system

Abstract

The system according to this embodiment aims to analyze customer behavior in the cookie-less era and propose optimal marketing strategies. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects first-party data such as customer behavior history, purchase history, and access history. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes optimal marketing measures based on the analysis results obtained by the analysis unit. The generation unit generates short videos based on the measures proposed by the proposal unit.

Description

Technical Field

[0006] ,

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, in the cookie-less era, the analysis of customer behavior and the proposal of optimal marketing measures have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to analyze customer behavior and propose optimal marketing measures in the cookie-less era.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects first-party data such as customer behavior history, purchase history, and access history. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes optimal marketing measures based on the analysis results obtained by the analysis unit. The generation unit generates short videos based on the measures proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze customer behavior in the cookie-less era and propose optimal marketing strategies. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 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". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The marketing system according to an embodiment of the present invention is a system that proposes new customer behavior analysis and marketing strategies in the cookie-less era. This marketing system uses conversational AI to evolve the content that customers use on a daily basis into a conversational format, thereby collecting first-party data such as customer behavior history, purchase history, and access history, and based on this, it implements optimal marketing strategies. For example, the marketing system uses conversational AI to update the content that customers use on a daily basis into a conversational format. For example, by making content such as news, entertainment, and shopping conversational, customers can acquire information through natural conversation. As a result, customers can enjoy the content they use on a daily basis while experiencing the advantages and appeal of conversational AI. Next, the marketing system collects first-party data such as customer behavior history, purchase history, and access history through the conversational AI. For example, data can be collected when a customer purchases a product or views specific content through the conversational AI. As a result, it is possible to understand the customer's interests and preferences and propose optimal marketing strategies for each individual customer. Furthermore, the marketing system generates short videos based on the collected first-party data. For example, if a customer is looking for restaurants in a specific area, a short video of recommended restaurants in that area can be generated and provided to the customer. This makes it easier for customers to visually absorb information and take proactive action on content that interests them. This mechanism allows for detailed analysis of customer behavior and the implementation of optimal marketing strategies, even in the age of cookies. For instance, if a customer shows high interest in a particular product, providing promotional information related to that product can increase the customer's purchase intent. Similarly, if a customer is looking for restaurants in a specific area, providing a short video of recommended restaurants in that area can encourage customer action.Thus, by using conversational AI for new customer behavior analysis and marketing strategies, effective marketing becomes possible even in the cookie-less era. This allows marketing systems to analyze customer behavior in detail and implement optimal marketing strategies, even in a cookie-less environment.

[0029] The marketing system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects first-party data such as customer behavior history, purchase history, and access history. For example, the collection unit collects clickstream data when a customer browses a website. The collection unit can also collect purchase history when a customer makes online purchases. Furthermore, the collection unit can collect access history when a customer views specific content. For example, the collection unit records the number of times a customer views a specific product page and the time spent on that page. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes customer behavior patterns to understand customer interests and preferences. The analysis unit can also analyze customer purchase history to understand trends in products purchased by the customer in the past. Furthermore, the analysis unit can analyze customer access history to identify the types of content that customers frequently view. For example, the analysis unit can identify that a customer frequently views products in a specific category. The proposal unit proposes optimal marketing measures based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, propose personalized offers based on the customer's interests. It can also suggest promotions for relevant products based on the customer's purchase history. Furthermore, it can recommend specific content based on the customer's browsing history. For example, it might suggest promotions related to products in categories the customer frequently views. The generation unit generates short videos based on the strategies proposed by the suggestion unit. For example, it might generate promotional videos about products the customer is interested in. It could also generate short videos about restaurants in areas the customer is interested in. Additionally, it can generate tutorial videos on topics the customer is interested in. For example, it might generate videos explaining how to use a product the customer is interested in.As a result, the marketing system according to this embodiment can collect and analyze first-party data such as customer behavior history, purchase history, and access history, propose optimal marketing measures, and generate short videos.

[0030] The data collection unit collects first-party data such as customer behavior history, purchase history, and access history. Specifically, it collects clickstream data when customers browse a website, recording detailed information such as which pages they visited, which links they clicked, and how long they spent on each page. This allows for a detailed understanding of customers' behavior patterns on the website. The data collection unit also collects purchase history when customers shop online. Purchase history includes information such as the type, quantity, price, and date of purchase of the purchased items, allowing for an understanding of customer purchasing trends. Furthermore, the data collection unit collects access history when customers view specific content. For example, it can record the number of times a customer viewed a specific product page and the time spent on that page, allowing for the identification of which content is appealing to customers. This data provides important clues for understanding customer interests and preferences. The data collection unit centrally manages and updates this data in real time, ensuring that the information is always up-to-date. In addition, the data collection unit can flexibly adjust the methods and frequency of data collection, and can focus data collection during specific campaigns or promotional periods. This allows the data collection unit to gain a detailed understanding of customer behavior and interests, providing data that forms the basis for marketing strategies.

[0031] The analytics department analyzes data collected by the data collection department to understand customer behavior patterns, interests, and preferences. Specifically, it analyzes customer behavior patterns to identify what products and services customers are interested in. For example, by analyzing product categories that customers frequently browse or their access tendencies at specific times of day, it is possible to reveal customer interests and preferences. The analytics department also analyzes customer purchase history to understand trends in products purchased in the past. This allows for predictions of what products customers prefer to purchase and forecasts future purchasing behavior. Furthermore, the analytics department analyzes customer access history to identify the types of content that customers frequently view. For example, if a customer frequently views products in a particular category, it can suggest promotions and offers related to that category. By integrating this data and understanding overall customer behavior patterns, interests, and preferences, the analytics department provides a foundation for developing more accurate marketing strategies. In addition, the analytics department can utilize AI to analyze data in real time and provide fast and accurate results. The AI ​​uses machine learning algorithms to analyze data and automatically identify customer behavior patterns, interests, and preferences. This allows the analysis unit to efficiently process vast amounts of data and respond quickly to customer needs.

