit can take in user emotional data as input and predict effects based on emotions. The prediction unit, for example, inputs user emotional data into an effect prediction system and predicts effects based on emotions. For example, it predicts the click rate of an advertising pattern that makes the user feel happy. The prediction unit also collects user emotional data in real time, and based on that data, the effect prediction system predicts effects according to emotions. For example, it predicts the conversion rate of an advertisement that makes the user feel surprised. The prediction unit also analyzes the emotional data, and the effect prediction system predicts the effect of an advertising pattern that best suits the user's emotions. For example, it predicts the effect of an advertisement that makes the user feel reassured. This makes it possible to predict effects based on user emotions.

[0057] The prediction unit reflects real-time market data and can predict effects based on the latest market trends. The prediction unit, for example, inputs real-time market data into the effect prediction system and predicts effects based on the latest market trends. For example, it predicts the click-through rate of an advertising pattern in line with current market trends. The prediction unit also collects market data in real time, and the effect prediction system predicts effects based on market trends based on that data. For example, it predicts the conversion rate of an advertisement based on the latest consumer behavior. The prediction unit also analyzes market data, and the effect prediction system predicts the effects of an advertising pattern that is most suitable for market trends. For example, it predicts the effects of an advertisement that reflects seasonal market trends. This makes it possible to predict effects based on the latest market trends.

[0058] The prediction unit can analyze past advertising campaign data and predict effectiveness based on past success patterns. For example, the prediction unit inputs past advertising campaign data into an effectiveness prediction system and predicts effectiveness based on past success patterns. For example, it predicts the effectiveness of advertising patterns that have recorded high click-through rates in the past. The prediction unit also analyzes advertising campaign data, and the effectiveness prediction system predicts effectiveness based on past success patterns. For example, it predicts the effectiveness of advertising patterns that have had high conversion rates in the past. The prediction unit also collects past data in real time, and the effectiveness prediction system uses that data to predict effectiveness based on past success patterns. For example, it predicts the effectiveness of advertising based on past success cases. This makes it possible to predict effectiveness based on past success patterns.

[0059] The prediction unit can input data from different industries and fields and predict effects from a cross-industry perspective. For example, the prediction unit inputs data from different industries and fields into the effect prediction system and predicts effects from a cross-industry perspective. For example, the effect of an advertising pattern is predicted based on success stories from different industries. The prediction unit also analyzes industry data, and the effect prediction system predicts effects based on data from different fields. For example, it predicts effects by combining data from the technology field and the consumer market. The prediction unit also collects data from different industries in real time, and the effect prediction system predicts effects from a cross-industry perspective based on that data. For example, it predicts effects by combining data from the medical and entertainment fields. This makes it possible to predict effects from a cross-industry perspective.

[0060] The prediction unit can import social media data and perform effect predictions that reflect real-time user responses. The prediction unit, for example, inputs social media data into an effect prediction system and performs effect predictions that reflect real-time user responses. For example, the prediction unit predicts the effect of an advertising pattern based on engagement data on social media. The prediction unit also collects social media data in real time, and the effect prediction system performs effect predictions that reflect user responses based on that data. For example, the prediction unit predicts the effect of an advertisement based on data on tweets and comments. The prediction unit also analyzes social media data, and the effect prediction system performs effect predictions based on real-time user responses. For example, the prediction unit predicts the effect of an advertisement based on the number of shares and likes on social media. This makes it possible to perform effect predictions that reflect real-time user responses.

[0061] The prediction unit uses the emotion estimation function to provide feedback on the user's emotional responses in real time when predicting the effectiveness of an advertising pattern, thereby narrowing down the optimal advertising pattern. For example, the prediction unit uses the emotion estimation function to collect the user's emotional responses in real time when the effect prediction system predicts the effectiveness of an advertising pattern, and narrows down the optimal advertising pattern based on that data. For example, the prediction unit analyzes the user's facial expressions and voice and calculates an emotional score. The prediction unit also builds a system in which the effect prediction system adjusts the advertising pattern in real time based on the user's emotional response data. For example, it prioritizes the selection of designs that generate a high number of positive emotional responses. The prediction unit also collects emotion estimation data in real time, and the effect prediction system narrows down the advertising patterns based on that data. For example, it adjusts the advertising message in accordance with changes in the user's emotions. This allows feedback on the user's emotional responses in real time, and narrows down the optimal advertising pattern.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The generation unit can take in user behavior data and generate advertising patterns based on the behavior. For example, it can analyze data on websites and apps frequently visited by the user and generate advertising patterns based on that data. The generation unit can also use the user's location information to generate advertisements related to specific regions or locations. For example, when a user approaches a specific store or event venue, it can display advertisements related to that location. The generation unit can also analyze the user's device usage patterns and display advertisements at optimal times. For example, it can generate advertisements based on the time of day when the user is using their smartphone. This makes it possible to generate advertising patterns based on user behavior.

