System

Abstract

An object of the system according to the embodiment is to distribute advertisements based on the specific personality and characteristics of a customer.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a distribution unit. The collection unit collects a result of the creative test. The analysis unit analyzes the data collected by the collection unit. The determination unit determines a specific personality or characteristic of the customer based on the data analyzed by the analysis unit. The distribution part distributes the advertisement based on the specific character and characteristics of the customer determined by the determination part.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 results of creative tests were uniformly sent to everyone, which meant that ads were not delivered in a way that was adequately tailored to the personality and characteristics of each customer.

[0005] The system according to the embodiment aims to provide advertisements based on the specific characteristics and features of the customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a distribution unit. The collection unit collects results of creative tests. The analysis unit analyzes the data collected by the collection unit. The determination unit determines specific personalities and characteristics of customers based on the data analyzed by the analysis unit. The distribution unit distributes advertisements based on the specific personalities and characteristics of customers determined by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide advertisements based on specific characteristics and traits of customers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An advertising delivery system according to an embodiment of the present invention delivers advertisements based on customer personalities and characteristics. The advertising delivery system repeatedly performs creative tests on copy and design, collects the results, and uses AI to analyze them to determine the customer's personalities and characteristics. For example, it continuously sends "advertisements emphasizing value" to people who respond to the phrase "Great Value!", and delivers "advertisements emphasizing the latest" to people who respond to the phrase "Latest." In this way, providing advertisements based on customer personalities and characteristics enables more effective marketing. For example, the advertising delivery system collects data such as click-through rates and conversion rates for each advertisement. For example, it displays an advertisement with the copy "Great Value!" and an advertisement with the copy "Latest" to different customer groups and compares their responses. Next, the advertising delivery system uses AI to analyze the collected data and determine the customer's personalities and characteristics. For example, it can determine that customers who respond well to the copy "Great Value!" tend to prioritize price. Furthermore, the advertising delivery system delivers advertisements based on the determined customer personalities and characteristics. For example, if a customer is resonating with the phrase "Great Deals!", they will continue to receive "Great Deals-Emphasis Ads," while if a customer is resonating with the phrase "Latest News," they will receive "Latest News-Emphasis Ads." This allows the ad delivery system to communicate based on the customer's personality and characteristics. By providing ads based on the customer's personality and characteristics, the ad delivery system enables more effective marketing. For example, by always providing great deals to price-conscious customers, customer satisfaction can be improved. Also, by always providing information on the latest products and services to customers who value the latest news, their interest can be maintained. This strengthens relationships with customers and builds long-term customer loyalty.

[0029] An advertisement delivery system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a delivery unit. The collection unit collects results of creative tests of copy and design. The collection unit collects data such as click rates and conversion rates for each advertisement. The collection unit can also collect users' dwell time and scrolling behavior. For example, the collection unit collects the click rates for each advertisement as well as the time users spent on an advertisement page. The collection unit can also record scrolling behavior on an advertisement page to determine which parts of the advertisement page interested the users. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the response patterns of each customer based on the collected data. For example, the analysis unit analyzes the response patterns of each customer based on data such as click rates, conversion rates, dwell time, and scrolling behavior. The determination unit determines the personality and characteristics of the customer based on the data analyzed by the analysis unit. For example, the determination unit determines the personality and characteristics of the customer based on the analyzed data. For example, the determination unit can determine that customers who respond well to the copy "Great Value!" tend to prioritize price. The distribution unit distributes advertisements based on the personality and characteristics of the customers determined by the determination unit. For example, the distribution unit distributes "advertisements that prioritize value" to customers who prioritize price, and "advertisements that prioritize the latest information" to customers who prioritize the latest information. For example, the distribution unit frequently distributes "advertisements that prioritize value" to customers who prioritize price. The distribution unit can also frequently distribute "advertisements that prioritize the latest information" to customers who prioritize the latest information. This makes it possible for the advertisement distribution system according to the embodiment to distribute advertisements based on the personality and characteristics of the customers.

[0030] The collection unit can collect specific data such as the click rate and conversion rate of each advertisement. For example, the collection unit collects the click rate of each advertisement. The click rate is calculated as the ratio between the number of clicks and the number of impressions. The collection unit can also collect the conversion rate of each advertisement. The conversion rate is calculated as the ratio between the number of conversions and the number of visitors. Furthermore, the collection unit can collect the user's stay time and scrolling behavior in addition to the click rate and conversion rate of each advertisement. For example, the collection unit collects the time the user spent on the advertisement page along with the click rate of each advertisement. The collection unit can also record scrolling behavior on the advertisement page to determine which parts the user was interested in. This allows for a detailed understanding of the effectiveness of the advertisement. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the click rate and conversion rate of each advertisement into the generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the response patterns of each customer based on the collected data. The analysis unit, for example, analyzes the response patterns of each customer based on the collected data. For example, the analysis unit analyzes the response patterns of each customer based on data such as click rate, conversion rate, dwell time, and scrolling behavior. The analysis unit can also analyze the collected data in real time to immediately grasp the response patterns. For example, the analysis unit can analyze the click rate of each advertisement in real time to immediately grasp the response patterns. The analysis unit can also analyze the conversion rate in real time to immediately grasp the response patterns. Furthermore, the analysis unit can analyze the user's dwell time and scrolling behavior in real time to immediately grasp the response patterns. This allows for detailed analysis of customer response patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the response patterns.

[0032] The determination unit can determine the customer's personality and characteristics based on the analyzed data. The determination unit, for example, determines the customer's personality and characteristics based on the analyzed data. For example, the determination unit can determine that customers who respond well to the copy "Great Deal!" tend to prioritize price. The determination unit can also determine changes in the customer's purchasing intent and interests based on the analyzed data. For example, the determination unit can determine that customers who show a high click rate have a high purchasing intent. The determination unit can also determine that customers who show a long stay time have a high interest. Furthermore, the determination unit can determine that customers who show a high conversion rate have a very high purchasing intent. This makes it possible to accurately determine the customer's personality and characteristics. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the analyzed data into a generation AI and cause the generation AI to determine the customer's personality and characteristics.

[0033] The distribution unit can deliver advertisements based on the determined customer's personality and characteristics. For example, the distribution unit delivers "advertisements that emphasize value" to price-conscious customers and "advertisements that emphasize the latest information" to customers who prioritize the latest information. For example, the distribution unit frequently delivers "advertisements that emphasize value" to price-conscious customers. The distribution unit can also frequently deliver "advertisements that emphasize the latest information" to customers who prioritize the latest information. Furthermore, the distribution unit can select the optimal advertisement by taking into account the customer's current purchasing phase when delivering an advertisement. For example, if a customer is highly motivated to purchase, the distribution unit delivers an advertisement that immediately encourages a purchase. Furthermore, if a customer is in the information gathering stage, the distribution unit can deliver an advertisement that provides detailed information. This makes it possible to deliver advertisements based on the customer's personality and characteristics. Some or all of the above-described processing by the distribution unit may be performed using, for example, AI, or may be performed without AI. For example, the distribution unit can input data on the determined customer's personality and characteristics into a generation AI and have the generation AI execute advertisement delivery.

