System

Abstract

To provide a system which provides an optimal promotion on the basis of preference or behavior pattern of a customer.SOLUTION: A system comprises a collection part, an analysis part, a generation part, and a distribution part. The collection part collects data related to behavior history or preference of a customer. The analysis part analyzes the data collected by the collection part and grasps the preference or behavior pattern of the customer. The generation part determines an optimal promotion on the basis of an analysis result obtained by the analysis part and generates a content. The distribution part distributes the content generated by the generation 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 a description and related instruction sentence regarding 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] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of not being able to adequately provide optimal promotions in real time based on customer preferences and behavioral patterns.

[0005] The system according to the embodiment aims to provide optimal promotions based on the preferences and behavioral patterns of customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a distribution unit. The collection unit collects data related to customer behavioral history or preferences. The analysis unit analyzes the data collected by the collection unit to understand the customer preferences and behavioral patterns. The generation unit determines appropriate promotions and generates content based on the analysis results obtained by the analysis unit. The distribution unit distributes the content generated by the generation unit. Effect of the Invention

[0007] The system according to the embodiment can provide optimal promotions based on the preferences and behavioral patterns of customers. [Brief description 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. [Diagram 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. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 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. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 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 PREFERRED EMBODIMENTS

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

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

[0011] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one 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 signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs and 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 code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or 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. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "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, the RAM 30, and the 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 (e.g., 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., voice and / or text). Display 40A displays visible information such as text and images according to instructions from processor 46. Speaker 40B outputs voice according to instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

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

[0024] As shown in Fig. 2, 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. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process 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 process program 56 executed on the RAM 30.

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

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the 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. Also, the data processing device 12 may be a server device, or may be a terminal device (e.g., a mobile phone, a robot, a home appliance, etc.) owned by a user. Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The advertisement delivery system according to the embodiment of the present invention is a system that analyzes a large amount of data and delivers optimal advertisements based on the preferences and behavioral patterns of individual customers. This advertisement delivery system collects data on the behavioral history and preferences of customers, and the AI ​​analyzes the data to grasp the preferences of the customers in real time. Based on this, the AI ​​determines the optimal promotion and generates content such as videos, images, and comics. Furthermore, the AI ​​analyzes the behavioral history of the customers, determines the delivery method (email, website, in-app, etc.) and time period with the highest probability of viewing, and implements the promotion. For example, the advertisement delivery system collects detailed data such as what products the customers viewed, which pages they visited, and which links they clicked. This makes it possible to grasp the preferences and behavioral patterns of the customers. Next, the AI ​​analyzes the collected data and grasps the preferences and behavioral patterns of the customers. For example, it is possible to identify customers who are highly interested in a specific product and propose an appropriate promotion to the customer. This makes it possible to provide personalized promotions tailored to the needs of the customers. Furthermore, the AI ​​determines the optimal promotion and generates content such as videos, images, and comics. For example, it is possible to generate content such as videos, images, and comics related to a specific product for customers who are highly interested in the product. This can attract customer interest and increase the effectiveness of promotions. Finally, AI analyzes the customer's behavioral history, determines the delivery method and time period with the highest probability of viewing, and then implements the promotion. For example, if a customer tends to check email at a specific time of day, promotional emails can be sent at that time. Also, if a customer frequently uses a specific website or app, promotions can be implemented on that platform. This maximizes the effectiveness of promotions. This allows the ad delivery system to provide optimal promotions based on the customer's behavioral history and preferences.

[0029] The advertisement delivery system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a delivery unit. The collection unit collects data on the behavioral history and preferences of the customer. The data on the behavioral history and preferences of the customer includes, but is not limited to, purchase history, browsing history, click history, and the like. The collection unit collects, for example, data on products viewed by the customer, pages visited, and links clicked. For example, on an e-commerce site, data is collected on which products the customer viewed and which products the customer added to a cart. This makes it possible to grasp the customer's preferences and behavioral patterns. The analysis unit analyzes the data collected by the collection unit and grasps the customer's preferences and behavioral patterns. For example, the analysis unit analyzes the collected data and grasps the customer's preferences and behavioral patterns. For example, it is possible to identify a customer who is highly interested in a specific product and propose an appropriate promotion to the customer. This makes it possible to provide personalized promotions tailored to the needs of the customer. The generation unit determines an optimal promotion based on the analysis results obtained by the analysis unit and generates content. The generating unit generates content such as videos, images, and cartoons related to a specific product for a customer who is highly interested in the product. For example, the generating unit can generate content such as videos, images, and cartoons related to a specific product for a customer who is highly interested in the product. This can attract the customer's interest and increase the effectiveness of the promotion. The distribution unit distributes the content generated by the generating unit. For example, the distribution unit analyzes the customer's behavior history, determines the distribution method and time period with the highest probability of viewing, and implements the promotion. For example, if a customer tends to check email during a specific time period, a promotion email can be sent during that time period. Also, if a customer frequently uses a specific website or app, a promotion can be implemented on that platform. This can maximize the effectiveness of the promotion. This allows the advertisement distribution system according to the embodiment to provide optimal promotion based on the customer's behavior history and preferences.

[0030] The collection unit collects data on the behavioral history and preferences of customers. Specifically, the collection unit collects data such as the purchase history, browsing history, and click history of the customers' online purchases. For example, on an e-commerce site, detailed data such as which products the customers viewed, which products they added to their cart, and even which products they purchased are collected. This allows the customer's preferences and behavioral patterns to be understood in detail. The collection unit uses website tracking tools and cookies to monitor the customer's behavior in real time and collect data. In addition, the collection unit can collect information on the device and browser used by the customer to obtain more accurate data. In addition, the collection unit can collect data from other sources such as social media activity and survey results. This allows the collection unit to understand not only the customer's online behavior but also their offline preferences and behavioral patterns. The collected data is stored in a secure database and used for subsequent analysis and promotion. The collection unit can flexibly adjust the frequency and scope of data collection, and can optimally collect data in accordance with a specific campaign or promotion. This allows the collection unit to efficiently and effectively collect data on the customer's behavioral history and preferences, and improve the performance of the entire system.

[0031] The analysis unit analyzes the data collected by the collection unit to grasp the customer's preferences and behavioral patterns. Specifically, the analysis unit analyzes the collected data to identify what products the customer is interested in and what behavioral patterns the customer has. For example, the analysis unit can identify customers who are highly interested in a specific product and propose appropriate promotions to the customers. The analysis unit uses machine learning algorithms and data mining technology to extract useful information from a huge amount of data. For example, the analysis unit uses a clustering algorithm to group customers based on their preferences and behavioral patterns and develops optimal promotion strategies for each group. In addition, the analysis unit can predict future customer behavior based on past data and optimize the timing and content of promotions. Furthermore, the analysis unit can use an anomaly detection algorithm to identify customers who have behavioral patterns that are different from normal and provide special support. This allows the analysis unit to provide a foundation for grasping customer preferences and behavioral patterns in detail and realizing personalized promotions.

[0032] The generation unit determines the optimal promotion and generates the content based on the analysis results obtained by the analysis unit. Specifically, for customers who are highly interested in a particular product, the generation unit generates content such as videos, images, and comics related to the product. The generation unit can automatically generate content that attracts the interest of customers using the generation AI. For example, the generation AI generates optimal promotional messages based on the customer's preferences and behavior patterns, and provides them in the form of videos, images, comics, and the like. The generation AI can generate effective messages for customers and create visually appealing content using natural language processing technology. Furthermore, the generation unit can evaluate the effectiveness of the generated content and modify the content as necessary. For example, if the generated content does not elicit a response from customers, the generation unit can change the content or format of the content and generate it again. In this way, the generation unit can provide optimal content to attract customers' interests and maximize the effectiveness of the promotion.

