System

Abstract

An object of the system according to the embodiment is to allow a store to easily adopt an effective sales promotion method.SOLUTION: A system includes a data analysis unit, an advertisement generation unit, and a proposal unit. The data-analyzing unit uses the generated AI to analyze sales information and commodity information of the store. The advertisement generation part generates an effective pop advertisement on the basis of the data analyzed by the data analysis part. The proposal part proposes the pop advertisement generated by the advertisement generation part to the store.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology makes it difficult for stores to adopt effective sales promotion methods, and a lack of management and human resources is an issue.

[0005] The system according to the embodiment aims to enable stores to easily adopt effective sales promotion techniques. [Means for solving the problem]

[0006] The system according to the embodiment includes a data analysis unit, an advertisement generation unit, and a proposal unit. The data analysis unit uses a generation AI to analyze sales data and product information of a store. The advertisement generation unit generates an effective pop advertisement based on the data analyzed by the data analysis unit. The proposal unit proposes the pop advertisement generated by the advertisement generation unit to the store. [Effects of the Invention]

[0007] The system according to the embodiment allows stores to easily adopt effective sales promotion techniques. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​consulting system according to an embodiment of the present invention is a system that automatically generates and proposes effective sales promotion methods for individual stores or shopping districts using AI technology to resolve the shortage of management resources and human resources in shopping districts. As a result, the AI ​​consulting system supports the sales promotion activities of the entire shopping district, thereby improving sales and customer satisfaction.

[0029] An AI consulting system according to an embodiment includes a generation AI, a data analysis unit, an advertisement generation unit, and a proposal unit. The generation AI analyzes a store's sales data and product information. For example, the generation AI analyzes best-selling products and sales trends based on the store's sales data. The generation AI can also analyze product characteristics and inventory status based on the product information. The generation AI can also perform a comprehensive analysis by combining the store's sales data and product information. The data analysis unit generates an effective pop-up advertisement based on the data analyzed by the generation AI. For example, the data analysis unit generates a pop-up advertisement that highlights best-selling products based on sales data. The data analysis unit can also generate a pop-up advertisement that highlights product characteristics based on the product information. The data analysis unit can also generate a comprehensive pop-up advertisement by combining the sales data and the product information. The advertisement generation unit proposes the pop-up advertisement generated by the data analysis unit to the store. For example, the advertisement generation unit displays the generated pop-up advertisement on a display in the store. The advertisement generation unit can also post the generated pop-up advertisement on the store's website. The advertisement generation unit can also post the generated POP advertisement to the store's social media account. The suggestion unit provides the POP advertisement suggested by the advertisement generation unit to the store. For example, the suggestion unit distributes the generated POP advertisement to store staff. The suggestion unit can also distribute the generated POP advertisement to store customers. The suggestion unit can also distribute the generated POP advertisement at store events. As a result, the AI ​​consulting system according to the embodiment can analyze the store's sales data and product information and automatically generate and suggest effective POP advertisements to support the store's sales promotion activities. For example, the store can increase sales by utilizing the generated POP advertisement. The store can also improve customer satisfaction by utilizing the generated POP advertisement. The store can also improve the efficiency of its sales promotion activities by utilizing the generated POP advertisement.

[0030] The data analysis unit can analyze inventory data in real time and generate optimal pop-up advertisements according to the inventory status. For example, the data analysis unit uses a generation AI to collect and analyze store inventory data in real time. For example, it generates pop-up advertisements that prioritize advertising products with high inventory levels. The data analysis unit also proposes pop-up advertisements that effectively promote unsold or seasonal products based on the inventory data. For example, it advertises products with high inventory levels as sales at the end of a season. The data analysis unit also generates pop-up advertisements that sell specific products as a set, according to the inventory status. For example, it proposes a set sale that combines a product with high inventory levels with related products. In this way, by generating pop-up advertisements according to the inventory status, inventory management and sales promotion activities can be made more efficient.