[0032] The Proposal Department proposes optimal marketing strategies based on the analysis results obtained by the Analysis Department. Specifically, it proposes personalized offers based on customer interests. For example, by offering special discounts or limited-time offers related to product categories that customers frequently browse, it can increase customer purchasing intent. The Proposal Department can also propose promotions for related products based on customer purchase history. For example, by recommending products highly related to products previously purchased, it can promote cross-selling and upselling. Furthermore, the Proposal Department can propose recommendations for specific content based on customer access history. For example, by suggesting blog posts or video content related to product categories that customers frequently browse, it can continue to attract customer interest. The Proposal Department can automate these proposals and deliver them to customers in a timely manner. For example, it can display personalized offers in real time when a customer visits the website. In addition, the Proposal Department can collect customer feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the Proposal Department to provide optimal marketing strategies that meet customer needs and improve customer satisfaction.

[0033] The generation unit generates short videos based on the measures proposed by the proposal unit. Specifically, it generates promotional videos related to products that customers are interested in. For example, it can generate promotional videos related to product categories that customers frequently browse, visually conveying the appeal of those products. The generation unit can also generate short videos about restaurants in areas that customers are interested in. For example, if a customer is interested in a particular region, it can generate videos introducing popular restaurants and recommended menu items in that region to attract the customer's attention. Furthermore, the generation unit can generate tutorial videos on themes that customers are interested in. For example, if a customer is interested in a particular product, it can generate videos explaining how to use and maintain that product to increase the customer's willingness to purchase. The generation unit can use AI to automatically generate videos and quickly provide content tailored to customer interests. The AI ​​analyzes the customer's behavior history, interests, and preferences, and generates optimal video content based on that analysis. This allows the generation unit to provide personalized video content to customers and improve customer engagement.

[0034] The data collection unit can collect data such as customer behavior history, purchase history, and access history through conversational AI. For example, the data collection unit collects data through conversations with customers using conversational AI. For example, the data collection unit collects data when a customer purchases a product through conversational AI. The data collection unit can also collect data when a customer views specific content through conversational AI. For example, the data collection unit collects data when a customer views a news article through conversational AI. Furthermore, the data collection unit can collect data when a customer enjoys entertainment content through conversational AI. For example, the data collection unit collects data when a customer watches a movie through conversational AI. In this way, the data collection unit can collect data such as customer behavior history, purchase history, and access history through conversational AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data collected through conversational AI into an AI model and have the AI ​​perform data analysis.

[0035] The analytics unit can analyze the collected data to understand customer interests and preferences. For example, the analytics unit can use the collected data to analyze customer behavior patterns. For instance, it can identify the types of content that customers frequently view. The analytics unit can also analyze customer purchase history to understand trends in products customers have purchased in the past. For example, it can identify that customers frequently purchase products from a particular brand. Furthermore, the analytics unit can analyze customer access history to identify themes that customers are interested in. For example, it can identify that customers frequently view articles in a particular category. In this way, the analytics unit can analyze the collected data to understand customer interests and preferences. Some or all of the above processing in the analytics unit may be performed using AI, for example, or not. For example, the analytics unit can input the collected data into an AI model and have the AI ​​perform the analysis of customer interests and preferences.

[0036] The proposal unit can propose the most suitable marketing strategies for individual customers based on the analysis results. For example, the proposal unit can propose personalized offers based on the customer's interests. For example, the proposal unit can propose special discounts on products that the customer is interested in. The proposal unit can also propose promotions for related products based on the customer's purchase history. For example, the proposal unit can propose promotions for new products related to products that the customer has previously purchased. Furthermore, the proposal unit can also propose recommendations for specific content based on the customer's access history. For example, the proposal unit can propose new articles related to articles in categories that the customer frequently views. In this way, the proposal unit can propose the most suitable marketing strategies for individual customers based on the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the analysis results into an AI model and have the AI ​​execute the proposal of the most suitable marketing strategies.

[0037] The generation unit can generate short videos based on the proposed measures. For example, the generation unit can generate promotional videos about products that the customer is interested in. For example, the generation unit can generate videos that introduce the features and benefits of products that the customer is interested in. The generation unit can also generate short videos about restaurants in areas that the customer is interested in. For example, the generation unit can generate videos that introduce recommended restaurants in areas that the customer is interested in. Furthermore, the generation unit can also generate tutorial videos on themes that the customer is interested in. For example, the generation unit can generate videos that explain how to use products that the customer is interested in. In this way, the generation unit can generate short videos based on the proposed measures. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the proposed measures into a generation AI and have the generation AI perform the generation of short videos.

[0038] The generation unit can provide the generated short video to the customer. For example, the generation unit can send the generated short video to the customer's smartphone via push notification. The generation unit can also send the generated short video to the customer's email address. Furthermore, the generation unit can post the generated short video to the social media platform used by the customer. For example, the generation unit can upload the short video to a video sharing site used by the customer and notify the customer. In this way, the generation unit can provide the generated short video to the customer. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the generated short video into an AI model and have the AI ​​execute the optimal distribution method.

[0039] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from devices the user has frequently used in the past. The data collection unit can also collect data from applications the user has preferred to use in the past. Furthermore, the data collection unit can analyze the user's past behavior patterns and select the most efficient data collection method. For example, the data collection unit prioritizes collecting data from devices the user has frequently used in the past. This allows the data collection unit to analyze the user's past behavior history and select the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history into an AI model and have the AI ​​select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current interests and preferences during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. The data collection unit can also filter data based on keywords the user has recently searched for. Furthermore, the data collection unit can collect relevant data based on content the user has viewed. For example, the data collection unit can prioritize collecting data related to keywords the user has recently searched for. This allows the data collection unit to filter data based on the user's current interests and preferences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform data filtering using an AI model that filters based on the user's current interests and preferences.

[0041] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect data based on places the user has visited in the past. Furthermore, the data collection unit can analyze the user's travel history and collect highly relevant data. For example, the data collection unit can prioritize the collection of data related to the user's current location. This allows the data collection unit to prioritize the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​perform the collection of highly relevant data.