[0033] The distribution unit distributes the content generated by the generation unit. Specifically, the distribution unit analyzes the behavioral history of the customer, determines the distribution method and time period with the highest probability of viewing, and implements the promotion. For example, if a customer tends to check emails at a specific time period, promotional emails can be sent at that time period. Also, if a customer frequently uses a specific website or app, promotions can be implemented on that platform. The distribution unit can utilize multiple distribution channels to deliver content to customers at the optimal timing. For example, promotions are implemented by combining various channels such as email, SMS, push notifications, and social media ads. Furthermore, the distribution unit can monitor the distribution results in real time and evaluate the effectiveness. For example, the opening rate and click rate of the distributed content are analyzed to measure the effectiveness of the promotion. This allows the distribution unit to plan and implement an optimal distribution strategy to maximize the effectiveness of the promotion. Furthermore, the distribution unit can collect customer feedback and continuously improve the content and distribution method of the promotion. This allows the distribution unit to provide effective promotions to customers and improve customer satisfaction.

[0034] The collection unit can collect data on products viewed by customers, pages visited, and links clicked by customers. The collection unit, for example, collects data on products viewed by customers, pages visited, and links clicked by customers. For example, on an e-commerce site, data is collected on which products a customer viewed and which products he or she added to a cart. This makes it possible to understand the customer's preferences and behavioral patterns. A part or all of the above-mentioned 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 customer behavior history data to the generation AI and cause the generation AI to collect data. This allows for more accurate analysis by collecting detailed customer behavioral data.

[0035] The analysis unit can analyze the collected data and grasp the customer's preferences and behavioral patterns. The analysis unit, for example, analyzes the collected data and grasps the customer's preferences and behavioral patterns. For example, it can identify customers who are highly interested in a specific product and propose an appropriate promotion to the customer. This enables personalized promotions that meet the needs of the customers. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and have the generation AI analyze the data. This enables personalized promotions by grasping the customer's preferences and behavioral patterns.

[0036] The generation unit can generate content such as videos, images, and cartoons related to a particular product for customers who are highly interested in the product. The generation unit can generate content such as videos, images, and cartoons related to a particular product for customers who are highly interested in the product. For example, the generation unit can generate content such as videos, images, and cartoons related to a particular product for customers who are highly interested in the product. This can attract the interest of customers and increase the effectiveness of promotion. A part or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input customer interest data to the generation AI and cause the generation AI to generate content. This can increase the effectiveness of promotion by generating content according to the customer's interest.

[0037] The distribution unit can analyze the behavioral history of the customer, determine the distribution method and time period with the highest probability of viewing, and implement the promotion. The distribution unit, for example, analyzes the behavioral history of the customer, determine the distribution method and time period with the highest probability of viewing, and implement the promotion. For example, if a customer tends to check emails at a specific time period, a promotional email can be sent at that time period. Also, if a customer frequently uses a specific website or app, a promotion can be implemented on that platform. This can maximize the effectiveness of the promotion. 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 the behavioral history data of the customer to the generation AI and cause the generation AI to determine the distribution method and time period. This can maximize the effectiveness of the promotion by selecting the optimal distribution method and time period based on the behavioral history of the customer.

[0038] The distribution unit can transmit the promotional email during a certain time period if the customer tends to check email during that time period. For example, if the customer tends to check email during a certain time period, the distribution unit can transmit the promotional email during that time period. For example, if the customer tends to check email during a certain time period, the distribution unit can transmit the promotional email during that time period. This allows the email open rate to be improved by transmitting the promotional email based on the customer's behavioral pattern. A part or all of the above-mentioned processing in the distribution unit may be performed using AI, for example, or may be performed without using AI. For example, the distribution unit can input the customer's email check time data to the generation AI and cause the generation AI to execute the timing of transmitting the promotional email. This allows the email open rate to be improved by transmitting the promotional email based on the customer's behavioral pattern.

[0039] The distribution unit can implement a promotion on a platform when a customer frequently uses a specific website or app. For example, when a customer frequently uses a specific website or app, the distribution unit can implement a promotion on that platform. For example, when a customer frequently uses a specific website or app, the distribution unit can implement a promotion on that platform. As a result, the effectiveness of the promotion can be increased by implementing the promotion on a platform frequently used by the customer. A part or all of the above-mentioned processing in the distribution unit can be performed using AI, for example, or can be performed without using AI. For example, the distribution unit can input the customer's website or app usage data into the generation AI and cause the generation AI to implement the promotion. As a result, the effectiveness of the promotion can be increased by implementing the promotion on a platform frequently used by the customer.

[0040] The collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the collection unit preferentially collects data from pages that the user has frequently visited in the past. In addition, the collection unit can analyze the pattern of links that the user has clicked in the past and collect related data. Furthermore, the collection unit can collect data on related products based on the user's past purchase history. This allows efficient data collection by selecting the optimal data collection method based on the user's past behavior history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavior history data to the generation AI and cause the generation AI to select the optimal data collection method. This allows efficient data collection by selecting the optimal data collection method based on the user's past behavior history.

[0041] The collection unit can perform filtering based on the user's current field of interest and living situation when collecting data. The collection unit collects data based on, for example, a product category in which the user is currently interested. The collection unit can also collect related data according to the user's living situation (e.g., traveling, working). Furthermore, the collection unit can collect related promotion data based on the user's current purchasing motivation. As a result, more relevant data can be collected by filtering data based on the user's current field of interest and living situation. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's field of interest and living situation data to the generation AI and cause the generation AI to filter the data. As a result, more relevant data can be collected by filtering data based on the user's current field of interest and living situation.

[0042] The collection unit can preferentially collect highly relevant data in consideration of the user's geographical location information when collecting data. For example, the collection unit preferentially collects data related to specific stores and services in the area where the user is currently located. The collection unit can also collect data related to nearby events and promotions based on the user's location information. Furthermore, when the user is traveling, the collection unit can preferentially collect data related to tourist information and services at the travel destination. This allows for more effective promotion by preferentially collecting highly relevant data based on the user's geographical location information. A part or all of the above-mentioned 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 geographical location information data to the generation AI and cause the generation AI to collect highly relevant data. This allows for more effective promotion by preferentially collecting highly relevant data based on the user's geographical location information.

[0043] The collection unit can analyze the social media activity of the user and collect related data when collecting data. The collection unit can collect related data based on, for example, content shared by the user on social media. The collection unit can also analyze the activity of accounts followed by the user and collect related data. Furthermore, the collection unit can collect related data based on posts that the user has "liked" on social media. This allows for more personalized promotion by collecting related data based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data. This allows for more personalized promotion by collecting related data based on the user's social media activity.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the customer when analyzing the data. For example, the analysis unit can perform a detailed analysis for important customers and propose individual promotions. In addition, the analysis unit can perform a basic analysis for general customers and propose standard promotions. Furthermore, the analysis unit can analyze initial behavior patterns for new customers and propose appropriate promotions. As a result, more effective promotions can be achieved by adjusting the level of detail of the analysis based on the importance of the customer. A part or all of the above-mentioned 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 customer importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis. As a result, more effective promotions can be achieved by adjusting the level of detail of the analysis based on the importance of the customer.

[0045] The analysis unit can apply different analysis algorithms depending on the customer category when analyzing data. For example, the analysis unit applies a specific algorithm to a customer who tends to purchase expensive products for analysis. In addition, the analysis unit can apply a different algorithm to a customer who frequently purchases products for analysis. Furthermore, the analysis unit can apply an algorithm specialized for a specific category to a customer who is interested in products in that category for analysis. This allows for more accurate analysis by applying different analysis algorithms depending on the customer category. Part 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 customer category data to the generation AI and cause the generation AI to apply the analysis algorithm. This allows for more accurate analysis by applying different analysis algorithms depending on the customer category.