[0031] The data analysis unit can analyze the effectiveness of a store's past promotional campaigns and generate pop-up advertisements that incorporate the most successful elements. For example, the data analysis unit uses a generation AI to collect and analyze data from a store's past promotional campaigns. For example, it extracts elements from past successful campaigns and generates new pop-up advertisements based on those elements. The data analysis unit also evaluates the effectiveness of past campaigns and identifies the factors that led to their success. For example, if a particular catchphrase or design was effective, it can incorporate it into new pop-up advertisements. The data analysis unit also suggests effective pop-up advertisements for different seasons and events based on data from past promotional campaigns. For example, it generates new advertisements based on the factors that led to the success of past Christmas campaigns. This maximizes the effectiveness of promotional advertisements by generating pop-up advertisements that incorporate the factors that led to past successes.

[0032] The data analysis unit can analyze photos of a store's exterior and interior and propose pop-up advertising designs that match them. For example, the data analysis unit uses a generation AI to collect and analyze photos of a store's exterior and interior. For example, it generates pop-up advertising that matches the color and design of the store. The data analysis unit also proposes pop-up advertising that is optimal for specific locations based on store interior data. For example, it optimizes the design of advertisements to be placed around the cash register or near the entrance. The data analysis unit also generates pop-up advertising with designs that catch the eye of passersby based on store exterior data. For example, it proposes colors and fonts that match the store's exterior. This allows it to propose pop-up advertising that matches the store's exterior and interior, making it possible to carry out promotional activities that suit the store's atmosphere.

[0033] The data analysis unit can link with the store's social media accounts and generate pop-up advertisements based on posts that have received a positive response on social media. For example, the data analysis unit uses a generation AI to collect and analyze data from the store's social media accounts. For example, it generates pop-up advertisements based on posts that have received a positive response on social media. The data analysis unit also suggests catchy slogans and designs that will attract customer attention based on the social media post data. For example, it reflects posts that have received a lot of likes on social media in advertisements. The data analysis unit also analyzes social media response data in real time to generate pop-up advertisements that match the latest trends. For example, it suggests advertisements that incorporate topics that are trending on social media. This allows promotional activities to attract customer attention by generating pop-up advertisements based on posts that have received a positive response on social media.

[0034] The data analysis unit can analyze access data from a store's website and generate advertisements tailored to the time periods with the most visitors. For example, the data analysis unit uses a generation AI to collect and analyze access data from a store's website. For example, it generates advertisements tailored to the time periods with the most visitors. The data analysis unit also identifies visitor behavior patterns based on website access data and suggests optimal ad copy and images. For example, it generates advertisements tailored to pages with the most visitors during specific time periods. The data analysis unit also analyzes access data in real time and generates advertisements that are optimal for the time periods with the most visitors. For example, it automatically displays advertisements tailored to peak hours. This enables effective online marketing by displaying advertisements at the optimal time periods based on website access data.

[0035] The data analysis unit can analyze competitors' advertising data and generate unique advertisements that can compete with competitors. For example, the data analysis unit uses the generation AI to collect and analyze advertising data from the store's competitors. For example, it identifies the success factors of competitors' advertisements and generates unique advertisements that can compete with them. The data analysis unit also suggests advertising copy and images that emphasize the store's strengths based on competitors' advertising data. For example, it reflects points of differentiation from competitors in the advertisement. The data analysis unit also analyzes competitors' advertising data in real time and generates unique advertisements that are in line with the latest trends. For example, it suggests advertisements that counter competitors' new campaigns. This allows the store to increase its competitiveness by analyzing competitors' advertising data and generating unique advertisements.

[0036] The data analysis unit can analyze a store's email marketing data and generate digital advertisements linked to email campaigns. For example, the data analysis unit uses generation AI to collect and analyze a store's email marketing data. For example, it generates digital advertisements that match the content of the email campaign. The data analysis unit also analyzes customer responses based on the email marketing data and suggests optimal advertising copy and images. For example, it reflects content with high email open rates and click rates in the advertisement. The data analysis unit also generates digital advertisements linked to email campaigns in real time and suggests advertisements that will attract customer interest. For example, it adjusts advertisements based on response data after emails are sent. This makes it possible to realize an integrated marketing strategy by generating digital advertisements linked to email campaigns.