[0042] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on content shared by the user on social media. The data collection unit can also analyze the activity of accounts followed by the user and collect relevant data. Furthermore, the data collection unit can collect data related to the user's interests based on their social media activity history. For example, the data collection unit can collect data based on content shared by the user on social media. This allows the data collection unit to analyze the user's social media activity and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into an AI model and have the AI ​​perform the collection of relevant data.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to provide deep insights. Alternatively, it can perform a concise analysis on less important data to provide concise results. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the data importance into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. It can also apply a behavior pattern analysis algorithm to behavior history data. Furthermore, it can apply a web traffic analysis algorithm to access history data. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. This allows the analysis unit to apply different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into an AI model and have the AI ​​execute the application of different analysis algorithms.

[0045] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. The analysis unit can also analyze historical data to grasp long-term trends. Furthermore, the analysis unit can optimally allocate analysis resources according to the data collection timing. For example, the analysis unit prioritizes the analysis of the most recent data to provide real-time insights. This allows the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into an AI model and have the AI ​​determine the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. Alternatively, the analysis unit can postpone the analysis of less relevant data to perform more efficient analysis. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into an AI model and have the AI ​​perform the adjustment of the analysis order.

[0047] The proposal department can adjust the level of detail in its proposals based on the importance of the marketing initiatives. For example, it can provide detailed proposals and deep insights for important marketing initiatives. Conversely, it can provide concise proposals and key information for less important marketing initiatives. Furthermore, the proposal department can optimally allocate resources to proposals according to the importance of the marketing initiatives. For example, it can provide detailed proposals for important marketing initiatives. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the marketing initiatives. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the marketing initiatives into an AI model and have the AI ​​adjust the level of detail in the proposals.

[0048] The suggestion unit can apply different suggestion algorithms depending on the customer's attributes when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm based on age group to make the best suggestion. It can also apply a suggestion algorithm based on gender to make the best suggestion. Furthermore, the suggestion unit can apply a suggestion algorithm based on purchase history to make the best suggestion. For example, the suggestion unit can apply a suggestion algorithm based on age group to make the best suggestion. This allows the suggestion unit to apply different suggestion algorithms depending on the customer's attributes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input customer attributes into an AI model and have the AI ​​apply different suggestion algorithms.

[0049] The proposal department can determine the priority of proposals based on the implementation timing of marketing measures. For example, the proposal department will prioritize proposals for immediate marketing measures. It can also provide detailed proposals for long-term marketing measures. Furthermore, the proposal department can optimally allocate resources to proposals according to the implementation timing of marketing measures. For example, it will prioritize proposals for immediate marketing measures. This allows the proposal department to determine the priority of proposals based on the implementation timing of marketing measures. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the implementation timing of marketing measures into an AI model and have the AI ​​determine the priority of proposals.

[0050] The proposal department can adjust the order of proposals based on the relevance of the marketing measures. For example, the proposal department can prioritize proposing highly relevant marketing measures. It can also postpone marketing measures with low relevance. Furthermore, the proposal department can optimally allocate resources to proposals according to the relevance of the marketing measures. For example, the proposal department can prioritize proposing highly relevant marketing measures. This allows the proposal department to adjust the order of proposals based on the relevance of the marketing measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the relevance of the marketing measures into an AI model and have the AI ​​perform the adjustment of the order of proposals.

[0051] The generation unit can adjust the level of detail in short videos based on the customer's interests. For example, if a customer is interested in a particular genre, the generation unit will generate a video containing detailed information related to that genre. Furthermore, if a customer has broad interests, the generation unit can generate a video covering multiple genres. In addition, the generation unit can generate videos containing interesting and detailed information based on the customer's past viewing history. For example, if a customer is interested in a particular genre, the generation unit will generate a video containing detailed information related to that genre. This allows the generation unit to adjust the level of detail in videos based on the customer's interests. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the customer's interests into a generation AI model and have the generation AI adjust the level of detail in the videos.

[0052] The generation unit can apply different generation algorithms depending on the customer's attributes when generating short videos. For example, the generation unit can apply a generation algorithm based on age group to generate the optimal video. It can also apply a generation algorithm based on gender to generate the optimal video. Furthermore, the generation unit can apply a generation algorithm based on purchase history to generate the optimal video. For example, the generation unit can apply a generation algorithm based on age group to generate the optimal video. This allows the generation unit to apply different generation algorithms depending on the customer's attributes. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input customer attributes into a generation AI model and have the generation AI execute the application of different generation algorithms.

[0053] The generation unit can generate highly relevant videos based on the customer's geographical location information when generating short videos. For example, the generation unit can generate videos related to the customer's current location. It can also generate videos based on places the customer has visited in the past. Furthermore, the generation unit can analyze the customer's travel history and generate highly relevant videos. For example, the generation unit can generate videos related to the customer's current location. This allows the generation unit to generate highly relevant videos based on the customer's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the customer's geographical location information into a generation AI model and have the generation AI perform the generation of highly relevant videos.

[0054] The generation unit can analyze the customer's social media activity and generate relevant videos when generating short videos. For example, the generation unit can generate videos based on content shared by the customer on social media. The generation unit can also analyze the activity of accounts followed by the customer and generate relevant videos. Furthermore, the generation unit can generate videos related to the customer's interests based on their social media activity history. For example, the generation unit can generate videos based on content shared by the customer on social media. This allows the generation unit to analyze the customer's social media activity and generate relevant videos. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the customer's social media activity into a generation AI model and have the generation AI perform the generation of relevant videos.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] Marketing systems can further incorporate incentive programs to increase customer purchasing intent. These programs could, for example, award points to customers when they purchase specific products. They could also offer discount coupons to customers who spend a certain amount. Furthermore, they could provide benefits to both the referrer and the referred customer when a customer introduces a friend. This allows the incentive program to increase customer purchasing intent and build repeat business.

[0057] The marketing system can also include a feedback collection unit to gather customer feedback. For example, the feedback collection unit could send a survey after a customer purchases a product to evaluate their satisfaction. It could also request feedback via pop-ups when customers use the website. Furthermore, the feedback collection unit could collect comments customers make about the product on social media. This allows the feedback collection unit to reflect customer opinions and use them to improve the service.