[0046] The analysis unit can determine the priority of the analysis based on the time of submission of the customer's behavior history during data analysis. The analysis unit, for example, prioritizes the analysis of recent behavior history and proposes the latest promotion. The analysis unit can also refer to past behavior history to grasp long-term trends. Furthermore, the analysis unit can analyze the history of actions concentrated in a specific period and propose a promotion specialized for that period. As a result, the latest promotion can be proposed by determining the priority of the analysis based on the time of submission of the customer's behavior history. A part 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 customer's behavior history data into the generation AI and cause the generation AI to execute the determination of the priority of the analysis. As a result, the latest promotion can be proposed by determining the priority of the analysis based on the time of submission of the customer's behavior history.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the customer when analyzing the data. For example, the analysis unit can prioritize analyzing the data of important customers and propose individual promotions. The analysis unit can also analyze the data of general customers next and propose standard promotions. Furthermore, the analysis unit can analyze the data of new customers last and propose initial promotions. In this way, by adjusting the order of analysis based on the relevance of the customer, it is possible to preferentially propose promotions to important customers. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the customer to the generation AI and cause the generation AI to adjust the order of analysis. In this way, by adjusting the order of analysis based on the relevance of the customer, it is possible to preferentially propose promotions to important customers.

[0048] The generation unit can adjust the level of detail of the content to be generated based on the customer's interest level when generating the content. For example, the generation unit generates content including detailed information for customers with a high level of interest. The generation unit can also generate content including basic information for customers with a general level of interest. Furthermore, the generation unit can generate content including concise information for customers with a low level of interest. This allows for more effective promotion by adjusting the level of detail of the content based on the customer's interest level. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer interest level data to the generation AI and cause the generation AI to adjust the level of detail of the content. This allows for more effective promotion by adjusting the level of detail of the content based on the customer's interest level.

[0049] The generation unit can apply different generation algorithms depending on the customer category when generating content. For example, the generation unit can apply a specific algorithm to a customer who is interested in expensive products to generate content. In addition, the generation unit can apply a different algorithm to a customer who frequently purchases products to generate content. Furthermore, the generation unit can apply an algorithm specialized for a specific category to a customer who is interested in products in that category to generate content. As a result, more accurate content generation is possible by applying different generation algorithms depending on the customer category. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer category data to the generation AI and cause the generation AI to apply the generation algorithm. As a result, more accurate content generation is possible by applying different generation algorithms depending on the customer category.

[0050] The generation unit can determine the priority of the content to be generated based on the time of submission of the customer's behavior history when generating the content. The generation unit, for example, generates the latest content preferentially based on the recent behavior history. The generation unit can also generate content based on a long-term trend by referring to the past behavior history. Furthermore, the generation unit can generate content specialized for a specific period based on a history of actions concentrated in that period. As a result, the latest content can be provided by determining the priority of the content to be generated based on the time of submission of the customer's behavior history. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's behavior history data to the generation AI and cause the generation AI to execute the determination of the priority of the content. As a result, the latest content can be provided by determining the priority of the content to be generated based on the time of submission of the customer's behavior history.

[0051] The generation unit can adjust the order of the generated content based on the relevance of the customer when generating the content. For example, the generation unit generates detailed content preferentially for important customers. Also, the generation unit can generate basic content next for general customers. Furthermore, the generation unit can generate initial content last for new customers. As a result, by adjusting the order of the generated content based on the relevance of the customer, the content can be provided preferentially to important customers. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer relevance data to the generation AI and cause the generation AI to adjust the order of the content. As a result, by adjusting the order of the generated content based on the relevance of the customer, the content can be provided preferentially to important customers.

[0052] The distribution unit can select the optimal distribution method by analyzing the customer's past behavior history at the time of distribution. For example, if the customer has frequently checked email in the past, the distribution unit can prioritize email distribution. In addition, if the customer has frequently visited a website in the past, the distribution unit can prioritize distribution on the website. In addition, if the customer has frequently used an app in the past, the distribution unit can prioritize distribution within the app. This allows the promotion effect to be enhanced by selecting the optimal distribution method based on the customer's past behavior history. A part 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 the customer's behavior history data to the generation AI and cause the generation AI to select the optimal distribution method. This allows the promotion effect to be enhanced by selecting the optimal distribution method based on the customer's past behavior history.

[0053] The distribution unit can customize the distribution means based on the customer's current living situation at the time of distribution. For example, when the customer is traveling, the distribution unit can perform distribution including information about the travel destination. Furthermore, when the customer is at work, the distribution unit can perform distribution including information related to the work. Furthermore, when the customer is at home, the distribution unit can perform distribution related to services that can be used at home. This allows for more effective promotion by customizing the distribution means based on the customer's current living situation. A part 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 the customer's living situation data into the generation AI and cause the generation AI to customize the distribution means. This allows for more effective promotion by customizing the distribution means based on the customer's current living situation.

[0054] The distribution unit can select the optimal distribution method in consideration of the geographical location information of the customer at the time of distribution. For example, the distribution unit preferentially distributes information about specific stores and services in the area where the customer is currently located. The distribution unit can also distribute information about nearby events and promotions based on the customer's location information. Furthermore, when the customer is traveling, the distribution unit can preferentially distribute information about tourist information and services at the travel destination. This allows for more effective promotion by selecting the optimal distribution method based on the geographical location information of the customer. Part or all of the above-mentioned processing in the distribution unit may be performed, for example, using AI or may be performed without using AI. For example, the distribution unit can input the geographical location information data of the customer to the generation AI and cause the generation AI to select the optimal distribution method. This allows for more effective promotion by selecting the optimal distribution method based on the geographical location information of the customer.

[0055] The distribution unit can analyze the social media activity of the customer at the time of distribution and suggest a distribution means. The distribution unit distributes related information based on, for example, content shared by the customer on social media. The distribution unit can also analyze the activity of accounts followed by the customer and deliver related information. Furthermore, the distribution unit can deliver related information based on posts that the customer has "liked" on social media. This allows for more personalized promotion by suggesting a distribution means based on the customer's social media activity. 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 the social media activity data of the customer to the generation AI and cause the generation AI to execute the suggestion of a distribution means. This allows for more personalized promotion by suggesting a distribution means based on the customer's social media activity.

[0056] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0057] The collection unit can analyze the social media activities of customers and collect related data. For example, the collection unit can analyze content shared by customers on social media and the activities of accounts they follow, and collect related data. This allows for a more detailed understanding of customers' interests and concerns, enabling more precise promotions. Furthermore, the collection unit can also collect related data based on posts that customers have "liked" on social media. This allows for more personalized promotions by collecting related data based on customers' social media activities.

[0058] The analysis unit can analyze the customer's purchasing history and make future purchasing predictions. For example, it can predict the next product that the customer is likely to purchase based on past purchasing patterns. This makes it possible to suggest appropriate promotions to the customer. Furthermore, the analysis unit can also suggest new products that the customer is likely to be interested in based on the customer's purchasing history. This makes it possible to increase the customer's purchasing motivation. Furthermore, the analysis unit can analyze whether the customer has a tendency to purchase products related to a particular season or event based on the customer's purchasing history. This makes it possible to suggest promotions that match the season or event.

[0059] The generation unit can generate optimal content based on the customer's past promotion response data. For example, video content can be generated for a customer who has responded well to video content in the past. Also, image content can be generated for a customer who has responded well to image content in the past. Furthermore, the generation unit can adjust the format and style of the content based on the customer's past promotion response data. This can increase the effectiveness of promotions by generating content that matches the customer's preferences.

[0060] The distribution unit can select the optimal distribution method in consideration of the geographical location information of the customer. For example, information about specific stores and services in the area where the customer is currently located can be preferentially distributed. Information about nearby events and promotions can also be distributed based on the customer's location information. Furthermore, if the customer is traveling, tourist information and information about services at the travel destination can be preferentially distributed. This allows for more effective promotion by selecting the optimal distribution method based on the geographical location information of the customer.

[0061] The process flow of the first embodiment will be briefly described below.