[0037] The data analysis unit can analyze the store's loyalty program data and generate special advertisements for loyalty members. For example, the data analysis unit uses a generation AI to collect and analyze the store's loyalty program data. For example, it generates special advertisements for loyalty members. The data analysis unit also identifies members' purchasing history and behavioral patterns based on the loyalty program data and suggests optimal advertising copy and images. For example, it generates advertisements that emphasize member-only benefits and discounts. The data analysis unit also generates special advertisements for loyalty members in real time and suggests advertisements that will attract members' interest. For example, it generates personalized advertisements based on members' purchasing history. In this way, customer loyalty can be improved by generating special advertisements for loyalty members.

[0038] The data analysis unit can analyze the effectiveness of a store's past seasonal campaigns and propose campaigns that incorporate the most successful elements. For example, the data analysis unit uses generative AI to collect and analyze data from a store's past seasonal campaigns. For example, it extracts elements from past successful campaigns and proposes new campaigns based on those elements. The data analysis unit also evaluates the effectiveness of past campaigns and identifies the factors that contributed to their success. For example, if a particular catchphrase or promotional method was effective, it can incorporate it into a new campaign. The data analysis unit also proposes effective campaign content according to the season or event based on data from past seasonal campaigns. For example, it can propose a new campaign based on the factors that made a past Christmas campaign successful. This makes it possible to maximize sales promotion effectiveness by proposing campaigns that incorporate past success factors.

[0039] The data analysis unit can analyze local event data and propose campaigns linked to local events. For example, the data analysis unit uses generation AI to collect and analyze local event data. For example, it proposes campaigns that coincide with local festivals and festivals. The data analysis unit also proposes campaign content that will attract customer interest based on local event data. For example, it proposes campaigns that feature products and services related to local events. The data analysis unit also analyzes local event data in real time and proposes campaigns that coincide with the latest events. For example, when a new local event is announced, it proposes a campaign that coincides with it. In this way, by proposing campaigns linked to local events, it is possible to realize community-based sales promotion activities.

[0040] The data analysis unit can analyze online store data and propose campaigns that link online and offline. For example, the data analysis unit uses a generation AI to collect and analyze data from a store's online store. For example, the data analysis unit can launch a similar campaign in-store based on best-selling items in the online store. The data analysis unit also proposes campaign content that links online and offline based on the online store data. For example, it can propose a campaign where items purchased online can be picked up in-store. The data analysis unit also analyzes online store data in real time and proposes campaigns that are effective both online and offline. For example, it can propose a campaign where online promotional codes can be used in-store. In this way, by proposing campaigns that link online and offline, it is possible to achieve integrated sales promotion activities.

[0041] The data analysis unit can analyze data from partner companies and propose joint campaigns. For example, the data analysis unit uses a generation AI to collect and analyze data from a store's partner companies. For example, it proposes a campaign to be carried out in collaboration with the partner company. The data analysis unit also proposes the content of the joint campaign based on the partner company's data. For example, it proposes a campaign linked to the partner company's products or services. The data analysis unit also analyzes partner company data in real time and proposes joint campaigns that are in line with the latest trends. For example, it proposes a campaign to coincide with the release of a new product by the partner company. In this way, by proposing joint campaigns with partner companies, it is possible to create a synergistic effect.

[0042] The data analysis unit analyzes a customer's purchase history, predicts when the next purchase will be made, and can propose promotions at the appropriate time. For example, the data analysis unit uses a generation AI to collect and analyze customer purchase history data. For example, it identifies a customer's purchasing cycle and predicts when the next purchase will be made. The data analysis unit also identifies a customer's purchasing pattern based on the purchase history data and proposes promotions at the optimal time. For example, it runs promotions for products that are purchased regularly. The data analysis unit also analyzes purchase history data in real time, predicts when the next purchase will be made, and proposes promotions. For example, it runs promotions to coincide with the time when a customer purchases a specific product. This makes it possible to propose promotions that coincide with the customer's purchasing timing, thereby increasing the effectiveness of sales promotions.