[0034] The distribution unit can deliver "advertisements that emphasize value" to customers who prioritize price and "advertisements that emphasize the latest information" to customers who prioritize the latest information. For example, the distribution unit delivers "advertisements that emphasize value" to customers who prioritize price. Advertisements that emphasize value include, for example, discount information and special offers. The distribution unit can also deliver "advertisements that emphasize the latest information" to customers who prioritize the latest information. Advertisements that emphasize the latest information include, for example, new product information and the latest news. Furthermore, when delivering an advertisement, the distribution unit can select the optimal advertisement by taking into account the customer's current purchasing phase. For example, if a customer is highly motivated to purchase, the distribution unit delivers an advertisement that immediately encourages a purchase. Furthermore, if a customer is in the information gathering stage, the distribution unit can deliver an advertisement that provides detailed information. This makes it possible to deliver advertisements that are tailored to the customer's personality and characteristics. Some or all of the above-mentioned processing by the distribution unit may be performed, for example, using AI or without AI. For example, the distribution unit can input data on the determined customer's personality and characteristics into the generation AI and have the generation AI execute different types of advertisements.

[0035] The collection unit can collect not only the click rate and conversion rate of each advertisement, but also the user's dwell time and scrolling behavior. For example, the collection unit collects the click rate of each advertisement as well as the time the user spent on the advertisement page. The collection unit can also record scrolling behavior on the advertisement page to determine which parts the user was interested in. Furthermore, in addition to the conversion rate, the collection unit can also collect behavioral patterns of users after viewing an advertisement. For example, the collection unit records page transitions and purchase behavior after the user clicks on an advertisement. This allows the effectiveness of the advertisement to be evaluated from multiple perspectives. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the click rate, conversion rate, dwell time, and scrolling behavior of each advertisement into the generation AI and have the generation AI analyze the data.

[0036] The collection unit can collect data taking into account the display environment of the advertisement (device, browser, time of day, etc.). The collection unit, for example, records the device (smartphone, PC, etc.) on which the advertisement was displayed and collects responses for each device. The collection unit can also record the browser (Chrome, Safari, etc.) on which the advertisement was displayed and collect responses for each browser. The collection unit can also record the time period in which the advertisement was displayed and collect responses for each time period. For example, the collection unit analyzes user response patterns based on the time period in which the advertisement was displayed. This makes it possible to collect data according to the display environment of the advertisement. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input advertisement display environment data to a generation AI and have the generation AI analyze the data.

[0037] The collection unit can analyze the user's past advertising response history and select the optimal data collection method. For example, the collection unit analyzes patterns of advertisements to which the user has responded highly in the past and collects data using similar patterns. The collection unit can also prioritize collection of responses during specific time periods or on specific devices based on the user's past response history. Furthermore, the collection unit can select the optimal frequency and timing of data collection based on the user's past response history. For example, the collection unit adjusts the frequency of data collection based on the user's past response history. This enables optimal data collection based on the past response history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past advertising response history data into the generation AI and cause the generation AI to select the optimal data collection method.

[0038] The collection unit can collect data based on the display position and size of the advertisement. For example, the collection unit collects the click rate when the advertisement is displayed at the top of the page. The collection unit can also collect the dwell time when the advertisement is displayed at the bottom of the page. Furthermore, the collection unit can collect response data for each size of advertisement (banner, pop-up, etc.) based on the size of the advertisement. For example, the collection unit collects the click rate and dwell time based on the size of the advertisement. This makes it possible to collect data according to the display position and size of the advertisement. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the display position and size of the advertisement to the generation AI and have the generation AI analyze the data.

[0039] The collection unit also collects social media response data, allowing for a multifaceted evaluation of the effectiveness of the advertisement. The collection unit, for example, collects the number of shares on social media after the advertisement is displayed. The collection unit can also collect the number of comments and retweets on the advertisement to evaluate the response. Furthermore, the collection unit can collect the engagement rate on social media after the advertisement is displayed. For example, the collection unit collects the number of likes and shares on social media after the advertisement is displayed. This makes it possible to evaluate the effectiveness of the advertisement, including the social media response data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the social media response data into a generation AI and have the generation AI analyze the data.

[0040] The collection unit can prioritize collecting highly relevant data taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects response data to advertisements related to that area. The collection unit can also collect response data to advertisements related to nearby stores and services based on the user's location information. Furthermore, when the user is traveling, the collection unit can prioritize collecting response data to advertisements related to the user's travel destination. For example, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information. This enables data collection based on geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to a generation AI and have the generation AI analyze the data.

[0041] The analysis unit analyzes the collected data in real time and can grasp response patterns immediately. The analysis unit, for example, analyzes the click rate of each advertisement in real time and can grasp response patterns immediately. The analysis unit can also analyze the conversion rate in real time and can grasp response patterns immediately. Furthermore, the analysis unit can analyze the user's stay time and scrolling behavior in real time and can grasp response patterns immediately. This makes it possible to grasp response patterns in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI perform real-time analysis.

[0042] The analysis unit can also take into account past purchase history and browsing history when analyzing each customer's response pattern. The analysis unit, for example, analyzes response patterns based on each customer's past purchase history. The analysis unit can also analyze response patterns based on each customer's past browsing history. Furthermore, the analysis unit can analyze response patterns by integrating each customer's past purchase history and browsing history. This makes it possible to analyze response patterns taking into account past purchase history and browsing history. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on each customer's past purchase history and browsing history into the generation AI and cause the generation AI to analyze the response patterns.

[0043] The analysis unit can build a model that predicts the effectiveness of an advertisement based on the analysis results. For example, the analysis unit can build a model that predicts the click-through rate of an advertisement based on the collected data. The analysis unit can also build a model that predicts the conversion rate of an advertisement based on the collected data. Furthermore, the analysis unit can build a model that predicts the dwell time on an advertisement and scrolling behavior based on the collected data. This makes it possible to build a model that predicts the effectiveness of an advertisement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to build a model that predicts the effectiveness of an advertisement.

[0044] During analysis, the analysis unit can change the analysis method for different ad formats (video, banner, text, etc.). For example, in the case of video ads, the analysis unit performs analysis based on viewing time and number of plays. In addition, in the case of banner ads, the analysis unit can also perform analysis based on click rates and number of impressions. Furthermore, in the case of text ads, the analysis unit can also perform analysis based on click rates and conversion rates. This makes it possible to change the analysis method according to different ad formats. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of different ad formats into the generation AI and have the generation AI change the analysis method.

[0045] The analysis unit can integrate the analysis results with other marketing data (sales data, customer satisfaction data, etc.) and analyze them. For example, the analysis unit can integrate the analysis results with sales data to comprehensively evaluate the effectiveness of advertising. The analysis unit can also integrate the analysis results with customer satisfaction data to evaluate the impact of advertising. Furthermore, the analysis unit can integrate the analysis results with other marketing data to analyze the effectiveness of advertising from multiple angles. This enables comprehensive analysis integrated with other marketing data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results and other marketing data into a generation AI and have the generation AI perform an integrated analysis.