[0062] Step 1: The collection unit collects data on the customer's behavioral history and preferences. For example, this includes purchase history, browsing history, and click history. The collection unit collects data on the products the customer viewed, the pages they visited, and the links they clicked. This allows the collection unit to understand the customer's preferences and behavioral patterns. Step 2: The analysis unit analyzes the data collected by the collection unit to understand customer preferences and behavioral patterns. For example, it can identify customers who are highly interested in a particular product and propose appropriate promotions to those customers. Step 3: The generation unit determines the optimal promotion and generates content based on the analysis results obtained by the analysis unit. For example, for customers who are highly interested in a particular product, it generates content such as videos, images, and comics related to that product. Step 4: The distribution unit distributes the content generated by the generation unit. For example, the distribution unit analyzes the behavioral history of customers, determines the distribution method and time period that will maximize the probability of viewing, and then implements promotions.

[0063] (Example 2) The advertisement delivery system according to the embodiment of the present invention is a system that analyzes a large amount of data and delivers optimal advertisements based on the preferences and behavioral patterns of individual customers. This advertisement delivery system collects data on the behavioral history and preferences of customers, and the AI ​​analyzes the data to grasp the preferences of the customers in real time. Based on this, the AI ​​determines the optimal promotion and generates content such as videos, images, and comics. Furthermore, the AI ​​analyzes the behavioral history of the customers, determines the delivery method (email, website, in-app, etc.) and time period with the highest probability of viewing, and implements the promotion. For example, the advertisement delivery system collects detailed data such as what products the customers viewed, which pages they visited, and which links they clicked. This makes it possible to grasp the preferences and behavioral patterns of the customers. Next, the AI ​​analyzes the collected data and grasps the preferences and behavioral patterns of the customers. For example, it is possible to identify customers who are highly interested in a specific product and propose an appropriate promotion to the customer. This makes it possible to provide personalized promotions tailored to the needs of the customers. Furthermore, the AI ​​determines the optimal promotion and generates content such as videos, images, and comics. For example, it is possible to generate content such as videos, images, and comics related to a specific product for customers who are highly interested in the product. This can attract customer interest and increase the effectiveness of promotions. Finally, AI analyzes the customer's behavioral history, determines the delivery method and time period with the highest probability of viewing, and then implements the promotion. For example, if a customer tends to check email at a specific time of day, promotional emails can be sent at that time. Also, if a customer frequently uses a specific website or app, promotions can be implemented on that platform. This maximizes the effectiveness of promotions. This allows the ad delivery system to provide optimal promotions based on the customer's behavioral history and preferences.

[0064] The advertisement delivery system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a delivery unit. The collection unit collects data on the behavioral history and preferences of the customer. The data on the behavioral history and preferences of the customer includes, but is not limited to, purchase history, browsing history, click history, and the like. The collection unit collects, for example, data on products viewed by the customer, pages visited, and links clicked. For example, on an e-commerce site, data is collected on which products the customer viewed and which products the customer added to a cart. This makes it possible to grasp the customer's preferences and behavioral patterns. The analysis unit analyzes the data collected by the collection unit and grasps the customer's preferences and behavioral patterns. For example, the analysis unit analyzes the collected data and grasps the customer's preferences and behavioral patterns. For example, it is possible to identify a customer who is highly interested in a specific product and propose an appropriate promotion to the customer. This makes it possible to provide personalized promotions tailored to the needs of the customer. The generation unit determines an optimal promotion based on the analysis results obtained by the analysis unit and generates content. The generating unit generates content such as videos, images, and cartoons related to a specific product for a customer who is highly interested in the product. For example, the generating unit can generate content such as videos, images, and cartoons related to a specific product for a customer who is highly interested in the product. This can attract the customer's interest and increase the effectiveness of the promotion. The distribution unit distributes the content generated by the generating unit. For example, the distribution unit analyzes the customer's behavior history, determines the distribution method and time period with the highest probability of viewing, and implements the promotion. For example, if a customer tends to check email during a specific time period, a promotion email can be sent during that time period. Also, if a customer frequently uses a specific website or app, a promotion can be implemented on that platform. This can maximize the effectiveness of the promotion. This allows the advertisement distribution system according to the embodiment to provide optimal promotion based on the customer's behavior history and preferences.

[0065] The collection unit collects data on the behavioral history and preferences of customers. Specifically, the collection unit collects data such as the purchase history, browsing history, and click history of the customers' online purchases. For example, on an e-commerce site, detailed data such as which products the customers viewed, which products they added to their cart, and even which products they purchased are collected. This allows the customer's preferences and behavioral patterns to be understood in detail. The collection unit uses website tracking tools and cookies to monitor the customer's behavior in real time and collect data. In addition, the collection unit can collect information on the device and browser used by the customer to obtain more accurate data. In addition, the collection unit can collect data from other sources such as social media activity and survey results. This allows the collection unit to understand not only the customer's online behavior but also their offline preferences and behavioral patterns. The collected data is stored in a secure database and used for subsequent analysis and promotion. The collection unit can flexibly adjust the frequency and scope of data collection, and can optimally collect data in accordance with a specific campaign or promotion. This allows the collection unit to efficiently and effectively collect data on the customer's behavioral history and preferences, and improve the performance of the entire system.

[0066] The analysis unit analyzes the data collected by the collection unit to grasp the customer's preferences and behavioral patterns. Specifically, the analysis unit analyzes the collected data to identify what products the customer is interested in and what behavioral patterns the customer has. For example, the analysis unit can identify customers who are highly interested in a specific product and propose appropriate promotions to the customers. The analysis unit uses machine learning algorithms and data mining technology to extract useful information from a huge amount of data. For example, the analysis unit uses a clustering algorithm to group customers based on their preferences and behavioral patterns and develops optimal promotion strategies for each group. In addition, the analysis unit can predict future customer behavior based on past data and optimize the timing and content of promotions. Furthermore, the analysis unit can use an anomaly detection algorithm to identify customers who have behavioral patterns that are different from normal and provide special support. This allows the analysis unit to provide a foundation for grasping customer preferences and behavioral patterns in detail and realizing personalized promotions.

[0067] The generation unit determines the optimal promotion and generates the content based on the analysis results obtained by the analysis unit. Specifically, for customers who are highly interested in a particular product, the generation unit generates content such as videos, images, and comics related to the product. The generation unit can automatically generate content that attracts the interest of customers using the generation AI. For example, the generation AI generates optimal promotional messages based on the customer's preferences and behavior patterns, and provides them in the form of videos, images, comics, and the like. The generation AI can generate effective messages for customers and create visually appealing content using natural language processing technology. Furthermore, the generation unit can evaluate the effectiveness of the generated content and modify the content as necessary. For example, if the generated content does not elicit a response from customers, the generation unit can change the content or format of the content and generate it again. In this way, the generation unit can provide optimal content to attract customers' interests and maximize the effectiveness of the promotion.

[0068] The distribution unit distributes the content generated by the generation unit. Specifically, the distribution unit analyzes the behavioral history of the customer, determines the distribution method and time period with the highest probability of viewing, and implements the promotion. For example, if a customer tends to check emails at a specific time period, promotional emails can be sent at that time period. Also, if a customer frequently uses a specific website or app, promotions can be implemented on that platform. The distribution unit can utilize multiple distribution channels to deliver content to customers at the optimal timing. For example, promotions are implemented by combining various channels such as email, SMS, push notifications, and social media ads. Furthermore, the distribution unit can monitor the distribution results in real time and evaluate the effectiveness. For example, the opening rate and click rate of the distributed content are analyzed to measure the effectiveness of the promotion. This allows the distribution unit to plan and implement an optimal distribution strategy to maximize the effectiveness of the promotion. Furthermore, the distribution unit can collect customer feedback and continuously improve the content and distribution method of the promotion. This allows the distribution unit to provide effective promotions to customers and improve customer satisfaction.