[0043] The data analysis unit can analyze a customer's lifestyle data and propose services that match their lifestyle. For example, the data analysis unit uses generative AI to collect and analyze customer lifestyle data. For example, it proposes services that match the customer's hobbies and interests. The data analysis unit also identifies a customer's lifestyle patterns based on the lifestyle data and proposes optimal services. For example, it proposes health-related products and services to health-conscious customers. The data analysis unit also analyzes lifestyle data in real time and proposes services that match the customer's lifestyle. For example, it adjusts the content of services in response to changes in the customer's lifestyle. This makes it possible to propose services that match the customer's lifestyle, thereby improving customer satisfaction.

[0044] The data analysis unit can analyze customers' social media data and suggest personalized promotions on social media. For example, the data analysis unit uses generative AI to collect and analyze customers' social media data. For example, it suggests personalized promotions based on the content of customers' posts and their reactions. The data analysis unit also suggests promotional content that will attract customers' interest based on the social media data. For example, it may run promotions related to topics that interest customers. The data analysis unit also analyzes social media data in real time and suggests personalized promotions that match the latest trends. For example, it may run promotions related to topics that customers have recently taken an interest in. This makes it possible to attract customers' interest by suggesting personalized promotions on social media.

[0045] The data analysis unit can analyze customer loyalty program data and propose special services for loyalty members. For example, the data analysis unit uses generative AI to collect and analyze customer loyalty program data. For example, it proposes special services for loyalty members. The data analysis unit also identifies members' purchasing history and behavioral patterns based on the loyalty program data and proposes optimal services. For example, it proposes benefits and events exclusive to members. The data analysis unit also proposes special services for loyalty members in real time and provides services that attract members' interest. For example, it proposes personalized services based on the member's purchasing history. In this way, customer loyalty can be improved by proposing special services for loyalty members.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The data analysis unit can analyze photos of a store's exterior and interior and propose pop-up advertising designs that match them. For example, the generation AI collects and analyzes photos of a store's exterior and interior. For example, it generates pop-up advertising that matches the store's color scheme and design. The data analysis unit also proposes pop-up advertising that is optimal for specific locations based on store interior data. For example, it optimizes the design of advertisements to be placed around the cash register or near the entrance. The data analysis unit also generates pop-up advertising with designs that catch the eye of passersby based on store exterior data. For example, it proposes color schemes and fonts that match the store's exterior. This allows it to propose pop-up advertising that matches the store's exterior and interior, making it possible to carry out promotional activities that suit the store's atmosphere.

[0048] The data analysis unit can link with the store's social media accounts and generate pop-up advertisements based on posts that have received a positive response on social media. For example, the generation AI collects and analyzes data from the store's social media accounts. For example, it generates pop-up advertisements based on posts that have received a positive response on social media. The data analysis unit also suggests catchy slogans and designs that will attract customer attention based on the social media post data. For example, it reflects posts that have received a lot of likes on social media in advertisements. The data analysis unit also analyzes social media response data in real time to generate pop-up advertisements that match the latest trends. For example, it suggests advertisements that incorporate topics that are trending on social media. This allows promotional activities to attract customer attention by generating pop-up advertisements based on posts that have received a positive response on social media.

[0049] The data analysis unit can analyze access data from a store's website and generate advertisements tailored to the time periods with the most visitors. For example, the generation AI collects and analyzes access data from a store's website. For example, it generates advertisements tailored to the time periods with the most visitors. The data analysis unit also identifies visitor behavior patterns based on website access data and suggests optimal ad copy and images. For example, it generates advertisements tailored to pages with the most visitors during specific time periods. The data analysis unit also analyzes access data in real time and generates advertisements that are optimal for the time periods with the most visitors. For example, it automatically displays advertisements tailored to peak hours. This enables effective online marketing by displaying advertisements at the optimal time periods based on website access data.

[0050] The data analysis unit can analyze competitors' advertising data and generate unique advertisements that can compete with rivals. For example, the generation AI collects and analyzes the advertising data of a store's competitors. For example, it identifies the factors that make the competitors' advertisements successful and generates unique advertisements that can compete with them. The data analysis unit also suggests advertising copy and images that emphasize the store's strengths based on the competitors' advertising data. For example, it reflects the store's points of differentiation from competitors in the advertisement. The data analysis unit also analyzes competitors' advertising data in real time and generates unique advertisements that are in line with the latest trends. For example, it suggests advertisements that counter competitors' new campaigns. This allows the store to increase its competitiveness by analyzing competitors' advertising data and generating unique advertisements.