[0064] The generation unit can take in user emotional data as input and generate advertising patterns based on emotions. For example, user emotional data can be input into the generation AI to generate advertising patterns based on emotions. For example, it can generate advertising designs and messages that make users feel happy. The generation unit can also collect user emotional data in real time, and based on that data, the generation AI can generate advertising patterns that correspond to the emotions. For example, it can generate an advertisement that makes the user feel surprised. The generation unit can also analyze the emotional data, and the generation AI can generate advertising patterns that best suit the user's emotions. For example, it can generate an advertising design that makes the user feel reassured. This makes it possible to generate advertising patterns based on user emotions.

[0065] The generation unit can reflect real-time market trend data and generate advertising patterns that are in line with the latest trends. For example, real-time market trend data is input into the generation AI to generate advertising patterns that are in line with the latest trends. For example, advertisements that incorporate currently popular fashions and products are generated. The generation unit also collects market trend data in real time, and the generation AI generates advertising patterns based on that data. For example, advertisements that reflect the latest technologies and services are generated. The generation unit also analyzes trend data, and the generation AI generates advertising patterns that are most suited to market trends. For example, advertising designs that reflect seasonal trends are generated. This makes it possible to generate advertising patterns that are in line with the latest market trends.

[0066] The generation unit can reflect the user's past purchasing history and generate advertising patterns based on individual purchasing tendencies. For example, the user's past purchasing history is input into the generation AI to generate advertising patterns based on individual purchasing tendencies. For example, advertisements related to products purchased in the past are generated. The generation unit also analyzes the purchasing history data, and the generation AI generates advertising patterns that are most suited to the user's purchasing tendencies. For example, advertisements are generated based on frequently purchased product categories. The generation unit also collects the user's purchasing history in real time, and the generation AI generates advertising patterns based on individual purchasing tendencies based on that data. For example, advertisements are generated for specific brands or products. This makes it possible to generate advertising patterns based on individual purchasing tendencies.

[0067] The generation unit can incorporate data from different cultural spheres and regions to generate advertising patterns that can be applied globally. For example, data from different cultural spheres and regions is input into the generation AI to generate advertising patterns that can be applied globally. For example, advertisements that are tailored to the culture and customs of each region are generated. The generation unit also analyzes data from cultural spheres and regions, and the generation AI generates advertising patterns based on that data. For example, advertising designs that are suited to different languages ​​and cultures are generated. The generation unit also collects global market data, and the generation AI generates advertising patterns based on that data. For example, advertisements that are tailored to international events and trends are generated. This makes it possible to generate advertising patterns that can be applied globally.

[0068] The generation unit can combine audio or video multimedia elements to generate visually and auditorily appealing advertising patterns. For example, audio or video data can be input into the generation AI to generate visually and auditorily appealing advertising patterns. For example, an advertisement that combines audio narration and video clips can be generated. The generation unit can also analyze multimedia elements, and the generation AI can generate advertising patterns based on that data. For example, it can generate advertising designs that incorporate music and sound effects. The generation unit can also collect audio or video data in real time, and the generation AI can generate advertising patterns based on that data. For example, it can generate advertisements that combine live footage and interviews. This makes it possible to generate visually and auditorily appealing advertising patterns.

[0069] The generation unit uses the emotion estimation function to provide feedback on the user's emotional response to the advertising pattern to be generated in real time, thereby generating the optimal advertising pattern. For example, the emotion estimation function can be used to collect the user's emotional response to the advertising pattern generated by the generation AI in real time, and generate the optimal advertising pattern based on that data. For example, the user's facial expressions and voice can be analyzed to calculate an emotional score. The generation unit also builds a system in which the generation AI adjusts the advertising pattern in real time based on the user's emotional response data. For example, it can prioritize the generation of designs that evoke a high number of positive emotional responses. The generation unit also collects emotion estimation data in real time, and the generation AI generates advertising patterns based on that data. For example, it adjusts the advertising message in response to changes in the user's emotions. This allows feedback on the user's emotional response in real time, and generates the optimal advertising pattern.

[0070] The prediction unit can take in user emotional data as input and predict effects based on emotions. For example, user emotional data is input into an effect prediction system and an effect prediction based on emotions is made. For example, the click rate of an advertising pattern that makes the user feel happy is predicted. The prediction unit also collects user emotional data in real time, and based on that data, the effect prediction system makes an effect prediction according to emotions. For example, it predicts the conversion rate of an advertisement that makes the user feel surprised. The prediction unit also analyzes the emotional data, and the effect prediction system predicts the effect of an advertising pattern that best suits the user's emotions. For example, it predicts the effect of an advertisement that makes the user feel reassured. This makes it possible to make effect predictions based on user emotions.