[0069] The collection unit can collect data on products viewed by customers, pages visited, and links clicked by customers. The collection unit, for example, collects data on products viewed by customers, pages visited, and links clicked by customers. For example, on an e-commerce site, data is collected on which products a customer viewed and which products he or she added to a cart. This makes it possible to understand the customer's preferences and behavioral patterns. A part or all of the above-mentioned 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 customer behavior history data to the generation AI and cause the generation AI to collect data. This allows for more accurate analysis by collecting detailed customer behavioral data.

[0070] The analysis unit can analyze the collected data and grasp the customer's preferences and behavioral patterns. The analysis unit, for example, analyzes the collected data and grasps the customer's preferences and behavioral patterns. For example, it can identify customers who are highly interested in a specific product and propose an appropriate promotion to the customer. This enables personalized promotions that meet the needs of the customers. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the collected data to the generation AI and have the generation AI analyze the data. This enables personalized promotions by grasping the customer's preferences and behavioral patterns.

[0071] The generation unit can generate content such as videos, images, and cartoons related to a particular product for customers who are highly interested in the product. The generation unit can generate content such as videos, images, and cartoons related to a particular product for customers who are highly interested in the product. For example, the generation unit can generate content such as videos, images, and cartoons related to a particular product for customers who are highly interested in the product. This can attract the interest of customers and increase the effectiveness of promotion. A part or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input customer interest data to the generation AI and cause the generation AI to generate content. This can increase the effectiveness of promotion by generating content according to the customer's interest.

[0072] The distribution unit can analyze the behavioral history of the customer, determine the distribution method and time period with the highest probability of viewing, and implement the promotion. The distribution unit, for example, analyzes the behavioral history of the customer, determine the distribution method and time period with the highest probability of viewing, and implement the promotion. For example, if a customer tends to check emails at a specific time period, a promotional email can be sent at that time period. Also, if a customer frequently uses a specific website or app, a promotion can be implemented on that platform. This can maximize the effectiveness of the promotion. 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 the behavioral history data of the customer to the generation AI and cause the generation AI to determine the distribution method and time period. This can maximize the effectiveness of the promotion by selecting the optimal distribution method and time period based on the behavioral history of the customer.

[0073] The distribution unit can transmit the promotional email during a certain time period if the customer tends to check email during that time period. For example, if the customer tends to check email during a certain time period, the distribution unit can transmit the promotional email during that time period. For example, if the customer tends to check email during a certain time period, the distribution unit can transmit the promotional email during that time period. This allows the email open rate to be improved by transmitting the promotional email based on the customer's behavioral pattern. A part or all of the above-mentioned processing in the distribution unit may be performed using AI, for example, or may be performed without using AI. For example, the distribution unit can input the customer's email check time data to the generation AI and cause the generation AI to execute the timing of transmitting the promotional email. This allows the email open rate to be improved by transmitting the promotional email based on the customer's behavioral pattern.

[0074] The distribution unit can implement a promotion on a platform when a customer frequently uses a specific website or app. For example, when a customer frequently uses a specific website or app, the distribution unit can implement a promotion on that platform. For example, when a customer frequently uses a specific website or app, the distribution unit can implement a promotion on that platform. As a result, the effectiveness of the promotion can be increased by implementing the promotion on a platform frequently used by the customer. A part or all of the above-mentioned processing in the distribution unit can be performed using AI, for example, or can be performed without using AI. For example, the distribution unit can input the customer's website or app usage data into the generation AI and cause the generation AI to implement the promotion. As a result, the effectiveness of the promotion can be increased by implementing the promotion on a platform frequently used by the customer.

[0075] The collection unit can estimate the user's emotion and adjust the timing of data collection based on the estimated user's emotion. For example, when the user is stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. Also, when the user is relaxed, the collection unit can increase the frequency of data collection to collect detailed data. Furthermore, when the user is excited, the collection unit can collect data in real time and obtain instant feedback. This can reduce the burden on the user by adjusting the timing of data collection according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to adjust the timing of data collection. This can reduce the burden on the user by adjusting the timing of data collection according to the user's emotion.

[0076] The collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the collection unit preferentially collects data from pages that the user has frequently visited in the past. In addition, the collection unit can analyze the pattern of links that the user has clicked in the past and collect related data. Furthermore, the collection unit can collect data on related products based on the user's past purchase history. This allows efficient data collection by selecting the optimal data collection method based on the user's past behavior history. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's behavior history data to the generation AI and cause the generation AI to select the optimal data collection method. This allows efficient data collection by selecting the optimal data collection method based on the user's past behavior history.

[0077] The collection unit can perform filtering based on the user's current field of interest and living situation when collecting data. The collection unit collects data based on, for example, a product category in which the user is currently interested. The collection unit can also collect related data according to the user's living situation (e.g., traveling, working). Furthermore, the collection unit can collect related promotion data based on the user's current purchasing motivation. As a result, more relevant data can be collected by filtering data based on the user's current field of interest and living situation. A part or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's field of interest and living situation data to the generation AI and cause the generation AI to filter the data. As a result, more relevant data can be collected by filtering data based on the user's current field of interest and living situation.

[0078] The collection unit can estimate the user's emotion and determine the priority of data to be collected based on the estimated user's emotion. For example, when the user is stressed, the collection unit preferentially collects only data with a high degree of importance. In addition, when the user is relaxed, the collection unit can preferentially collect detailed data. Furthermore, when the user is excited, the collection unit can preferentially collect important data in real time. As a result, important data can be preferentially collected by determining the priority of data to be collected according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to execute the determination of the priority of the data. As a result, important data can be preferentially collected by determining the priority of data to be collected according to the user's emotion.

[0079] The collection unit can preferentially collect highly relevant data in consideration of the user's geographical location information when collecting data. For example, the collection unit preferentially collects data related to specific stores and services in the area where the user is currently located. The collection unit can also collect data related to nearby events and promotions based on the user's location information. Furthermore, when the user is traveling, the collection unit can preferentially collect data related to tourist information and services at the travel destination. This allows for more effective promotion by preferentially collecting highly relevant data based on the user's geographical location information. A part or all of the above-mentioned 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 geographical location information data to the generation AI and cause the generation AI to collect highly relevant data. This allows for more effective promotion by preferentially collecting highly relevant data based on the user's geographical location information.

[0080] The collection unit can analyze the social media activity of the user and collect related data when collecting data. The collection unit can collect related data based on, for example, content shared by the user on social media. The collection unit can also analyze the activity of accounts followed by the user and collect related data. Furthermore, the collection unit can collect related data based on posts that the user has "liked" on social media. This allows for more personalized promotion by collecting related data based on the user's social media activity. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data to the generation AI and cause the generation AI to collect related data. This allows for more personalized promotion by collecting related data based on the user's social media activity.

[0081] The analysis unit can estimate the user's emotion and adjust the data analysis method based on the estimated user's emotion. For example, when the user is stressed, the analysis unit applies a simple analysis method and provides a result quickly. Also, when the user is relaxed, the analysis unit can apply a detailed analysis method and provide deep insight. Furthermore, when the user is excited, the analysis unit can analyze data in real time and provide instant feedback. As a result, a more appropriate analysis result can be provided by adjusting the data analysis method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to adjust the data analysis method. As a result, a more appropriate analysis result can be provided by adjusting the data analysis method according to the user's emotion.

[0082] The analysis unit can adjust the level of detail of the analysis based on the importance of the customer when analyzing the data. For example, the analysis unit can perform a detailed analysis for important customers and propose individual promotions. In addition, the analysis unit can perform a basic analysis for general customers and propose standard promotions. Furthermore, the analysis unit can analyze initial behavior patterns for new customers and propose appropriate promotions. As a result, more effective promotions can be achieved by adjusting the level of detail of the analysis based on the importance of the customer. A part or all of the above-mentioned 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 customer importance data to the generation AI and cause the generation AI to adjust the level of detail of the analysis. As a result, more effective promotions can be achieved by adjusting the level of detail of the analysis based on the importance of the customer.