[0058] Marketing systems can also include a predictive unit that forecasts customer behavior. For example, the predictive unit can predict which products a customer is most likely to purchase next based on their past purchase history. It can also predict which content a customer is most likely to view next based on their browsing history. Furthermore, it can analyze customer behavior patterns and predict which places they are most likely to visit next. This allows the predictive unit to anticipate customer needs and propose optimal marketing strategies.

[0059] Marketing systems can further incorporate gamification elements to encourage customer purchasing behavior. For example, the gamification element could offer games where customers earn points by completing specific tasks. It could also provide a system where customers level up by purchasing products. Furthermore, it could offer a ranking system where customers can compete with friends. In this way, the gamification element can make the purchasing process more enjoyable and increase customer motivation.

[0060] The marketing system can also include an assistant unit to further support customer purchasing behavior. For example, the assistant unit can suggest the most suitable products when a customer is searching for something. It can also provide support during the purchase process. Furthermore, the assistant unit can provide immediate answers to customer questions about products. In this way, the assistant unit can improve the customer's purchasing experience and support a smoother purchasing process.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The data collection unit collects first-party data such as customer behavior history, purchase history, and access history. For example, it collects clickstream data when a customer browses a website, purchase history when they shop online, and access history when they view specific content. The data collection unit can also record the number of times a customer views a specific product page and the time spent on that page. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes customer behavior patterns to understand customer interests and preferences. It can also analyze customer purchase history to understand trends in products purchased in the past. Furthermore, it can analyze customer access history to identify the types of content that are frequently viewed. Step 3: The proposal department proposes optimal marketing strategies based on the analysis results obtained by the analysis department. For example, they may propose personalized offers based on customer interests and preferences, or suggest promotions for related products based on purchase history. Furthermore, they can also suggest recommendations for specific content based on access history. Step 4: The generation unit generates short videos based on the measures proposed by the proposal unit. For example, it generates promotional videos about products the customer is interested in, short videos about restaurants in areas of interest, or tutorial videos on topics of interest.

[0063] (Example of form 2) The marketing system according to an embodiment of the present invention is a system that proposes new customer behavior analysis and marketing strategies in the cookie-less era. This marketing system uses conversational AI to evolve the content that customers use on a daily basis into a conversational format, thereby collecting first-party data such as customer behavior history, purchase history, and access history, and based on this, it implements optimal marketing strategies. For example, the marketing system uses conversational AI to update the content that customers use on a daily basis into a conversational format. For example, by making content such as news, entertainment, and shopping conversational, customers can acquire information through natural conversation. As a result, customers can enjoy the content they use on a daily basis while experiencing the advantages and appeal of conversational AI. Next, the marketing system collects first-party data such as customer behavior history, purchase history, and access history through the conversational AI. For example, data can be collected when a customer purchases a product or views specific content through the conversational AI. As a result, it is possible to understand the customer's interests and preferences and propose optimal marketing strategies for each individual customer. Furthermore, the marketing system generates short videos based on the collected first-party data. For example, if a customer is looking for restaurants in a specific area, a short video of recommended restaurants in that area can be generated and provided to the customer. This makes it easier for customers to visually absorb information and take proactive action on content that interests them. This mechanism allows for detailed analysis of customer behavior and the implementation of optimal marketing strategies, even in the age of cookies. For instance, if a customer shows high interest in a particular product, providing promotional information related to that product can increase the customer's purchase intent. Similarly, if a customer is looking for restaurants in a specific area, providing a short video of recommended restaurants in that area can encourage customer action.Thus, by using conversational AI for new customer behavior analysis and marketing strategies, effective marketing becomes possible even in the cookie-less era. This allows marketing systems to analyze customer behavior in detail and implement optimal marketing strategies, even in a cookie-less environment.

[0064] The marketing system according to this embodiment comprises a collection unit, an analysis unit, a proposal unit, and a generation unit. The collection unit collects first-party data such as customer behavior history, purchase history, and access history. For example, the collection unit collects clickstream data when a customer browses a website. The collection unit can also collect purchase history when a customer makes online purchases. Furthermore, the collection unit can collect access history when a customer views specific content. For example, the collection unit records the number of times a customer views a specific product page and the time spent on that page. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes customer behavior patterns to understand customer interests and preferences. The analysis unit can also analyze customer purchase history to understand trends in products purchased by the customer in the past. Furthermore, the analysis unit can analyze customer access history to identify the types of content that customers frequently view. For example, the analysis unit can identify that a customer frequently views products in a specific category. The proposal unit proposes optimal marketing measures based on the analysis results obtained by the analysis unit. The suggestion unit can, for example, propose personalized offers based on the customer's interests. It can also suggest promotions for relevant products based on the customer's purchase history. Furthermore, it can recommend specific content based on the customer's browsing history. For example, it might suggest promotions related to products in categories the customer frequently views. The generation unit generates short videos based on the strategies proposed by the suggestion unit. For example, it might generate promotional videos about products the customer is interested in. It could also generate short videos about restaurants in areas the customer is interested in. Additionally, it can generate tutorial videos on topics the customer is interested in. For example, it might generate videos explaining how to use a product the customer is interested in.As a result, the marketing system according to this embodiment can collect and analyze first-party data such as customer behavior history, purchase history, and access history, propose optimal marketing measures, and generate short videos.

[0065] The data collection unit collects first-party data such as customer behavior history, purchase history, and access history. Specifically, it collects clickstream data when customers browse a website, recording detailed information such as which pages they visited, which links they clicked, and how long they spent on each page. This allows for a detailed understanding of customers' behavior patterns on the website. The data collection unit also collects purchase history when customers shop online. Purchase history includes information such as the type, quantity, price, and date of purchase of the purchased items, allowing for an understanding of customer purchasing trends. Furthermore, the data collection unit collects access history when customers view specific content. For example, it can record the number of times a customer viewed a specific product page and the time spent on that page, allowing for the identification of which content is appealing to customers. This data provides important clues for understanding customer interests and preferences. The data collection unit centrally manages and updates this data in real time, ensuring that the information is always up-to-date. In addition, the data collection unit can flexibly adjust the methods and frequency of data collection, and can focus data collection during specific campaigns or promotional periods. This allows the data collection unit to gain a detailed understanding of customer behavior and interests, providing data that forms the basis for marketing strategies.