[0046] The analysis unit can build a feedback loop for improving the targeting accuracy of advertisements based on the analysis results. For example, the analysis unit can adjust an algorithm for improving the targeting accuracy based on the analysis results. The analysis unit can also adjust the timing and frequency of advertisement display based on the analysis results. Furthermore, the analysis unit can introduce a new data collection method for improving the targeting accuracy based on the analysis results. This makes it possible to build a feedback loop for improving the targeting accuracy of advertisements. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the analysis results to the generation AI and cause the generation AI to build a feedback loop.

[0047] The determination unit can determine changes in the customer's purchasing intent and interests based on the analyzed data. For example, the determination unit determines that a customer who shows a high click rate has a high purchasing intent. The determination unit can also determine that a customer who shows a long stay time has a high interest. Furthermore, the determination unit can determine that a customer who shows a high conversion rate has a very high purchasing intent. This makes it possible to accurately determine changes in the customer's purchasing intent and interests. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the analyzed data into a generation AI and cause the generation AI to determine changes in the customer's purchasing intent and interests.

[0048] The determination unit can also take into account past purchase history and feedback when determining a customer's personality and characteristics. The determination unit determines a customer's personality and characteristics based on, for example, past purchase history. The determination unit can also determine personality and characteristics based on feedback from the customer. Furthermore, the determination unit can determine personality and characteristics by integrating past purchase history and feedback. This makes it possible to determine a customer's personality and characteristics taking past purchase history and feedback into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on past purchase history and feedback into a generation AI and cause the generation AI to determine the customer's personality and characteristics.

[0049] The determination unit can predict a customer's life stage and purchasing cycle based on the determination result. The determination unit can predict a life stage based on, for example, the customer's personality and characteristics. The determination unit can also predict the customer's next purchasing cycle based on the customer's purchasing history. Furthermore, the determination unit can predict a life stage and purchasing cycle based on customer feedback. This makes it possible to predict a customer's life stage and purchasing cycle. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the determination result to a generation AI and cause the generation AI to predict a life stage and purchasing cycle.

[0050] The determination unit can also take into account specific social media activity data when determining a customer's personality and characteristics. For example, the determination unit analyzes the content of the customer's social media posts to determine the personality and characteristics. The determination unit can also analyze the customer's friendships on social media to determine the personality and characteristics. Furthermore, the determination unit can analyze the frequency of the customer's social media activities to determine the personality and characteristics. This makes it possible to determine a customer's personality and characteristics while taking social media activity data into consideration. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input social media activity data into the generation AI and cause the generation AI to determine the customer's personality and characteristics.

[0051] The determination unit can subdivide customer segments based on the determination results and perform more precise targeting. The determination unit creates subdivided segments based on, for example, customer personalities and characteristics. The determination unit can also create subdivided segments based on customer purchase histories. Furthermore, the determination unit can also create subdivided segments based on customer feedback. This enables subdividing customer segments and performing more precise targeting. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the determination results to a generation AI and cause the generation AI to perform segment subdivision.

[0052] The determination unit can integrate the determination result with other marketing data to create a comprehensive customer profile. The determination unit can, for example, integrate the determination result with sales data to create a comprehensive customer profile. The determination unit can also integrate the determination result with customer satisfaction data to create a comprehensive customer profile. The determination unit can also integrate the determination result with other marketing data to create a comprehensive customer profile. This makes it possible to create a comprehensive customer profile integrated with other marketing data. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input the determination result and other marketing data into a generation AI and cause the generation AI to create a customer profile.

[0053] The distribution unit can adjust the frequency of advertisement display based on the determined customer's personality and characteristics. For example, the distribution unit can frequently deliver "advertisements that emphasize value" to customers who prioritize price. The distribution unit can also frequently deliver "advertisements that emphasize the latest information" to customers who prioritize the latest information. Furthermore, the distribution unit can adjust the optimal display frequency based on the customer's personality and characteristics. For example, the distribution unit adjusts the number of times an advertisement is displayed per day or per week based on the customer's personality and characteristics. This makes it possible to adjust the advertisement display frequency based on the customer's personality and characteristics. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI, for example. For example, the distribution unit can input data on the determined customer's personality and characteristics into the generation AI and cause the generation AI to adjust the advertisement display frequency.

[0054] When distributing advertisements, the distribution unit can select the optimal advertisement by taking into account the customer's specific purchasing phase. For example, if the customer has a high desire to purchase, the distribution unit distributes an advertisement that immediately encourages the customer to purchase. The distribution unit can also distribute advertisements that provide detailed information if the customer is in the information gathering stage. Furthermore, the distribution unit can also distribute advertisements for after-sales service or related products if the customer is in the post-purchase stage. This makes it possible to select the optimal advertisement according to the customer's purchasing phase. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input data on the customer's purchasing phase into the generation AI and have the generation AI select the optimal advertisement.

[0055] The distribution unit can monitor the advertisement delivery results in real time and immediately adjust the delivery strategy. The distribution unit can, for example, monitor the advertisement click rate in real time and immediately adjust the delivery strategy. The distribution unit can also monitor the advertisement conversion rate in real time and immediately adjust the delivery strategy. Furthermore, the distribution unit can monitor the advertisement dwell time and scrolling behavior in real time and immediately adjust the delivery strategy. This makes it possible to adjust the advertisement delivery strategy in real time. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input advertisement delivery result data to the generation AI and cause the generation AI to adjust the delivery strategy.

[0056] When distributing advertisements, the distribution unit can select the optimal advertisement by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the distribution unit distributes advertisements related to that area. The distribution unit can also distribute advertisements for nearby stores and services based on the customer's location information. Furthermore, if the customer is traveling, the distribution unit can also distribute advertisements related to the customer's travel destination. This makes it possible to select the optimal advertisement based on the customer's geographical location information. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the customer's geographical location information data into the generation AI and cause the generation AI to select the optimal advertisement.

[0057] When delivering an advertisement, the distribution unit can select the optimal format by taking into consideration the customer's device information (smartphone, PC, etc.). For example, if the customer uses a smartphone, the distribution unit can deliver an advertisement optimized for the smartphone. Furthermore, if the customer uses a PC, the distribution unit can also deliver an advertisement optimized for the PC. Furthermore, if the customer uses a tablet, the distribution unit can also deliver an advertisement optimized for the tablet. This makes it possible to select the optimal advertisement format based on the customer's device information. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input customer device information data into the generation AI and cause the generation AI to select the optimal advertisement format.

[0058] The distribution unit can build a feedback loop for optimizing the next ad delivery strategy based on the ad delivery results. The distribution unit can optimize the next ad delivery strategy based on, for example, the ad click rate. The distribution unit can also optimize the next ad delivery strategy based on the ad conversion rate. Furthermore, the distribution unit can optimize the next ad delivery strategy based on the ad dwell time and scrolling behavior. This makes it possible to optimize the next ad delivery strategy based on the ad delivery results. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input ad delivery result data to the generation AI and cause the generation AI to optimize the next ad delivery strategy.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The advertising delivery system can also deliver advertisements by taking into account the user's purchasing history. For example, the collection unit collects data on products purchased in the past by the user, and the analysis unit analyzes the user's purchasing tendencies based on that data. The determination unit determines the user's purchasing willingness based on the analysis results, and the delivery unit delivers the most appropriate advertisement based on the determination results. This enables more precise targeting based on the user's past purchasing history. For example, the collection unit collects the categories and price ranges of products purchased in the past by the user, and the analysis unit analyzes the user's purchasing tendencies based on that data. The determination unit determines the user's purchasing willingness based on the analysis results, and the delivery unit delivers the most appropriate advertisement based on the determination results. This enables more precise targeting based on the user's past purchasing history.