[0083] The analysis unit can apply different analysis algorithms depending on the customer category when analyzing data. For example, the analysis unit applies a specific algorithm to a customer who tends to purchase expensive products for analysis. In addition, the analysis unit can apply a different algorithm to a customer who frequently purchases products for analysis. Furthermore, the analysis unit can apply an algorithm specialized for a specific category to a customer who is interested in products in that category for analysis. This allows for more accurate analysis by applying different analysis algorithms depending on the customer category. Part 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 customer category data to the generation AI and cause the generation AI to apply the analysis algorithm. This allows for more accurate analysis by applying different analysis algorithms depending on the customer category.

[0084] The analysis unit can estimate the user's emotion and adjust the display method of the analysis result based on the estimated user's emotion. For example, when the user is stressed, the analysis unit provides a simple and highly visible display method. Furthermore, when the user is relaxed, the analysis unit can provide a display method including detailed information. Furthermore, when the user is excited, the analysis unit can provide a visually stimulating display method. As a result, by adjusting the display method of the analysis result according to the user's emotion, it is possible to provide a result with higher visibility. The estimation of emotion is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is, for example, a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to adjust the display method of the analysis result. As a result, by adjusting the display method of the analysis result according to the user's emotion, it is possible to provide a result with higher visibility.

[0085] The analysis unit can determine the priority of the analysis based on the time of submission of the customer's behavior history during data analysis. The analysis unit, for example, prioritizes the analysis of recent behavior history and proposes the latest promotion. The analysis unit can also refer to past behavior history to grasp long-term trends. Furthermore, the analysis unit can analyze the history of actions concentrated in a specific period and propose a promotion specialized for that period. As a result, the latest promotion can be proposed by determining the priority of the analysis based on the time of submission of the customer's behavior history. A part 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 customer's behavior history data into the generation AI and cause the generation AI to execute the determination of the priority of the analysis. As a result, the latest promotion can be proposed by determining the priority of the analysis based on the time of submission of the customer's behavior history.

[0086] The analysis unit can adjust the order of analysis based on the relevance of the customer when analyzing the data. For example, the analysis unit can prioritize analyzing the data of important customers and propose individual promotions. The analysis unit can also analyze the data of general customers next and propose standard promotions. Furthermore, the analysis unit can analyze the data of new customers last and propose initial promotions. In this way, by adjusting the order of analysis based on the relevance of the customer, it is possible to preferentially propose promotions to important customers. A part or all of the above-mentioned processing in the analysis unit may be performed, for example, using AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the customer to the generation AI and cause the generation AI to adjust the order of analysis. In this way, by adjusting the order of analysis based on the relevance of the customer, it is possible to preferentially propose promotions to important customers.

[0087] The generation unit can estimate the user's emotion and adjust the content generation method based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates a video that progresses at a leisurely pace. Furthermore, when the user is in a hurry, the generation unit can generate a video that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit can generate a video with a visually stimulating effect added. This allows more appropriate content to be provided by adjusting the content generation method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the content generation method. This allows more appropriate content to be provided by adjusting the content generation method according to the user's emotion.

[0088] The generation unit can adjust the level of detail of the content to be generated based on the customer's interest level when generating the content. For example, the generation unit generates content including detailed information for customers with a high level of interest. The generation unit can also generate content including basic information for customers with a general level of interest. Furthermore, the generation unit can generate content including concise information for customers with a low level of interest. This allows for more effective promotion by adjusting the level of detail of the content based on the customer's interest level. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer interest level data to the generation AI and cause the generation AI to adjust the level of detail of the content. This allows for more effective promotion by adjusting the level of detail of the content based on the customer's interest level.

[0089] The generation unit can apply different generation algorithms depending on the customer category when generating content. For example, the generation unit can apply a specific algorithm to a customer who is interested in expensive products to generate content. In addition, the generation unit can apply a different algorithm to a customer who frequently purchases products to generate content. Furthermore, the generation unit can apply an algorithm specialized for a specific category to a customer who is interested in products in that category to generate content. As a result, more accurate content generation is possible by applying different generation algorithms depending on the customer category. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer category data to the generation AI and cause the generation AI to apply the generation algorithm. As a result, more accurate content generation is possible by applying different generation algorithms depending on the customer category.

[0090] The generation unit can estimate the user's emotion and determine the type of content to be generated based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates video content. Furthermore, when the user is in a hurry, the generation unit can generate image content. Furthermore, when the user is excited, the generation unit can generate cartoon content. As a result, by determining the type of content to be generated according to the user's emotion, more appropriate content can be provided. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the type of content to be generated. As a result, by determining the type of content to be generated according to the user's emotion, more appropriate content can be provided.

[0091] The generation unit can determine the priority of the content to be generated based on the time of submission of the customer's behavior history when generating the content. The generation unit, for example, generates the latest content preferentially based on the recent behavior history. The generation unit can also generate content based on a long-term trend by referring to the past behavior history. Furthermore, the generation unit can generate content specialized for a specific period based on a history of actions concentrated in that period. As a result, the latest content can be provided by determining the priority of the content to be generated based on the time of submission of the customer's behavior history. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the customer's behavior history data to the generation AI and cause the generation AI to execute the determination of the priority of the content. As a result, the latest content can be provided by determining the priority of the content to be generated based on the time of submission of the customer's behavior history.

[0092] The generation unit can adjust the order of the generated content based on the relevance of the customer when generating the content. For example, the generation unit generates detailed content preferentially for important customers. Also, the generation unit can generate basic content next for general customers. Furthermore, the generation unit can generate initial content last for new customers. As a result, by adjusting the order of the generated content based on the relevance of the customer, the content can be provided preferentially to important customers. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input customer relevance data to the generation AI and cause the generation AI to adjust the order of the content. As a result, by adjusting the order of the generated content based on the relevance of the customer, the content can be provided preferentially to important customers.

[0093] The delivery unit can estimate the user's emotion and adjust the delivery method based on the estimated user's emotion. For example, when the user is stressed, the delivery unit selects a simple delivery method. Furthermore, when the user is relaxed, the delivery unit can select a delivery method including detailed information. Furthermore, when the user is excited, the delivery unit can select a visually stimulating delivery method. This allows for more appropriate delivery by adjusting the delivery method according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery unit can input the user's emotion data to the generation AI and cause the generation AI to adjust the delivery method. This allows for more appropriate delivery by adjusting the delivery method according to the user's emotion.

[0094] The distribution unit can select the optimal distribution method by analyzing the customer's past behavior history at the time of distribution. For example, if the customer has frequently checked email in the past, the distribution unit can prioritize email distribution. In addition, if the customer has frequently visited a website in the past, the distribution unit can prioritize distribution on the website. In addition, if the customer has frequently used an app in the past, the distribution unit can prioritize distribution within the app. This allows the promotion effect to be enhanced by selecting the optimal distribution method based on the customer's past behavior history. A part 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 the customer's behavior history data to the generation AI and cause the generation AI to select the optimal distribution method. This allows the promotion effect to be enhanced by selecting the optimal distribution method based on the customer's past behavior history.

[0095] The distribution unit can customize the distribution means based on the customer's current living situation at the time of distribution. For example, when the customer is traveling, the distribution unit can perform distribution including information about the travel destination. Furthermore, when the customer is at work, the distribution unit can perform distribution including information related to the work. Furthermore, when the customer is at home, the distribution unit can perform distribution related to services that can be used at home. This allows for more effective promotion by customizing the distribution means based on the customer's current living situation. A part 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 the customer's living situation data into the generation AI and cause the generation AI to customize the distribution means. This allows for more effective promotion by customizing the distribution means based on the customer's current living situation.