[0066] The analytics department analyzes data collected by the data collection department to understand customer behavior patterns, interests, and preferences. Specifically, it analyzes customer behavior patterns to identify what products and services customers are interested in. For example, by analyzing product categories that customers frequently browse or their access tendencies at specific times of day, it is possible to reveal customer interests and preferences. The analytics department also analyzes customer purchase history to understand trends in products purchased in the past. This allows for predictions of what products customers prefer to purchase and forecasts future purchasing behavior. Furthermore, the analytics department analyzes customer access history to identify the types of content that customers frequently view. For example, if a customer frequently views products in a particular category, it can suggest promotions and offers related to that category. By integrating this data and understanding overall customer behavior patterns, interests, and preferences, the analytics department provides a foundation for developing more accurate marketing strategies. In addition, the analytics department can utilize AI to analyze data in real time and provide fast and accurate results. The AI ​​uses machine learning algorithms to analyze data and automatically identify customer behavior patterns, interests, and preferences. This allows the analysis unit to efficiently process vast amounts of data and respond quickly to customer needs.

[0067] The Proposal Department proposes optimal marketing strategies based on the analysis results obtained by the Analysis Department. Specifically, it proposes personalized offers based on customer interests. For example, by offering special discounts or limited-time offers related to product categories that customers frequently browse, it can increase customer purchasing intent. The Proposal Department can also propose promotions for related products based on customer purchase history. For example, by recommending products highly related to products previously purchased, it can promote cross-selling and upselling. Furthermore, the Proposal Department can propose recommendations for specific content based on customer access history. For example, by suggesting blog posts or video content related to product categories that customers frequently browse, it can continue to attract customer interest. The Proposal Department can automate these proposals and deliver them to customers in a timely manner. For example, it can display personalized offers in real time when a customer visits the website. In addition, the Proposal Department can collect customer feedback and continuously improve the accuracy and effectiveness of its proposals. This allows the Proposal Department to provide optimal marketing strategies that meet customer needs and improve customer satisfaction.

[0068] The generation unit generates short videos based on the measures proposed by the proposal unit. Specifically, it generates promotional videos related to products that customers are interested in. For example, it can generate promotional videos related to product categories that customers frequently browse, visually conveying the appeal of those products. The generation unit can also generate short videos about restaurants in areas that customers are interested in. For example, if a customer is interested in a particular region, it can generate videos introducing popular restaurants and recommended menu items in that region to attract the customer's attention. Furthermore, the generation unit can generate tutorial videos on themes that customers are interested in. For example, if a customer is interested in a particular product, it can generate videos explaining how to use and maintain that product to increase the customer's willingness to purchase. The generation unit can use AI to automatically generate videos and quickly provide content tailored to customer interests. The AI ​​analyzes the customer's behavior history, interests, and preferences, and generates optimal video content based on that analysis. This allows the generation unit to provide personalized video content to customers and improve customer engagement.

[0069] The data collection unit can collect data such as customer behavior history, purchase history, and access history through conversational AI. For example, the data collection unit collects data through conversations with customers using conversational AI. For example, the data collection unit collects data when a customer purchases a product through conversational AI. The data collection unit can also collect data when a customer views specific content through conversational AI. For example, the data collection unit collects data when a customer views a news article through conversational AI. Furthermore, the data collection unit can collect data when a customer enjoys entertainment content through conversational AI. For example, the data collection unit collects data when a customer watches a movie through conversational AI. In this way, the data collection unit can collect data such as customer behavior history, purchase history, and access history through conversational AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the data collected through conversational AI into an AI model and have the AI ​​perform data analysis.

[0070] The analytics unit can analyze the collected data to understand customer interests and preferences. For example, the analytics unit can use the collected data to analyze customer behavior patterns. For instance, it can identify the types of content that customers frequently view. The analytics unit can also analyze customer purchase history to understand trends in products customers have purchased in the past. For example, it can identify that customers frequently purchase products from a particular brand. Furthermore, the analytics unit can analyze customer access history to identify themes that customers are interested in. For example, it can identify that customers frequently view articles in a particular category. In this way, the analytics unit can analyze the collected data to understand customer interests and preferences. Some or all of the above processing in the analytics unit may be performed using AI, for example, or not. For example, the analytics unit can input the collected data into an AI model and have the AI ​​perform the analysis of customer interests and preferences.

[0071] The proposal unit can propose the most suitable marketing strategies for individual customers based on the analysis results. For example, the proposal unit can propose personalized offers based on the customer's interests. For example, the proposal unit can propose special discounts on products that the customer is interested in. The proposal unit can also propose promotions for related products based on the customer's purchase history. For example, the proposal unit can propose promotions for new products related to products that the customer has previously purchased. Furthermore, the proposal unit can also propose recommendations for specific content based on the customer's access history. For example, the proposal unit can propose new articles related to articles in categories that the customer frequently views. In this way, the proposal unit can propose the most suitable marketing strategies for individual customers based on the analysis results. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input the analysis results into an AI model and have the AI ​​execute the proposal of the most suitable marketing strategies.

[0072] The generation unit can generate short videos based on the proposed measures. For example, the generation unit can generate promotional videos about products that the customer is interested in. For example, the generation unit can generate videos that introduce the features and benefits of products that the customer is interested in. The generation unit can also generate short videos about restaurants in areas that the customer is interested in. For example, the generation unit can generate videos that introduce recommended restaurants in areas that the customer is interested in. Furthermore, the generation unit can also generate tutorial videos on themes that the customer is interested in. For example, the generation unit can generate videos that explain how to use products that the customer is interested in. In this way, the generation unit can generate short videos based on the proposed measures. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input the proposed measures into a generation AI and have the generation AI perform the generation of short videos.