[0061] The ad delivery system can also collect user activity data on social media and deliver targeted advertisements. For example, the collection unit collects content and comments shared by users on social media, and the analysis unit analyzes the user's interests based on that data. The determination unit determines the user's interests based on the analysis results, and the delivery unit delivers optimal advertisements based on the determination results. This enables more precise targeting based on user activity data on social media. For example, the collection unit collects content and comments shared by users on social media, and the analysis unit analyzes the user's interests based on that data. The determination unit determines the user's interests based on the analysis results, and the delivery unit delivers optimal advertisements based on the determination results. This enables more precise targeting based on user activity data on social media.

[0062] The advertisement delivery system can also deliver advertisements selectively taking into account the user's geographical location information. For example, the collection unit collects the user's current location information, and the analysis unit analyzes the user's behavioral patterns based on that data. The determination unit determines the user's behavioral patterns based on the analysis results, and the delivery unit delivers the most appropriate advertisement based on the determination results. This enables more precise targeting based on the user's geographical location information. For example, the collection unit collects the user's current location information, and the analysis unit analyzes the user's behavioral patterns based on that data. The determination unit determines the user's behavioral patterns based on the analysis results, and the delivery unit delivers the most appropriate advertisement based on the determination results. This enables more precise targeting based on the user's geographical location information.

[0063] The ad delivery system can also deliver ads based on user device information. For example, the collection unit collects the type of device used by the user (smartphone, PC, tablet, etc.), and the analysis unit analyzes the user's device usage trends based on that data. The determination unit determines the user's device usage trends based on the analysis results, and the delivery unit delivers the most appropriate ad based on that determination. This enables more precise targeting based on the user's device information. For example, the collection unit collects the type of device used by the user (smartphone, PC, tablet, etc.), and the analysis unit analyzes the user's device usage trends based on that data. The determination unit determines the user's device usage trends based on the analysis results, and the delivery unit delivers the most appropriate ad based on that determination. This enables more precise targeting based on the user's device information.

[0064] The ad delivery system can also deliver different ads by taking into account user browser information. For example, the collection unit collects the type of browser (Chrome, Safari, Firefox, etc.) used by the user, and the analysis unit analyzes the user's browser usage trends based on that data. The determination unit determines the user's browser usage trends based on the analysis results, and the delivery unit delivers the most appropriate ad based on that determination. This enables more precise targeting based on the user's browser information. For example, the collection unit collects the type of browser (Chrome, Safari, Firefox, etc.) used by the user, and the analysis unit analyzes the user's browser usage trends based on that data. The determination unit determines the user's browser usage trends based on the analysis results, and the delivery unit delivers the most appropriate ad based on that determination. This enables more precise targeting based on the user's browser information.

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

[0066] Step 1: The collection department collects the results of creative tests of copy and design. For example, it collects data such as the click-through rate and conversion rate of each ad, as well as the user's time spent on the ad and scrolling behavior. This allows it to understand how long users spent on the ad page and which parts of the page interested them. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the response patterns of each customer based on data such as click rate, conversion rate, dwell time, and scrolling behavior. Step 3: The determination unit determines the customer's personality and characteristics based on the data analyzed by the analysis unit. For example, it can determine that customers who respond well to the copy "Great Value!" tend to be price-conscious. Step 4: The distribution unit distributes advertisements based on the customer's personality and characteristics determined by the determination unit. For example, it distributes "advertisements that emphasize bargains" to customers who prioritize price, and "advertisements that emphasize the latest information" to customers who prioritize the latest information.

[0067] (Example 2) An advertising delivery system according to an embodiment of the present invention delivers advertisements based on customer personalities and characteristics. The advertising delivery system repeatedly performs creative tests on copy and design, collects the results, and uses AI to analyze them to determine the customer's personalities and characteristics. For example, it continuously sends "advertisements emphasizing value" to people who respond to the phrase "Great Value!", and delivers "advertisements emphasizing the latest" to people who respond to the phrase "Latest." In this way, providing advertisements based on customer personalities and characteristics enables more effective marketing. For example, the advertising delivery system collects data such as click-through rates and conversion rates for each advertisement. For example, it displays an advertisement with the copy "Great Value!" and an advertisement with the copy "Latest" to different customer groups and compares their responses. Next, the advertising delivery system uses AI to analyze the collected data and determine the customer's personalities and characteristics. For example, it can determine that customers who respond well to the copy "Great Value!" tend to prioritize price. Furthermore, the advertising delivery system delivers advertisements based on the determined customer personalities and characteristics. For example, if a customer is resonating with the phrase "Great Deals!", they will continue to receive "Great Deals-Emphasis Ads," while if a customer is resonating with the phrase "Latest News," they will receive "Latest News-Emphasis Ads." This allows the ad delivery system to communicate based on the customer's personality and characteristics. By providing ads based on the customer's personality and characteristics, the ad delivery system enables more effective marketing. For example, by always providing great deals to price-conscious customers, customer satisfaction can be improved. Also, by always providing information on the latest products and services to customers who value the latest news, their interest can be maintained. This strengthens relationships with customers and builds long-term customer loyalty.

[0068] An advertisement delivery system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a delivery unit. The collection unit collects results of creative tests of copy and design. The collection unit collects data such as click rates and conversion rates for each advertisement. The collection unit can also collect users' dwell time and scrolling behavior. For example, the collection unit collects the click rates for each advertisement as well as the time users spent on an advertisement page. The collection unit can also record scrolling behavior on an advertisement page to determine which parts of the advertisement page interested the users. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the response patterns of each customer based on the collected data. For example, the analysis unit analyzes the response patterns of each customer based on data such as click rates, conversion rates, dwell time, and scrolling behavior. The determination unit determines the personality and characteristics of the customer based on the data analyzed by the analysis unit. For example, the determination unit determines the personality and characteristics of the customer based on the analyzed data. For example, the determination unit can determine that customers who respond well to the copy "Great Value!" tend to prioritize price. The distribution unit distributes advertisements based on the personality and characteristics of the customers determined by the determination unit. For example, the distribution unit distributes "advertisements that prioritize value" to customers who prioritize price, and "advertisements that prioritize the latest information" to customers who prioritize the latest information. For example, the distribution unit frequently distributes "advertisements that prioritize value" to customers who prioritize price. The distribution unit can also frequently distribute "advertisements that prioritize the latest information" to customers who prioritize the latest information. This makes it possible for the advertisement distribution system according to the embodiment to distribute advertisements based on the personality and characteristics of the customers.