[0051] The data analysis unit can analyze a store's email marketing data and generate digital advertisements linked to email campaigns. For example, the generation AI collects and analyzes a store's email marketing data. For example, it generates digital advertisements that match the content of the email campaign. The data analysis unit also analyzes customer responses based on the email marketing data and suggests optimal advertising copy and images. For example, it reflects content with high email open rates and click rates in the advertisement. The data analysis unit also generates digital advertisements linked to email campaigns in real time and suggests advertisements that will attract customer interest. For example, it adjusts advertisements based on response data after emails are sent. In this way, an integrated marketing strategy can be realized by generating digital advertisements linked to email campaigns.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The data analysis unit uses generative AI to analyze the store's sales data and product information. For example, it analyzes best-selling products and sales trends based on sales data, and analyzes product characteristics and inventory status based on product information. It can also combine sales data and product information to perform comprehensive analysis. Step 2: The advertisement generation unit generates effective pop-up advertisements based on the data analyzed by the data analysis unit. For example, it generates pop-up advertisements that highlight best-selling products based on sales data, or pop-up advertisements that highlight product characteristics based on product information. It is also possible to generate comprehensive pop-up advertisements by combining sales data and product information. Step 3: The proposal unit proposes the POP advertisement generated by the advertisement generation unit to the store. For example, the generated POP advertisement can be displayed on the store's display, posted on the store's website or social media account, or distributed to store staff and customers.

[0054] (Example 2) The AI ​​consulting system according to an embodiment of the present invention is a system that automatically generates and proposes effective sales promotion methods for individual stores or shopping districts using AI technology to resolve the shortage of management resources and human resources in shopping districts. As a result, the AI ​​consulting system supports the sales promotion activities of the entire shopping district, thereby improving sales and customer satisfaction.

[0055] An AI consulting system according to an embodiment includes a generation AI, a data analysis unit, an advertisement generation unit, and a proposal unit. The generation AI analyzes a store's sales data and product information. For example, the generation AI analyzes best-selling products and sales trends based on the store's sales data. The generation AI can also analyze product characteristics and inventory status based on the product information. The generation AI can also perform a comprehensive analysis by combining the store's sales data and product information. The data analysis unit generates an effective pop-up advertisement based on the data analyzed by the generation AI. For example, the data analysis unit generates a pop-up advertisement that highlights best-selling products based on sales data. The data analysis unit can also generate a pop-up advertisement that highlights product characteristics based on the product information. The data analysis unit can also generate a comprehensive pop-up advertisement by combining the sales data and the product information. The advertisement generation unit proposes the pop-up advertisement generated by the data analysis unit to the store. For example, the advertisement generation unit displays the generated pop-up advertisement on a display in the store. The advertisement generation unit can also post the generated pop-up advertisement on the store's website. The advertisement generation unit can also post the generated POP advertisement to the store's social media account. The suggestion unit provides the POP advertisement suggested by the advertisement generation unit to the store. For example, the suggestion unit distributes the generated POP advertisement to store staff. The suggestion unit can also distribute the generated POP advertisement to store customers. The suggestion unit can also distribute the generated POP advertisement at store events. As a result, the AI ​​consulting system according to the embodiment can analyze the store's sales data and product information and automatically generate and suggest effective POP advertisements to support the store's sales promotion activities. For example, the store can increase sales by utilizing the generated POP advertisement. The store can also improve customer satisfaction by utilizing the generated POP advertisement. The store can also improve the efficiency of its sales promotion activities by utilizing the generated POP advertisement.

[0056] The data analysis unit can analyze emotional data from customer segments and propose catchy slogans and designs that most resonate with customers. For example, the data analysis unit uses generation AI to collect and analyze emotional data from a store's customer segments. For example, it extracts emotional data from customers' social media posts and reviews and generates catchy slogans that evoke positive emotions. The data analysis unit also performs emotional analysis based on customers' purchase history and behavioral data to propose designs and catchy slogans that customers can most easily relate to. For example, it identifies customer preferences from past purchase data and generates pop advertisements that match them. The data analysis unit also analyzes customer emotional data in real time to generate catchy slogans and designs that appeal to emotions according to seasons and events. For example, it proposes warm designs for the Christmas season. This allows for the proposal of catchy slogans and designs based on customer emotions, thereby increasing the effectiveness of sales promotions.