[0073] The generation unit can provide the generated short video to the customer. For example, the generation unit can send the generated short video to the customer's smartphone via push notification. The generation unit can also send the generated short video to the customer's email address. Furthermore, the generation unit can post the generated short video to the social media platform used by the customer. For example, the generation unit can upload the short video to a video sharing site used by the customer and notify the customer. In this way, the generation unit can provide the generated short video to the customer. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the generated short video into an AI model and have the AI ​​execute the optimal distribution method.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can refrain from collecting data and collect it when the user is relaxed. Furthermore, if the user is excited, the data collection unit can collect data in real time and analyze it immediately. Additionally, if the user is tired, the data collection unit can delay data collection and resume it after rest. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the data collection unit to adjust the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input image data of the user captured by the camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0075] The data collection unit can analyze the user's past behavior history and select the optimal data collection method. For example, the data collection unit can prioritize collecting data from devices the user has frequently used in the past. The data collection unit can also collect data from applications the user has preferred to use in the past. Furthermore, the data collection unit can analyze the user's past behavior patterns and select the most efficient data collection method. For example, the data collection unit prioritizes collecting data from devices the user has frequently used in the past. This allows the data collection unit to analyze the user's past behavior history and select the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past behavior history into an AI model and have the AI ​​select the optimal data collection method.

[0076] The data collection unit can filter data based on the user's current interests and preferences during data collection. For example, the data collection unit can prioritize collecting data related to topics the user is currently interested in. The data collection unit can also filter data based on keywords the user has recently searched for. Furthermore, the data collection unit can collect relevant data based on content the user has viewed. For example, the data collection unit can prioritize collecting data related to keywords the user has recently searched for. This allows the data collection unit to filter data based on the user's current interests and preferences. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can perform data filtering using an AI model that filters based on the user's current interests and preferences.

[0077] The data collection unit can estimate the user's emotions and prioritize the data to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed data. If the user is in a hurry, the data collection unit can also prioritize collecting only the most important data. Furthermore, if the user is excited, the data collection unit can increase the priority of data to collect in real time. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the data collection unit to prioritize the data to collect based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input image data of the user captured by the camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0078] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of data related to the user's current location. The data collection unit can also collect data based on places the user has visited in the past. Furthermore, the data collection unit can analyze the user's travel history and collect highly relevant data. For example, the data collection unit can prioritize the collection of data related to the user's current location. This allows the data collection unit to prioritize the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into an AI model and have the AI ​​perform the collection of highly relevant data.

[0079] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect data based on content shared by the user on social media. The data collection unit can also analyze the activity of accounts followed by the user and collect relevant data. Furthermore, the data collection unit can collect data related to the user's interests based on their social media activity history. For example, the data collection unit can collect data based on content shared by the user on social media. This allows the data collection unit to analyze the user's social media activity and collect relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the user's social media activity into an AI model and have the AI ​​perform the collection of relevant data.

[0080] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a concise analysis and provide results that get straight to the point. Furthermore, if the user is excited, the analysis unit can perform a real-time analysis and provide results immediately. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to adjust the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on important data to provide deep insights. Alternatively, it can perform a concise analysis on less important data to provide concise results. Furthermore, the analysis unit can optimally allocate analysis resources according to the importance of the data. For example, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the data importance into an AI model and have the AI ​​perform the adjustment of the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. It can also apply a behavior pattern analysis algorithm to behavior history data. Furthermore, it can apply a web traffic analysis algorithm to access history data. For example, the analysis unit can apply a purchase pattern analysis algorithm to purchase history data. This allows the analysis unit to apply different analysis algorithms depending on the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into an AI model and have the AI ​​execute the application of different analysis algorithms.

[0083] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit may prioritize detailed analysis. It can also prioritize the analysis of important data if the user is in a hurry. Furthermore, if the user is excited, the analysis unit can perform real-time analysis and provide results immediately. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the analysis unit to determine the priority of analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0084] The analysis unit can determine the priority of analysis based on the data collection timing during analysis. For example, the analysis unit can prioritize the analysis of the most recent data to provide real-time insights. The analysis unit can also analyze historical data to grasp long-term trends. Furthermore, the analysis unit can optimally allocate analysis resources according to the data collection timing. For example, the analysis unit prioritizes the analysis of the most recent data to provide real-time insights. This allows the analysis unit to determine the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into an AI model and have the AI ​​determine the analysis priority.

[0085] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. Alternatively, the analysis unit can postpone the analysis of less relevant data to perform more efficient analysis. Furthermore, the analysis unit can optimally allocate analysis resources according to the relevance of the data. For example, the analysis unit can prioritize the analysis of highly relevant data to provide highly accurate results. This allows the analysis unit to adjust the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the relevance of the data into an AI model and have the AI ​​perform the adjustment of the analysis order.

[0086] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and deeper insights. If the user is in a hurry, the suggestion unit can provide concise suggestions and get straight to the point. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions to capture their interest. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to adjust the way it presents its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0087] The proposal department can adjust the level of detail in its proposals based on the importance of the marketing initiatives. For example, it can provide detailed proposals and deep insights for important marketing initiatives. Conversely, it can provide concise proposals and key information for less important marketing initiatives. Furthermore, the proposal department can optimally allocate resources to proposals according to the importance of the marketing initiatives. For example, it can provide detailed proposals for important marketing initiatives. This allows the proposal department to adjust the level of detail in its proposals based on the importance of the marketing initiatives. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the importance of the marketing initiatives into an AI model and have the AI ​​adjust the level of detail in the proposals.

[0088] The suggestion unit can apply different suggestion algorithms depending on the customer's attributes when making a suggestion. For example, the suggestion unit can apply a suggestion algorithm based on age group to make the best suggestion. It can also apply a suggestion algorithm based on gender to make the best suggestion. Furthermore, the suggestion unit can apply a suggestion algorithm based on purchase history to make the best suggestion. For example, the suggestion unit can apply a suggestion algorithm based on age group to make the best suggestion. This allows the suggestion unit to apply different suggestion algorithms depending on the customer's attributes. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input customer attributes into an AI model and have the AI ​​apply different suggestion algorithms.