[0069] The collection unit can collect specific data such as the click rate and conversion rate of each advertisement. For example, the collection unit collects the click rate of each advertisement. The click rate is calculated as the ratio between the number of clicks and the number of impressions. The collection unit can also collect the conversion rate of each advertisement. The conversion rate is calculated as the ratio between the number of conversions and the number of visitors. Furthermore, the collection unit can collect the user's stay time and scrolling behavior in addition to the click rate and conversion rate of each advertisement. For example, the collection unit collects the time the user spent on the advertisement page along with the click rate of each advertisement. The collection unit can also record scrolling behavior on the advertisement page to determine which parts the user was interested in. This allows for a detailed understanding of the effectiveness of the advertisement. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the click rate and conversion rate of each advertisement into the generation AI and have the generation AI analyze the data.

[0070] The analysis unit can analyze the response patterns of each customer based on the collected data. The analysis unit, for example, analyzes the response patterns of each customer based on the collected data. For example, the analysis unit analyzes the response patterns of each customer based on data such as click rate, conversion rate, dwell time, and scrolling behavior. The analysis unit can also analyze the collected data in real time to immediately grasp the response patterns. For example, the analysis unit can analyze the click rate of each advertisement in real time to immediately grasp the response patterns. The analysis unit can also analyze the conversion rate in real time to immediately grasp the response patterns. Furthermore, the analysis unit can analyze the user's dwell time and scrolling behavior in real time to immediately grasp the response patterns. This allows for detailed analysis of customer response patterns. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI analyze the response patterns.

[0071] The determination unit can determine the customer's personality and characteristics based on the analyzed data. The determination unit, for example, determines the customer's personality and characteristics based on the analyzed data. For example, the determination unit can determine that customers who respond well to the copy "Great Deal!" tend to prioritize price. The determination unit can also determine changes in the customer's purchasing intent and interests based on the analyzed data. For example, the determination unit can determine that customers who show a high click rate have a high purchasing intent. The determination unit can also determine that customers who show a long stay time have a high interest. Furthermore, the determination unit can determine that customers who show a high conversion rate have a very high purchasing intent. This makes it possible to accurately determine the customer's personality and characteristics. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the analyzed data into a generation AI and cause the generation AI to determine the customer's personality and characteristics.

[0072] The distribution unit can deliver advertisements based on the determined customer's personality and characteristics. For example, the distribution unit delivers "advertisements that emphasize value" to price-conscious customers and "advertisements that emphasize the latest information" to customers who prioritize the latest information. For example, the distribution unit frequently delivers "advertisements that emphasize value" to price-conscious customers. The distribution unit can also frequently deliver "advertisements that emphasize the latest information" to customers who prioritize the latest information. Furthermore, the distribution unit can select the optimal advertisement by taking into account the customer's current purchasing phase when delivering an advertisement. For example, if a customer is highly motivated to purchase, the distribution unit delivers an advertisement that immediately encourages a purchase. Furthermore, if a customer is in the information gathering stage, the distribution unit can deliver an advertisement that provides detailed information. This makes it possible to deliver advertisements based on the customer's personality and characteristics. Some or all of the above-described processing by the distribution unit may be performed using, for example, AI, or may be performed without AI. For example, the distribution unit can input data on the determined customer's personality and characteristics into a generation AI and have the generation AI execute advertisement delivery.

[0073] The distribution unit can deliver "advertisements that emphasize value" to customers who prioritize price and "advertisements that emphasize the latest information" to customers who prioritize the latest information. For example, the distribution unit delivers "advertisements that emphasize value" to customers who prioritize price. Advertisements that emphasize value include, for example, discount information and special offers. The distribution unit can also deliver "advertisements that emphasize the latest information" to customers who prioritize the latest information. Advertisements that emphasize the latest information include, for example, new product information and the latest news. Furthermore, when delivering an advertisement, the distribution unit can select the optimal advertisement by taking into account the customer's current purchasing phase. For example, if a customer is highly motivated to purchase, the distribution unit delivers an advertisement that immediately encourages a purchase. Furthermore, if a customer is in the information gathering stage, the distribution unit can deliver an advertisement that provides detailed information. This makes it possible to deliver advertisements that are tailored to the customer's personality and characteristics. Some or all of the above-mentioned processing by the distribution unit may be performed, for example, using AI or without AI. For example, the distribution unit can input data on the determined customer's personality and characteristics into the generation AI and have the generation AI execute different types of advertisements.

[0074] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, when the user is excited, the collection unit collects data in real time to immediately grasp the user's reaction. Furthermore, when the user is relaxed, the collection unit can collect data at regular intervals to observe the user's reaction over a long period of time. Furthermore, when the user is stressed, the collection unit can reduce the frequency of data collection to reduce the user's burden. This enables the timing of data collection to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the timing of data collection.

[0075] The collection unit can collect not only the click rate and conversion rate of each advertisement, but also the user's dwell time and scrolling behavior. For example, the collection unit collects the click rate of each advertisement as well as the time the user spent on the advertisement page. The collection unit can also record scrolling behavior on the advertisement page to determine which parts the user was interested in. Furthermore, in addition to the conversion rate, the collection unit can also collect behavioral patterns of users after viewing an advertisement. For example, the collection unit records page transitions and purchase behavior after the user clicks on an advertisement. This allows the effectiveness of the advertisement to be evaluated from multiple perspectives. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the click rate, conversion rate, dwell time, and scrolling behavior of each advertisement into the generation AI and have the generation AI analyze the data.

[0076] The collection unit can collect data taking into account the display environment of the advertisement (device, browser, time of day, etc.). The collection unit, for example, records the device (smartphone, PC, etc.) on which the advertisement was displayed and collects responses for each device. The collection unit can also record the browser (Chrome, Safari, etc.) on which the advertisement was displayed and collect responses for each browser. The collection unit can also record the time period in which the advertisement was displayed and collect responses for each time period. For example, the collection unit analyzes user response patterns based on the time period in which the advertisement was displayed. This makes it possible to collect data according to the display environment of the advertisement. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input advertisement display environment data to a generation AI and have the generation AI analyze the data.

[0077] The collection unit can analyze the user's past advertising response history and select the optimal data collection method. For example, the collection unit analyzes patterns of advertisements to which the user has responded highly in the past and collects data using similar patterns. The collection unit can also prioritize collection of responses during specific time periods or on specific devices based on the user's past response history. Furthermore, the collection unit can select the optimal frequency and timing of data collection based on the user's past response history. For example, the collection unit adjusts the frequency of data collection based on the user's past response history. This enables optimal data collection based on the past response history. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past advertising response history data into the generation AI and cause the generation AI to select the optimal data collection method.

[0078] The collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. For example, if the user is excited, the collection unit can prioritize collecting click rates and conversion rates. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data on dwell time and scrolling behavior. Furthermore, if the user is stressed, the collection unit can prioritize collecting data related to the advertisement display environment. This enables prioritization of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI prioritize the data.

[0079] The collection unit can collect data based on the display position and size of the advertisement. For example, the collection unit collects the click rate when the advertisement is displayed at the top of the page. The collection unit can also collect the dwell time when the advertisement is displayed at the bottom of the page. Furthermore, the collection unit can collect response data for each size of advertisement (banner, pop-up, etc.) based on the size of the advertisement. For example, the collection unit collects the click rate and dwell time based on the size of the advertisement. This makes it possible to collect data according to the display position and size of the advertisement. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on the display position and size of the advertisement to the generation AI and have the generation AI analyze the data.