[0057] The data analysis unit can analyze inventory data in real time and generate optimal pop-up advertisements according to the inventory status. For example, the data analysis unit uses a generation AI to collect and analyze store inventory data in real time. For example, it generates pop-up advertisements that prioritize advertising products with high inventory levels. The data analysis unit also proposes pop-up advertisements that effectively promote unsold or seasonal products based on the inventory data. For example, it advertises products with high inventory levels as sales at the end of a season. The data analysis unit also generates pop-up advertisements that sell specific products as a set, according to the inventory status. For example, it proposes a set sale that combines a product with high inventory levels with related products. In this way, by generating pop-up advertisements according to the inventory status, inventory management and sales promotion activities can be made more efficient.

[0058] The data analysis unit can analyze the effectiveness of a store's past promotional campaigns and generate pop-up advertisements that incorporate the most successful elements. For example, the data analysis unit uses a generation AI to collect and analyze data from a store's past promotional campaigns. For example, it extracts elements from past successful campaigns and generates new pop-up advertisements based on those elements. The data analysis unit also evaluates the effectiveness of past campaigns and identifies the factors that led to their success. For example, if a particular catchphrase or design was effective, it can incorporate it into new pop-up advertisements. The data analysis unit also suggests effective pop-up advertisements for different seasons and events based on data from past promotional campaigns. For example, it generates new advertisements based on the factors that led to the success of past Christmas campaigns. This maximizes the effectiveness of promotional advertisements by generating pop-up advertisements that incorporate the factors that led to past successes.

[0059] The data analysis unit can analyze photos of a store's exterior and interior and propose pop-up advertising designs that match them. For example, the data analysis unit uses a generation AI to collect and analyze photos of a store's exterior and interior. For example, it generates pop-up advertising that matches the color and design of the store. The data analysis unit also proposes pop-up advertising that is optimal for specific locations based on store interior data. For example, it optimizes the design of advertisements to be placed around the cash register or near the entrance. The data analysis unit also generates pop-up advertising with designs that catch the eye of passersby based on store exterior data. For example, it proposes colors and fonts that match the store's exterior. This allows it to propose pop-up advertising that matches the store's exterior and interior, making it possible to carry out promotional activities that suit the store's atmosphere.

[0060] The data analysis unit can link with the store's social media accounts and generate pop-up advertisements based on posts that have received a positive response on social media. For example, the data analysis unit uses a generation AI to collect and analyze data from the store's social media accounts. For example, it generates pop-up advertisements based on posts that have received a positive response on social media. The data analysis unit also suggests catchy slogans and designs that will attract customer attention based on the social media post data. For example, it reflects posts that have received a lot of likes on social media in advertisements. The data analysis unit also analyzes social media response data in real time to generate pop-up advertisements that match the latest trends. For example, it suggests advertisements that incorporate topics that are trending on social media. This allows promotional activities to attract customer attention by generating pop-up advertisements based on posts that have received a positive response on social media.

[0061] The data analysis unit can analyze the emotions of store staff and generate pop-up advertisements that the staff can recommend with the most confidence. The data analysis unit, for example, uses an emotion estimation function to collect and analyze emotional data of store staff. For example, a pop-up advertisement is generated based on products that the staff can recommend with confidence. The data analysis unit also optimizes the catchphrase and design of the pop-up advertisement based on the emotional data of the staff. For example, the data analysis unit proposes advertisements that highlight products that the staff have positive feelings about. The data analysis unit also analyzes the emotional data of the staff in real time and generates pop-up advertisements based on products that the staff can recommend with the most confidence. For example, the content of the advertisement can be adjusted according to changes in the emotional state of the staff. In this way, by generating pop-up advertisements based on the emotions of the staff, promotional activities that the staff can recommend with confidence can be carried out.