[0096] The delivery unit can estimate the user's emotion and determine the priority of delivery based on the estimated user's emotion. For example, when the user is feeling stressed, the delivery unit can preferentially deliver information with high importance. Furthermore, when the user is relaxed, the delivery unit can preferentially deliver detailed information. Furthermore, when the user is excited, the delivery unit can preferentially deliver visually stimulating information. As a result, important information can be preferentially delivered by determining the priority of delivery according to the user's emotion. The emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. A part or all of the above-mentioned processing in the delivery unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the delivery unit can input the user's emotion data to the generation AI and cause the generation AI to execute the determination of the priority of delivery. As a result, important information can be preferentially delivered by determining the priority of delivery according to the user's emotion.

[0097] The distribution unit can select the optimal distribution method in consideration of the geographical location information of the customer at the time of distribution. For example, the distribution unit preferentially distributes information about specific stores and services in the area where the customer is currently located. The distribution unit can also distribute information about nearby events and promotions based on the customer's location information. Furthermore, when the customer is traveling, the distribution unit can preferentially distribute information about tourist information and services at the travel destination. This allows for more effective promotion by selecting the optimal distribution method based on the geographical location information of the customer. Part or all of the above-mentioned processing in the distribution unit may be performed, for example, using AI or may be performed without using AI. For example, the distribution unit can input the geographical location information data of the customer to the generation AI and cause the generation AI to select the optimal distribution method. This allows for more effective promotion by selecting the optimal distribution method based on the geographical location information of the customer.

[0098] The distribution unit can analyze the social media activity of the customer at the time of distribution and suggest a distribution means. The distribution unit distributes related information based on, for example, content shared by the customer on social media. The distribution unit can also analyze the activity of accounts followed by the customer and deliver related information. Furthermore, the distribution unit can deliver related information based on posts that the customer has "liked" on social media. This allows for more personalized promotion by suggesting a distribution means based on the customer's social media activity. 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 the social media activity data of the customer to the generation AI and cause the generation AI to execute the suggestion of a distribution means. This allows for more personalized promotion by suggesting a distribution means based on the customer's social media activity.

[0099] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0100] The advertisement delivery system further includes a feedback collection unit. The feedback collection unit can collect responses to the promotion received by the customer. For example, it can record whether the customer opened a promotional email, watched a video, or clicked a link. This allows the collected feedback data to be provided to the analysis unit to improve the accuracy of the next promotion. Furthermore, the feedback collection unit can also estimate how the customer felt about the promotion and provide the emotion data to the analysis unit. This enables a more personalized promotion based on the customer's emotions.

[0101] The collection unit can analyze the social media activities of customers and collect related data. For example, the collection unit can analyze content shared by customers on social media and the activities of accounts they follow, and collect related data. This allows for a more detailed understanding of customers' interests and concerns, enabling more precise promotions. Furthermore, the collection unit can also collect related data based on posts that customers have "liked" on social media. This allows for more personalized promotions by collecting related data based on customers' social media activities.

[0102] The analysis unit can analyze the customer's purchasing history and make future purchasing predictions. For example, it can predict the next product that the customer is likely to purchase based on past purchasing patterns. This makes it possible to suggest appropriate promotions to the customer. Furthermore, the analysis unit can also suggest new products that the customer is likely to be interested in based on the customer's purchasing history. This makes it possible to increase the customer's purchasing motivation. Furthermore, the analysis unit can analyze whether the customer has a tendency to purchase products related to a particular season or event based on the customer's purchasing history. This makes it possible to suggest promotions that match the season or event.

[0103] The generation unit can generate optimal content based on the customer's past promotion response data. For example, video content can be generated for a customer who has responded well to video content in the past. Also, image content can be generated for a customer who has responded well to image content in the past. Furthermore, the generation unit can adjust the format and style of the content based on the customer's past promotion response data. This can increase the effectiveness of promotions by generating content that matches the customer's preferences.

[0104] The distribution unit can select the optimal distribution method in consideration of the geographical location information of the customer. For example, information about specific stores and services in the area where the customer is currently located can be preferentially distributed. Information about nearby events and promotions can also be distributed based on the customer's location information. Furthermore, if the customer is traveling, tourist information and information about services at the travel destination can be preferentially distributed. This allows for more effective promotion by selecting the optimal distribution method based on the geographical location information of the customer.

[0105] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, when the user is stressed, the frequency of data collection can be reduced to reduce the burden on the user. When the user is relaxed, the frequency of data collection can be increased to collect detailed data. Furthermore, when the user is excited, data can be collected in real time to obtain instant feedback. This allows the burden on the user to be reduced by adjusting the timing of data collection according to the user's emotions.

[0106] The analysis unit can estimate the user's emotions and adjust the method of data analysis based on the estimated user's emotions. For example, if the user is stressed, a simple analysis method can be applied to provide results quickly. If the user is relaxed, a detailed analysis method can be applied to provide deep insights. Furthermore, if the user is excited, data can be analyzed in real time and instant feedback can be provided. This makes it possible to provide more appropriate analysis results by adjusting the method of data analysis according to the user's emotions.

[0107] The generation unit can estimate the user's emotion and adjust the content generation method based on the estimated user's emotion. For example, if the user is relaxed, a video that progresses at a leisurely pace can be generated. If the user is in a hurry, a video that emphasizes the shortest route can be generated. Furthermore, if the user is excited, a video that adds a visually stimulating effect can be generated. In this way, more appropriate content can be provided by adjusting the content generation method according to the user's emotion.

[0108] The delivery unit can estimate the user's emotion and adjust the delivery method based on the estimated user's emotion. For example, if the user is stressed, a simple delivery method can be selected. If the user is relaxed, a delivery method including detailed information can be selected. If the user is excited, a visually stimulating delivery method can be selected. This allows for more appropriate delivery by adjusting the delivery method according to the user's emotion.

[0109] The delivery unit can estimate the user's emotions and determine the priority of delivery based on the estimated user's emotions. For example, when the user is stressed, information of high importance can be delivered with priority. When the user is relaxed, detailed information can be delivered with priority. When the user is excited, visually stimulating information can be delivered with priority. Thus, important information can be delivered with priority by determining the priority of delivery according to the user's emotions.

[0110] The process flow of the second embodiment will be briefly described below.

[0111] Step 1: The collection unit collects data on the customer's behavioral history and preferences. For example, this includes purchase history, browsing history, and click history. The collection unit collects data on the products the customer viewed, the pages they visited, and the links they clicked. This allows the collection unit to understand the customer's preferences and behavioral patterns. Step 2: The analysis unit analyzes the data collected by the collection unit to understand customer preferences and behavioral patterns. For example, it can identify customers who are highly interested in a particular product and propose appropriate promotions to those customers. Step 3: The generation unit determines the optimal promotion and generates content based on the analysis results obtained by the analysis unit. For example, for customers who are highly interested in a particular product, it generates content such as videos, images, and comics related to that product. Step 4: The distribution unit distributes the content generated by the generation unit. For example, the distribution unit analyzes the behavioral history of customers, determines the distribution method and time period that will maximize the probability of viewing, and then implements promotions.

[0112] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice 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 voice data.

[0113] 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<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. 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 a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0114] Furthermore, the above-described processing by the data processing system 10 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 required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and distribution unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects data on the behavior history and preferences of the customer using the camera 42 and microphone 38B of the smart device 14. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the customer's preferences and behavior patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the optimal promotion based on the analysis result and generates content. The distribution unit is realized, for example, by the control unit 46A of the smart device 14, and distributes the generated content to the customer. The collection unit can, for example, estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The correspondence between each unit and the device and control unit is not limited to the above-mentioned example, and various changes are possible.

[0116] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0117] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] 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, the RAM 30, and the 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.

[0119] 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.

[0120] 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 the voice according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0122] 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 performed in a secure state.

[0123] Fig. 4 shows an example of 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.

[0124] 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.

[0125] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0126] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 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 also perform processing similar to that of the specific processing unit 290 using these models.