[0080] The collection unit also collects social media response data, allowing for a multifaceted evaluation of the effectiveness of the advertisement. The collection unit, for example, collects the number of shares on social media after the advertisement is displayed. The collection unit can also collect the number of comments and retweets on the advertisement to evaluate the response. Furthermore, the collection unit can collect the engagement rate on social media after the advertisement is displayed. For example, the collection unit collects the number of likes and shares on social media after the advertisement is displayed. This makes it possible to evaluate the effectiveness of the advertisement, including the social media response data. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the social media response data into a generation AI and have the generation AI analyze the data.

[0081] The collection unit can prioritize collecting highly relevant data taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit collects response data to advertisements related to that area. The collection unit can also collect response data to advertisements related to nearby stores and services based on the user's location information. Furthermore, when the user is traveling, the collection unit can prioritize collecting response data to advertisements related to the user's travel destination. For example, the collection unit prioritizes collecting highly relevant data based on the user's geographical location information. This enables data collection based on geographical location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information data to a generation AI and have the generation AI analyze the data.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user emotions. For example, when the user is excited, the analysis unit analyzes data in real time to instantly understand the reaction pattern. Furthermore, when the user is relaxed, the analysis unit can analyze long-term data to understand a stable reaction pattern. Furthermore, when the user is stressed, the analysis unit can reduce the frequency of data analysis to reduce the user's burden. This enables the analysis algorithm to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the analysis algorithm.

[0083] The analysis unit analyzes the collected data in real time and can grasp response patterns immediately. The analysis unit, for example, analyzes the click rate of each advertisement in real time and can grasp response patterns immediately. The analysis unit can also analyze the conversion rate in real time and can grasp response patterns immediately. Furthermore, the analysis unit can analyze the user's stay time and scrolling behavior in real time and can grasp response patterns immediately. This makes it possible to grasp response patterns in real time. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data to a generation AI and have the generation AI perform real-time analysis.

[0084] The analysis unit can also take into account past purchase history and browsing history when analyzing each customer's response pattern. The analysis unit, for example, analyzes response patterns based on each customer's past purchase history. The analysis unit can also analyze response patterns based on each customer's past browsing history. Furthermore, the analysis unit can analyze response patterns by integrating each customer's past purchase history and browsing history. This makes it possible to analyze response patterns taking into account past purchase history and browsing history. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on each customer's past purchase history and browsing history into the generation AI and cause the generation AI to analyze the response patterns.

[0085] The analysis unit can build a model that predicts the effectiveness of an advertisement based on the analysis results. For example, the analysis unit can build a model that predicts the click-through rate of an advertisement based on the collected data. The analysis unit can also build a model that predicts the conversion rate of an advertisement based on the collected data. Furthermore, the analysis unit can build a model that predicts the dwell time on an advertisement and scrolling behavior based on the collected data. This makes it possible to build a model that predicts the effectiveness of an advertisement. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI and cause the generation AI to build a model that predicts the effectiveness of an advertisement.

[0086] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. Furthermore, if the user is relaxed, the analysis unit can provide a calming display method. Furthermore, if the user is stressed, the analysis unit can provide a simple, highly visible display method. This enables the display method of the analysis results to be adjusted according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0087] During analysis, the analysis unit can change the analysis method for different ad formats (video, banner, text, etc.). For example, in the case of video ads, the analysis unit performs analysis based on viewing time and number of plays. In addition, in the case of banner ads, the analysis unit can also perform analysis based on click rates and number of impressions. Furthermore, in the case of text ads, the analysis unit can also perform analysis based on click rates and conversion rates. This makes it possible to change the analysis method according to different ad formats. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data of different ad formats into the generation AI and have the generation AI change the analysis method.

[0088] The analysis unit can integrate the analysis results with other marketing data (sales data, customer satisfaction data, etc.) and analyze them. For example, the analysis unit can integrate the analysis results with sales data to comprehensively evaluate the effectiveness of advertising. The analysis unit can also integrate the analysis results with customer satisfaction data to evaluate the impact of advertising. Furthermore, the analysis unit can integrate the analysis results with other marketing data to analyze the effectiveness of advertising from multiple angles. This enables comprehensive analysis integrated with other marketing data. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the analysis results and other marketing data into a generation AI and have the generation AI perform an integrated analysis.

[0089] The analysis unit can build a feedback loop for improving the targeting accuracy of advertisements based on the analysis results. For example, the analysis unit can adjust an algorithm for improving the targeting accuracy based on the analysis results. The analysis unit can also adjust the timing and frequency of advertisement display based on the analysis results. Furthermore, the analysis unit can introduce a new data collection method for improving the targeting accuracy based on the analysis results. This makes it possible to build a feedback loop for improving the targeting accuracy of advertisements. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit can input the analysis results to the generation AI and cause the generation AI to build a feedback loop.

[0090] The determination unit can estimate the user's emotions and determine the customer's personality and characteristics based on the estimated user emotions. For example, if the user is excited, the determination unit can determine that the user has an aggressive personality. If the user is relaxed, the determination unit can also determine that the user has a calm personality. Furthermore, if the user is stressed, the determination unit can also determine that the user has a cautious personality. This makes it possible to determine the customer's personality and characteristics based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and have the generation AI determine the customer's personality and characteristics.

[0091] The determination unit can determine changes in the customer's purchasing intent and interests based on the analyzed data. For example, the determination unit determines that a customer who shows a high click rate has a high purchasing intent. The determination unit can also determine that a customer who shows a long stay time has a high interest. Furthermore, the determination unit can determine that a customer who shows a high conversion rate has a very high purchasing intent. This makes it possible to accurately determine changes in the customer's purchasing intent and interests. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the analyzed data into a generation AI and cause the generation AI to determine changes in the customer's purchasing intent and interests.

[0092] The determination unit can also take into account past purchase history and feedback when determining a customer's personality and characteristics. The determination unit determines a customer's personality and characteristics based on, for example, past purchase history. The determination unit can also determine personality and characteristics based on feedback from the customer. Furthermore, the determination unit can determine personality and characteristics by integrating past purchase history and feedback. This makes it possible to determine a customer's personality and characteristics taking past purchase history and feedback into consideration. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input data on past purchase history and feedback into a generation AI and cause the generation AI to determine the customer's personality and characteristics.

[0093] The determination unit can predict a customer's life stage and purchasing cycle based on the determination result. The determination unit can predict a life stage based on, for example, the customer's personality and characteristics. The determination unit can also predict the customer's next purchasing cycle based on the customer's purchasing history. Furthermore, the determination unit can predict a life stage and purchasing cycle based on customer feedback. This makes it possible to predict a customer's life stage and purchasing cycle. Some or all of the above-mentioned processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the determination result to a generation AI and cause the generation AI to predict a life stage and purchasing cycle.