[0089] The suggestion unit can estimate the user's emotions and adjust the length of its suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions and deeper insights. If the user is in a hurry, the suggestion unit can provide concise suggestions and get straight to the point. Furthermore, if the user is excited, the suggestion unit can provide visually stimulating suggestions to capture their interest. For example, the suggestion unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the suggestion unit to adjust the length of its suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input image data of the user captured by a camera into a generative AI and have the generative AI perform the user's emotion estimation.

[0090] The proposal department can determine the priority of proposals based on the implementation timing of marketing measures. For example, the proposal department will prioritize proposals for immediate marketing measures. It can also provide detailed proposals for long-term marketing measures. Furthermore, the proposal department can optimally allocate resources to proposals according to the implementation timing of marketing measures. For example, it will prioritize proposals for immediate marketing measures. This allows the proposal department to determine the priority of proposals based on the implementation timing of marketing measures. Some or all of the above processes in the proposal department may be performed using AI, or not. For example, the proposal department can input the implementation timing of marketing measures into an AI model and have the AI ​​determine the priority of proposals.

[0091] The proposal department can adjust the order of proposals based on the relevance of the marketing measures. For example, the proposal department can prioritize proposing highly relevant marketing measures. It can also postpone marketing measures with low relevance. Furthermore, the proposal department can optimally allocate resources to proposals according to the relevance of the marketing measures. For example, the proposal department can prioritize proposing highly relevant marketing measures. This allows the proposal department to adjust the order of proposals based on the relevance of the marketing measures. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input the relevance of the marketing measures into an AI model and have the AI ​​perform the adjustment of the order of proposals.

[0092] The generation unit can estimate the user's emotions and adjust the content of the short video it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a video that progresses at a leisurely pace. If the user is in a hurry, the generation unit can also generate a video that emphasizes the shortest route. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the generation unit to adjust the content of the short video it generates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input image data of the user captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0093] The generation unit can adjust the level of detail in short videos based on the customer's interests. For example, if a customer is interested in a particular genre, the generation unit will generate a video containing detailed information related to that genre. Furthermore, if a customer has broad interests, the generation unit can generate a video covering multiple genres. In addition, the generation unit can generate videos containing interesting and detailed information based on the customer's past viewing history. For example, if a customer is interested in a particular genre, the generation unit will generate a video containing detailed information related to that genre. This allows the generation unit to adjust the level of detail in videos based on the customer's interests. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the customer's interests into a generation AI model and have the generation AI adjust the level of detail in the videos.

[0094] The generation unit can apply different generation algorithms depending on the customer's attributes when generating short videos. For example, the generation unit can apply a generation algorithm based on age group to generate the optimal video. It can also apply a generation algorithm based on gender to generate the optimal video. Furthermore, the generation unit can apply a generation algorithm based on purchase history to generate the optimal video. For example, the generation unit can apply a generation algorithm based on age group to generate the optimal video. This allows the generation unit to apply different generation algorithms depending on the customer's attributes. Some or all of the above processing in the generation unit may be performed using a generation AI, for example, or without a generation AI. For example, the generation unit can input customer attributes into a generation AI model and have the generation AI execute the application of different generation algorithms.

[0095] The generation unit can estimate the user's emotions and adjust the length of the short video it generates based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate a longer video. If the user is in a hurry, the generation unit can also generate a short, concise video. Furthermore, if the user is excited, the generation unit can generate a video with visually stimulating effects. For example, the generation unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. This allows the generation unit to adjust the length of the short video it generates based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI, or not. For example, the generation unit can input image data of the user captured by a camera into the generation AI and have the generation AI perform the estimation of the user's emotions.

[0096] The generation unit can generate highly relevant videos based on the customer's geographical location information when generating short videos. For example, the generation unit can generate videos related to the customer's current location. It can also generate videos based on places the customer has visited in the past. Furthermore, the generation unit can analyze the customer's travel history and generate highly relevant videos. For example, the generation unit can generate videos related to the customer's current location. This allows the generation unit to generate highly relevant videos based on the customer's geographical location information. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the customer's geographical location information into a generation AI model and have the generation AI perform the generation of highly relevant videos.

[0097] The generation unit can analyze the customer's social media activity and generate relevant videos when generating short videos. For example, the generation unit can generate videos based on content shared by the customer on social media. The generation unit can also analyze the activity of accounts followed by the customer and generate relevant videos. Furthermore, the generation unit can generate videos related to the customer's interests based on their social media activity history. For example, the generation unit can generate videos based on content shared by the customer on social media. This allows the generation unit to analyze the customer's social media activity and generate relevant videos. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can input the customer's social media activity into a generation AI model and have the generation AI perform the generation of relevant videos.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] Marketing systems can further incorporate incentive programs to increase customer purchasing intent. These programs could, for example, award points to customers when they purchase specific products. They could also offer discount coupons to customers who spend a certain amount. Furthermore, they could provide benefits to both the referrer and the referred customer when a customer introduces a friend. This allows the incentive program to increase customer purchasing intent and build repeat business.

[0100] The marketing system can also include a feedback collection unit to gather customer feedback. For example, the feedback collection unit could send a survey after a customer purchases a product to evaluate their satisfaction. It could also request feedback via pop-ups when customers use the website. Furthermore, the feedback collection unit could collect comments customers make about the product on social media. This allows the feedback collection unit to reflect customer opinions and use them to improve the service.

[0101] Marketing systems can also include a predictive unit that forecasts customer behavior. For example, the predictive unit can predict which products a customer is most likely to purchase next based on their past purchase history. It can also predict which content a customer is most likely to view next based on their browsing history. Furthermore, it can analyze customer behavior patterns and predict which places they are most likely to visit next. This allows the predictive unit to anticipate customer needs and propose optimal marketing strategies.