[0127] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0128] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the 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.

[0129] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0130] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, 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, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and distribution unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects data on the behavior history and preferences of the customer using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the customer's preferences and behavior patterns. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the optimal promotion based on the analysis result and generates content. The distribution unit is realized, for example, by the control unit 46A of the smart glasses 214, and distributes the generated content to the customer. The collection unit can, for example, estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0132] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0133] 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.

[0134] 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, the RAM 30, and the 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.

[0135] 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.

[0136] 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 the voice according to instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a 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 (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0138] 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 performed in a secure state.

[0139] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0140] 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.

[0141] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0142] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific 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 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.

[0143] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0144] 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 voice indicating a user input for 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.

[0145] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0146] 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 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. In addition, 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, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and distribution unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit collects data on the behavior history and preferences of customers using the camera 42 and microphone 238 of the headset type terminal 314. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the preferences and behavior patterns of customers. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the optimal promotion based on the analysis result and generates content. The distribution unit is realized, for example, by the control unit 46A of the headset type terminal 314, and distributes the generated content to customers. The collection unit can, for example, estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0148] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0149] 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.

[0150] 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, the RAM 30, and the 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.

[0151] 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.

[0152] 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 the voice according to instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0154] 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 performed in a secure state.

[0155] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. 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.

[0156] 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.

[0157] 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.

[0158] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0159] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific 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 program 60 executed on the RAM 48. The robot 414 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.

[0160] 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 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0161] 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 a voice indicating a user input for 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.

[0162] 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. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while 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, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0163] 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 be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, 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.

[0164] Each of the multiple elements including the above-mentioned collection unit, analysis unit, generation unit, and distribution unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data on the behavior history and preferences of customers using the camera 42 and microphone 238 of the robot 414. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to grasp the preferences and behavior patterns of customers. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and determines the optimal promotion based on the analysis result and generates content. The distribution unit is realized, for example, by the control unit 46A of the robot 414, and distributes the generated content to customers. The collection unit can, for example, estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0165] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0166] FIG. 9 is a diagram showing 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. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0167] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually 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.

[0168] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0169] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0170] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is 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 the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the 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, "relief," "calm," and "encouraging," have similar emotion values.

[0172] 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.

[0173] 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 Universal Serial Bus (USB) 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.

[0174] In addition, 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 upon request from the data processing device 12.

[0175] 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.

[0176] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0177] The hardware resource that executes the specific process may be one of these various processors, or may be 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.

[0178] As an example of a configuration using one 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 configuration using a processor that realizes the functions of the 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.

[0179] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0180] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0181] The above description and illustrations are detailed descriptions 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, function, action, and effect is an example of the configuration, function, action, and effect 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 replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0182] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0183] (Appendix 1) A collection unit that collects data regarding customer behavior history or preferences; an analysis unit that analyzes the data collected by the collection unit and grasps customer preferences and behavior patterns; a generation unit that determines an appropriate promotion and generates content based on the analysis result obtained by the analysis unit; A distribution unit that distributes the content generated by the generation unit. A system characterized by: (Appendix 2) The collecting unit includes: Collect data on what products you view, what pages you visit, and what links you click 2. The system of claim 1, (Appendix 3) The analysis unit includes: Analyze collected data to understand customer preferences and behavioral patterns 2. The system of claim 1, (Appendix 4) The generation unit is Generate content such as videos, images, and comics related to a particular product for customers who are highly interested in that product 2. The system of claim 1, (Appendix 5) The distribution unit is Analyze customer behavioral history to determine the delivery method and time period that will maximize viewing probability and then implement promotions 2. The system of claim 1, (Appendix 6) The distribution unit is If your customers tend to check their email at certain times, send them promotional emails at those times. 2. The system of claim 1, (Appendix 7) The distribution unit is If customers frequent a particular website or app, run promotions on that platform. 2. The system of claim 1, (Appendix 8) The collecting unit includes: Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1, (Appendix 9) The collecting unit includes: Analyze users' past behavioral history and select the most appropriate data collection method 2. The system of claim 1, (Appendix 10) The collecting unit includes: Filter data as it is collected based on the user's current interests and life situation 2. The system of claim 1, (Appendix 11) The collecting unit includes: Infer user sentiment and prioritize data to be collected based on the inferred user sentiment 2. The system of claim 1, (Appendix 12) The collecting unit includes: When collecting data, prioritize collecting data that is more relevant by taking into account the user's geographic location. 2. The system of claim 1, (Appendix 13) The collecting unit includes: When collecting data, analyze your social media activity and collect relevant data 2. The system of claim 1, (Appendix 14) The analysis unit includes: Infer user sentiment and adjust data analysis methods based on the inferred user sentiment 2. The system of claim 1, (Appendix 15) The analysis unit includes: When analyzing data, adjust the level of analysis based on customer importance 2. The system of claim 1, (Appendix 16) The analysis unit includes: When analyzing data, apply different analytical algorithms depending on customer categories 2. The system of claim 1, (Appendix 17) The analysis unit includes: Infer user sentiment and adjust how analysis results are displayed based on the inferred user sentiment 2. The system of claim 1, (Appendix 18) The analysis unit includes: When analyzing data, prioritize analysis based on when customer behavior history was submitted 2. The system of claim 1, (Appendix 19) The analysis unit includes: When analyzing data, adjust the order of analysis based on customer relevance 2. The system of claim 1, (Appendix 20) The generation unit is To estimate a user's emotion and adjust a content generation method based on the estimated user's emotion. 2. The system of claim 1, (Appendix 21) The generation unit is When generating content, adjust the level of detail based on customer interest. 2. The system of claim 1, (Appendix 22) The generation unit is When generating content, apply different generation algorithms depending on customer categories 2. The system of claim 1, (Appendix 23) The generation unit is To estimate a user's emotion and determine a type of content to be generated based on the estimated user's emotion. 2. The system of claim 1, (Appendix 24) The generation unit is When generating content, prioritize the content to be generated based on when the customer's behavior history was submitted 2. The system of claim 1, (Appendix 25) The generation unit is When generating content, adjust the order of generated content based on customer relevance 2. The system of claim 1, (Appendix 26) The distribution unit is Inferring user emotions and adjusting delivery methods based on the estimated user emotions 2. The system of claim 1, (Appendix 27) The distribution unit is When delivering, analyze the customer's past behavior history to select the optimal delivery method. 2. The system of claim 1, (Appendix 28) The distribution unit is At the time of delivery, customize delivery based on the customer's current life situation 2. The system of claim 1, (Appendix 29) The distribution unit is Estimate user sentiment and determine delivery priorities based on the estimated user sentiment 2. The system of claim 1, (Appendix 30) The distribution unit is When delivering, the optimal delivery method is selected taking into account the customer's geographic location information 2. The system of claim 1, (Appendix 31) The distribution unit is At the time of delivery, analyze the customer's social media activity and suggest delivery methods 2. The system of claim 1, [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection unit that collects data regarding customer behavior history or preferences; an analysis unit that analyzes the data collected by the collection unit and grasps customer preferences and behavior patterns; a generation unit that determines an appropriate promotion and generates content based on the analysis result obtained by the analysis unit; A distribution unit that distributes the content generated by the generation unit. A system characterized by:

2. The collecting unit includes: Collect data on what products you view, what pages you visit, and what links you click 2. The system of claim 1 .

3. The generation unit is Generate content such as videos, images, and comics related to a particular product for customers who are highly interested in that product 2. The system of claim 1 .

4. The distribution unit is Analyze customer behavioral history to determine the delivery method and time period that will maximize viewing probability and then implement promotions 2. The system of claim 1 .

5. The distribution unit is If your customers tend to check their email at certain times, send them promotional emails at those times.

2. The system of claim 1 .

6. The distribution unit is If customers frequent a particular website or app, run promotions on that platform.

2. The system of claim 1 .

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

Images