[0094] The determination unit can estimate the user's emotion and adjust the display method of the determination result based on the estimated user emotion. For example, if the user is excited, the determination unit can provide a visually stimulating display method. Furthermore, if the user is relaxed, the determination unit can provide a calm display method. Furthermore, if the user is stressed, the determination unit can provide a simple, highly visible display method. This makes it possible to adjust the display method of the determination result according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the determination unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the determination unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the determination result.

[0095] The determination unit can also take into account specific social media activity data when determining a customer's personality and characteristics. For example, the determination unit analyzes the content of the customer's social media posts to determine the personality and characteristics. The determination unit can also analyze the customer's friendships on social media to determine the personality and characteristics. Furthermore, the determination unit can analyze the frequency of the customer's social media activities to determine the personality and characteristics. This makes it possible to determine a customer's personality and characteristics while taking social media activity data into consideration. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input social media activity data into the generation AI and cause the generation AI to determine the customer's personality and characteristics.

[0096] The determination unit can subdivide customer segments based on the determination results and perform more precise targeting. The determination unit creates subdivided segments based on, for example, customer personalities and characteristics. The determination unit can also create subdivided segments based on customer purchase histories. Furthermore, the determination unit can also create subdivided segments based on customer feedback. This enables subdividing customer segments and performing more precise targeting. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the determination results to a generation AI and cause the generation AI to perform segment subdivision.

[0097] The determination unit can integrate the determination result with other marketing data to create a comprehensive customer profile. The determination unit can, for example, integrate the determination result with sales data to create a comprehensive customer profile. The determination unit can also integrate the determination result with customer satisfaction data to create a comprehensive customer profile. The determination unit can also integrate the determination result with other marketing data to create a comprehensive customer profile. This makes it possible to create a comprehensive customer profile integrated with other marketing data. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit can input the determination result and other marketing data into a generation AI and cause the generation AI to create a customer profile.

[0098] The distribution unit can estimate the user's emotions and adjust the timing of advertisement delivery based on the estimated user emotions. For example, if the user is excited, the distribution unit can immediately deliver an advertisement. Furthermore, if the user is relaxed, the distribution unit can also deliver an advertisement at regular time intervals. Furthermore, if the user is feeling stressed, the distribution unit can reduce the frequency of advertisement delivery to reduce the burden on the user. This makes it possible to adjust the timing of advertisement delivery according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the distribution unit can input user emotion data into the generation AI and cause the generation AI to adjust the timing of advertisement delivery.

[0099] The distribution unit can adjust the frequency of advertisement display based on the determined customer's personality and characteristics. For example, the distribution unit can frequently deliver "advertisements that emphasize value" to customers who prioritize price. The distribution unit can also frequently deliver "advertisements that emphasize the latest information" to customers who prioritize the latest information. Furthermore, the distribution unit can adjust the optimal display frequency based on the customer's personality and characteristics. For example, the distribution unit adjusts the number of times an advertisement is displayed per day or per week based on the customer's personality and characteristics. This makes it possible to adjust the advertisement display frequency based on the customer's personality and characteristics. Some or all of the above-described processing in the distribution unit may be performed using, or without, AI, for example. For example, the distribution unit can input data on the determined customer's personality and characteristics into the generation AI and cause the generation AI to adjust the advertisement display frequency.

[0100] When distributing advertisements, the distribution unit can select the optimal advertisement by taking into account the customer's specific purchasing phase. For example, if the customer has a high desire to purchase, the distribution unit distributes an advertisement that immediately encourages the customer to purchase. The distribution unit can also distribute advertisements that provide detailed information if the customer is in the information gathering stage. Furthermore, the distribution unit can also distribute advertisements for after-sales service or related products if the customer is in the post-purchase stage. This makes it possible to select the optimal advertisement according to the customer's purchasing phase. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input data on the customer's purchasing phase into the generation AI and have the generation AI select the optimal advertisement.

[0101] The distribution unit can monitor the advertisement delivery results in real time and immediately adjust the delivery strategy. The distribution unit can, for example, monitor the advertisement click rate in real time and immediately adjust the delivery strategy. The distribution unit can also monitor the advertisement conversion rate in real time and immediately adjust the delivery strategy. Furthermore, the distribution unit can monitor the advertisement dwell time and scrolling behavior in real time and immediately adjust the delivery strategy. This makes it possible to adjust the advertisement delivery strategy in real time. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input advertisement delivery result data to the generation AI and cause the generation AI to adjust the delivery strategy.

[0102] The distribution unit can estimate the user's emotions and adjust the advertisement display method based on the estimated user emotions. For example, if the user is excited, the distribution unit can provide a visually stimulating display method. Furthermore, if the user is relaxed, the distribution unit can provide a calming display method. Furthermore, if the user is stressed, the distribution unit can provide a simple, highly visible display method. This makes it possible to adjust the advertisement display method according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the distribution unit may be performed using AI, for example, or without AI. For example, the distribution unit can input user emotion data into the generation AI and cause the generation AI to adjust the advertisement display method.

[0103] When distributing advertisements, the distribution unit can select the optimal advertisement by taking into account the customer's geographical location information. For example, if the customer is in a specific area, the distribution unit distributes advertisements related to that area. The distribution unit can also distribute advertisements for nearby stores and services based on the customer's location information. Furthermore, if the customer is traveling, the distribution unit can also distribute advertisements related to the customer's travel destination. This makes it possible to select the optimal advertisement based on the customer's geographical location information. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input the customer's geographical location information data into the generation AI and cause the generation AI to select the optimal advertisement.

[0104] When delivering an advertisement, the distribution unit can select the optimal format by taking into consideration the customer's device information (smartphone, PC, etc.). For example, if the customer uses a smartphone, the distribution unit can deliver an advertisement optimized for the smartphone. Furthermore, if the customer uses a PC, the distribution unit can also deliver an advertisement optimized for the PC. Furthermore, if the customer uses a tablet, the distribution unit can also deliver an advertisement optimized for the tablet. This makes it possible to select the optimal advertisement format based on the customer's device information. Some or all of the above-described processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input customer device information data into the generation AI and cause the generation AI to select the optimal advertisement format.

[0105] The distribution unit can build a feedback loop for optimizing the next ad delivery strategy based on the ad delivery results. The distribution unit can optimize the next ad delivery strategy based on, for example, the ad click rate. The distribution unit can also optimize the next ad delivery strategy based on the ad conversion rate. Furthermore, the distribution unit can optimize the next ad delivery strategy based on the ad dwell time and scrolling behavior. This makes it possible to optimize the next ad delivery strategy based on the ad delivery results. Some or all of the above-mentioned processing in the distribution unit may be performed using, for example, AI, or may be performed without using AI. For example, the distribution unit can input ad delivery result data to the generation AI and cause the generation AI to optimize the next ad delivery strategy. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and distribution unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 38B of the smart device 14 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the personality and characteristics of the customer based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the smart device 14 and distributes advertisements based on the determined personality and characteristics of the customer. === Hard Collateral 1-2 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and distribution unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the customer's personality and characteristics based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the smart glasses 214 and distributes advertisements based on the determined customer's personality and characteristics. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and distribution unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the headset type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the personality and characteristics of the customer based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the headset type terminal 314 and distributes advertisements based on the determined personality and characteristics of the customer. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, determination unit, and distribution unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects user behavior data using the camera 42 and microphone 238 of the robot 414 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and determines the personality and characteristics of the customer based on the analysis results. The distribution unit is realized, for example, by the control unit 46A of the robot 414 and distributes advertisements based on the determined personality and characteristics of the customer.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The advertising delivery system can also deliver advertisements by taking into account the user's purchasing history. For example, the collection unit collects data on products purchased in the past by the user, and the analysis unit analyzes the user's purchasing tendencies based on that data. The determination unit determines the user's purchasing willingness based on the analysis results, and the delivery unit delivers the most appropriate advertisement based on the determination results. This enables more precise targeting based on the user's past purchasing history. For example, the collection unit collects the categories and price ranges of products purchased in the past by the user, and the analysis unit analyzes the user's purchasing tendencies based on that data. The determination unit determines the user's purchasing willingness based on the analysis results, and the delivery unit delivers the most appropriate advertisement based on the determination results. This enables more precise targeting based on the user's past purchasing history.