[0071] The prediction unit reflects real-time market data and can predict effects based on the latest market trends. For example, real-time market data is input into the effect prediction system, and an effect prediction is made based on the latest market trends. For example, the click-through rate of an advertising pattern in line with current market trends is predicted. The prediction unit also collects market data in real time, and the effect prediction system makes an effect prediction in accordance with market trends based on that data. For example, the prediction unit predicts the conversion rate of an advertisement based on the latest consumer behavior. The prediction unit also analyzes the market data, and the effect prediction system predicts the effect of an advertising pattern that is most suitable for market trends. For example, the effect of an advertisement that reflects seasonal market trends is predicted. This makes it possible to make effect predictions based on the latest market trends.

[0072] The prediction unit can analyze past advertising campaign data and predict effectiveness based on past success patterns. For example, past advertising campaign data is input into the effectiveness prediction system, and effectiveness prediction is performed based on past success patterns. For example, the effectiveness of advertising patterns that have recorded high click-through rates in the past is predicted. The prediction unit also analyzes the advertising campaign data, and the effectiveness prediction system predicts effectiveness based on past success patterns. For example, the effectiveness of advertising patterns that have had high conversion rates in the past is predicted. The prediction unit also collects past data in real time, and the effectiveness prediction system uses that data to predict effectiveness based on past success patterns. For example, the effectiveness of advertising is predicted based on past success cases. This makes it possible to predict effectiveness based on past success patterns.

[0073] The prediction unit uses the emotion estimation function to provide feedback on the user's emotional responses in real time when predicting the effectiveness of an advertising pattern, thereby narrowing down the optimal advertising pattern. For example, the emotion estimation function is used to collect the user's emotional responses in real time when the effect prediction system predicts the effectiveness of an advertising pattern, and the optimal advertising pattern is narrowed down based on that data. For example, the emotion estimation function analyzes the user's facial expressions and voice and calculates an emotion score. The prediction unit also builds a system in which the effect prediction system adjusts the advertising pattern in real time based on the user's emotional response data. For example, designs with a high number of positive emotional responses are selected preferentially. The prediction unit also collects emotion estimation data in real time, and the effect prediction system narrows down the advertising patterns based on that data. For example, the advertising message is adjusted in accordance with changes in the user's emotions. This allows the user's emotional responses to be fed back in real time, and the optimal advertising pattern to be narrowed down.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The generation unit uses generation AI to generate multiple advertising patterns. For example, the generation AI generates advertising patterns based on user instructions. The generation AI can also analyze target audience data and generate advertising patterns based on that data. Furthermore, the generation AI can generate advertising patterns based on past advertising data. For example, when creating an advertisement for a specific target audience, the target's hobbies, preferences, and behavioral patterns are analyzed, and multiple advertising designs and messages are generated based on that information. Step 2: The prediction unit predicts the effectiveness of the advertising patterns generated by the generation unit. For example, the prediction unit analyzes past advertising data and market data to quantify the effectiveness of each advertising pattern. It can also predict which advertising pattern will be most effective using indicators such as click-through rate and conversion rate. It can also predict the effectiveness of advertising patterns using natural language processing technology. For example, it can quantify the effectiveness of each advertising pattern based on past advertising data and select the most effective pattern. Step 3: The selection unit selects the optimal advertising pattern based on the effects predicted by the prediction unit. For example, the selection unit selects the most effective advertising pattern based on the effects quantified by the prediction unit. It can also select the advertising pattern that is most suitable for a specific target audience. It can also monitor the effects of advertising patterns in real time and select the optimal advertising pattern. For example, the selection unit selects the most effective advertising pattern based on the effects quantified by the prediction unit, and delivers an appropriate message to the target audience.

[0076] 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.

[0077] 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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a generation unit that generates a plurality of advertisement patterns using a generation AI; a prediction unit that predicts the effectiveness of the advertisement pattern generated by the generation unit; a selection unit that selects an optimal advertising pattern based on the effect predicted by the prediction unit. A system characterized by:

2. The generation unit Reflects real-time market trend data and generates advertising patterns that are in line with the latest trends 2. The system of claim 1.

3. The generation unit Combine audio or video multimedia elements to create visually and aurally appealing ad variations 2. The system of claim 1.

4. The prediction unit Inputs user emotional data and predicts effects based on emotions.

2. The system of claim 1.

5. The prediction unit When predicting the effectiveness of advertising patterns, users' emotional reactions are fed back in real time to narrow down the optimal advertising patterns.

2. The system of claim 1.

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