[0102] Marketing systems can further incorporate gamification elements to encourage customer purchasing behavior. For example, the gamification element could offer games where customers earn points by completing specific tasks. It could also provide a system where customers level up by purchasing products. Furthermore, it could offer a ranking system where customers can compete with friends. In this way, the gamification element can make the purchasing process more enjoyable and increase customer motivation.

[0103] The marketing system can also include an assistant unit to further support customer purchasing behavior. For example, the assistant unit can suggest the most suitable products when a customer is searching for something. It can also provide support during the purchase process. Furthermore, the assistant unit can provide immediate answers to customer questions about products. In this way, the assistant unit can improve the customer's purchasing experience and support a smoother purchasing process.

[0104] Marketing systems can further estimate customer emotions and adjust how ads are displayed based on those estimates. For example, if a customer is relaxed, ads with a calm tone can be displayed. If a customer is excited, visually stimulating ads can be displayed. Furthermore, if a customer is stressed, relaxing ads can be displayed. This maximizes the effectiveness of advertising by tailoring the way ads are displayed to customer emotions.

[0105] Marketing systems can further estimate customer emotions and adjust customer support responses based on those estimates. For example, if a customer is angry, they can respond quickly and courteously. If a customer is feeling anxious, they can provide reassurance. Furthermore, if a customer is happy, they can express gratitude. By aligning customer support responses with customer emotions, customer satisfaction can be improved.

[0106] Marketing systems can further estimate customer emotions and tailor email marketing content based on those estimates. For example, if a customer is relaxed, they can send an email with detailed information. If a customer is in a hurry, they can send an email with concise information. Furthermore, if a customer is excited, they can send an email with a visually stimulating design. By tailoring email marketing content to customer emotions, this can improve email open rates and click-through rates.

[0107] The marketing system can further estimate customer emotions and adjust the website design based on those emotions. For example, if a customer is relaxed, it can display a design with calming colors. If a customer is excited, it can display a design with vibrant colors. Furthermore, if a customer is stressed, it can display a simple and intuitive design. This improves usability by aligning the website design with customer emotions.

[0108] The marketing system can further estimate the customer's emotions and adjust the chatbot's response based on those emotions. For example, if the customer is relaxed, it can respond in a friendly tone. If the customer is in a hurry, it can provide a concise and quick response. Furthermore, if the customer is excited, it can provide an engaging response. This allows for smoother communication with customers by tailoring the chatbot's response to their emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The data collection unit collects first-party data such as customer behavior history, purchase history, and access history. For example, it collects clickstream data when a customer browses a website, purchase history when they shop online, and access history when they view specific content. The data collection unit can also record the number of times a customer views a specific product page and the time spent on that page. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it analyzes customer behavior patterns to understand customer interests and preferences. It can also analyze customer purchase history to understand trends in products purchased in the past. Furthermore, it can analyze customer access history to identify the types of content that are frequently viewed. Step 3: The proposal department proposes optimal marketing strategies based on the analysis results obtained by the analysis department. For example, they may propose personalized offers based on customer interests and preferences, or suggest promotions for related products based on purchase history. Furthermore, they can also suggest recommendations for specific content based on access history. Step 4: The generation unit generates short videos based on the measures proposed by the proposal unit. For example, it generates promotional videos about products the customer is interested in, short videos about restaurants in areas of interest, or tutorial videos on topics of interest.

[0111] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0112] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects first-party data such as customer behavior history, purchase history, and access history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal marketing measures based on the analysis results. The generation unit is implemented by the control unit 46A of the smart device 14 and generates a short video based on the proposed measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0118] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0120] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0122] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed 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 necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects first-party data such as customer behavior history, purchase history, and access history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal marketing measures based on the analysis results. The generation unit is implemented by the control unit 46A of the smart glasses 214 and generates a short video based on the proposed measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0134] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0136] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0138] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, 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 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects first-party data such as customer behavior history, purchase history, and access history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal marketing measures based on the analysis results. The generation unit is implemented by the control unit 46A of the headset terminal 314 and generates short videos based on the proposed measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0150] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0152] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0154] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed 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 necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the collection unit, analysis unit, proposal unit, and generation unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects first-party data such as customer behavior history, purchase history, and access history. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes optimal marketing measures based on the analysis results. The generation unit is implemented by the control unit 46A of the robot 414 and generates a short video based on the proposed measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0164] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0165] Figure 9 shows the emotion map 400, in 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 the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0166] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0167] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0168] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0169] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it 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 emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0170] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0171] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0173] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0174] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0175] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0176] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0177] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0178] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0181] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0182] (Note 1) A data collection unit that collects first-party data such as customer behavior history, purchase history, and access history, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes the optimal marketing measures. The system includes a generation unit that generates short videos based on the measures proposed by the proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as customer behavior history, purchase history, and access history through conversational AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the collected data to understand customer interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the analysis results, we propose the most suitable marketing strategies for each individual customer. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate short videos based on the proposed measures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is Provide the generated short video to the customer. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current interests and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the marketing initiative. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the customer's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making proposals, prioritize them based on the timing of their implementation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the marketing measures. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates the user's emotions and adjusts the content of the short video generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating short videos, adjust the level of detail in the video based on the customer's interests. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating short videos, different generation algorithms are applied depending on the customer's attributes. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the user's emotions and adjusts the length of the short video generated based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating short videos, the system generates highly relevant videos based on the customer's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating short videos, the system analyzes the customer's social media activity and generates relevant videos. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects first-party data such as customer behavior history, purchase history, and access history, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes the optimal marketing measures, The system includes a generation unit that generates short videos based on the measures proposed by the proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is Through conversational AI, we collect data such as customer behavior history, purchase history, and access history. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the collected data to understand customer interests and preferences. The system according to feature 1.

4. The aforementioned proposal section is, Based on the analysis results, we propose the most suitable marketing strategies for each individual customer. The system according to feature 1.

5. The generating unit is Generate short videos based on the proposed measures. The system according to feature 1.

6. The generating unit is Provide the generated short video to the customer. The system according to feature 1.

7. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the user's past behavior history and select the optimal data collection method. The system according to feature 1.

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