[0108] The ad delivery system can also collect user activity data on social media and deliver targeted advertisements. For example, the collection unit collects content and comments shared by users on social media, and the analysis unit analyzes the user's interests based on that data. The determination unit determines the user's interests based on the analysis results, and the delivery unit delivers optimal advertisements based on the determination results. This enables more precise targeting based on user activity data on social media. For example, the collection unit collects content and comments shared by users on social media, and the analysis unit analyzes the user's interests based on that data. The determination unit determines the user's interests based on the analysis results, and the delivery unit delivers optimal advertisements based on the determination results. This enables more precise targeting based on user activity data on social media.

[0109] The advertisement delivery system can also deliver advertisements selectively taking into account the user's geographical location information. For example, the collection unit collects the user's current location information, and the analysis unit analyzes the user's behavioral patterns based on that data. The determination unit determines the user's behavioral patterns based on the analysis results, and the delivery unit delivers the most appropriate advertisement based on the determination results. This enables more precise targeting based on the user's geographical location information. For example, the collection unit collects the user's current location information, and the analysis unit analyzes the user's behavioral patterns based on that data. The determination unit determines the user's behavioral patterns based on the analysis results, and the delivery unit delivers the most appropriate advertisement based on the determination results. This enables more precise targeting based on the user's geographical location information.

[0110] The ad delivery system can also deliver ads based on user device information. For example, the collection unit collects the type of device used by the user (smartphone, PC, tablet, etc.), and the analysis unit analyzes the user's device usage trends based on that data. The determination unit determines the user's device usage trends based on the analysis results, and the delivery unit delivers the most appropriate ad based on that determination. This enables more precise targeting based on the user's device information. For example, the collection unit collects the type of device used by the user (smartphone, PC, tablet, etc.), and the analysis unit analyzes the user's device usage trends based on that data. The determination unit determines the user's device usage trends based on the analysis results, and the delivery unit delivers the most appropriate ad based on that determination. This enables more precise targeting based on the user's device information.

[0111] The ad delivery system can also deliver different ads by taking into account user browser information. For example, the collection unit collects the type of browser (Chrome, Safari, Firefox, etc.) used by the user, and the analysis unit analyzes the user's browser usage trends based on that data. The determination unit determines the user's browser usage trends based on the analysis results, and the delivery unit delivers the most appropriate ad based on that determination. This enables more precise targeting based on the user's browser information. For example, the collection unit collects the type of browser (Chrome, Safari, Firefox, etc.) used by the user, and the analysis unit analyzes the user's browser usage trends based on that data. The determination unit determines the user's browser usage trends based on the analysis results, and the delivery unit delivers the most appropriate ad based on that determination. This enables more precise targeting based on the user's browser information.

[0112] The advertisement delivery system can further estimate a user's emotions and dynamically change the content of the advertisement based on the estimated emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit dynamically changes the content of the advertisement based on the determination results. This enables more effective advertisement delivery in accordance with the user's emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit dynamically changes the content of the advertisement based on the determination results. This enables more effective advertisement delivery in accordance with the user's emotions.

[0113] The advertisement delivery system can further estimate a user's emotions and adjust the timing of advertisement display based on the estimated emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit adjusts the timing of advertisement display based on the determination results. This enables more effective advertisement delivery in accordance with the user's emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit adjusts the timing of advertisement display based on the determination results. This enables more effective advertisement delivery in accordance with the user's emotions.

[0114] The advertisement delivery system can further estimate a user's emotions and adjust the advertisement display method based on the estimated emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit adjusts the advertisement display method based on the determination results. This enables more effective advertisement delivery in accordance with the user's emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit adjusts the advertisement display method based on the determination results. This enables more effective advertisement delivery in accordance with the user's emotions.

[0115] The advertisement delivery system can further estimate a user's emotions and adjust the frequency of advertisement display based on the estimated emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit adjusts the advertisement display frequency based on the determination results. This enables more effective advertisement delivery in accordance with the user's emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit adjusts the advertisement display frequency based on the determination results. This enables more effective advertisement delivery in accordance with the user's emotions.

[0116] The advertisement delivery system can further estimate a user's emotions and personalize the advertisement content based on the estimated emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit personalizes the advertisement content based on the determination results. This enables more effective advertisement delivery according to the user's emotions. For example, the collection unit collects user emotion data, and the analysis unit analyzes the user's emotions based on the data. The determination unit determines the user's emotions based on the analysis results, and the delivery unit personalizes the advertisement content based on the determination results. This enables more effective advertisement delivery according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The collection department collects the results of creative tests of copy and design. For example, it collects data such as the click-through rate and conversion rate of each ad, as well as the user's time spent on the ad and scrolling behavior. This allows it to understand how long users spent on the ad page and which parts of the page interested them. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes the response patterns of each customer based on data such as click rate, conversion rate, dwell time, and scrolling behavior. Step 3: The determination unit determines the customer's personality and characteristics based on the data analyzed by the analysis unit. For example, it can determine that customers who respond well to the copy "Great Value!" tend to be price-conscious. Step 4: The distribution unit distributes advertisements based on the customer's personality and characteristics determined by the determination unit. For example, it distributes "advertisements that emphasize bargains" to customers who prioritize price, and "advertisements that emphasize the latest information" to customers who prioritize the latest information.

[0119] 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.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects results of the creative tests; an analysis unit that analyzes the data collected by the collection unit; a determination unit that determines specific characteristics and traits of the customer based on the data analyzed by the analysis unit; a distribution unit that distributes advertisements based on the specific characteristics and features of the customer determined by the determination unit; Equipped with A system characterized by:

2. The collecting unit Collect specific data such as click-through rates and conversion rates for each ad 2. The system of claim 1.

3. The analysis unit Analyze each customer's response patterns based on the collected data 2. The system of claim 1.

4. The determination unit Determine customer characteristics and traits based on analyzed data 2. The system of claim 1.

5. The distribution unit Displaying advertisements based on the determined customer's personality and characteristics 2. The system of claim 1.

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

7. The collecting unit Collect click-through rates and conversion rates for each ad, as well as user dwell time and scrolling behavior.

2. The system of claim 1.

Images