[0062] The data analysis unit can analyze consumer emotional data and generate emotionally appealing advertising copy and images. For example, the data analysis unit uses a generation AI to collect and analyze consumer emotional data. For example, it extracts emotional data from social media and review sites and generates advertising copy that evokes positive emotions. The data analysis unit also performs emotional analysis based on consumers' purchase history and behavioral data, and suggests emotionally appealing advertising copy and images. For example, it identifies consumer preferences from past purchase data and generates advertisements that match those preferences. The data analysis unit also analyzes consumer emotional data in real time and generates emotionally appealing advertising copy and images according to seasons and events. For example, it suggests warm-hearted advertisements during the Christmas season. This allows for the generation of advertising copy and images based on consumer emotions, thereby increasing the effectiveness of sales promotions.

[0063] The data analysis unit can analyze access data from a store's website and generate advertisements tailored to the time periods with the most visitors. For example, the data analysis unit uses a generation AI to collect and analyze access data from a store's website. For example, it generates advertisements tailored to the time periods with the most visitors. The data analysis unit also identifies visitor behavior patterns based on website access data and suggests optimal ad copy and images. For example, it generates advertisements tailored to pages with the most visitors during specific time periods. The data analysis unit also analyzes access data in real time and generates advertisements that are optimal for the time periods with the most visitors. For example, it automatically displays advertisements tailored to peak hours. This enables effective online marketing by displaying advertisements at the optimal time periods based on website access data.

[0064] The data analysis unit can analyze competitors' advertising data and generate unique advertisements that can compete with competitors. For example, the data analysis unit uses the generation AI to collect and analyze advertising data from the store's competitors. For example, it identifies the success factors of competitors' advertisements and generates unique advertisements that can compete with them. The data analysis unit also suggests advertising copy and images that emphasize the store's strengths based on competitors' advertising data. For example, it reflects points of differentiation from competitors in the advertisement. The data analysis unit also analyzes competitors' advertising data in real time and generates unique advertisements that are in line with the latest trends. For example, it suggests advertisements that counter competitors' new campaigns. This allows the store to increase its competitiveness by analyzing competitors' advertising data and generating unique advertisements.

[0065] The data analysis unit can analyze a store's email marketing data and generate digital advertisements linked to email campaigns. For example, the data analysis unit uses generation AI to collect and analyze a store's email marketing data. For example, it generates digital advertisements that match the content of the email campaign. The data analysis unit also analyzes customer responses based on the email marketing data and suggests optimal advertising copy and images. For example, it reflects content with high email open rates and click rates in the advertisement. The data analysis unit also generates digital advertisements linked to email campaigns in real time and suggests advertisements that will attract customer interest. For example, it adjusts advertisements based on response data after emails are sent. This makes it possible to realize an integrated marketing strategy by generating digital advertisements linked to email campaigns.

[0066] The data analysis unit can analyze the store's loyalty program data and generate special advertisements for loyalty members. For example, the data analysis unit uses a generation AI to collect and analyze the store's loyalty program data. For example, it generates special advertisements for loyalty members. The data analysis unit also identifies members' purchasing history and behavioral patterns based on the loyalty program data and suggests optimal advertising copy and images. For example, it generates advertisements that emphasize member-only benefits and discounts. The data analysis unit also generates special advertisements for loyalty members in real time and suggests advertisements that will attract members' interest. For example, it generates personalized advertisements based on members' purchasing history. In this way, customer loyalty can be improved by generating special advertisements for loyalty members.

[0067] The data analysis unit can analyze the sentiment of online reviews of a store and generate advertisements that emphasize positive reviews. The data analysis unit, for example, uses an emotion estimation function to collect and analyze emotion data of online reviews of a store. For example, it generates advertisements that emphasize positive reviews. The data analysis unit also suggests advertising copy and images that elicit positive emotions from customers based on the emotion data of online reviews. For example, it reflects particularly highly rated points in the reviews in the advertisement. The data analysis unit also analyzes the emotion data of online reviews in real time and generates advertisements that incorporate the latest positive reviews. For example, it updates the advertisement content every time a new review is posted. In this way, it is possible to gain customer trust by generating advertisements that emphasize positive reviews.

[0068] The data analysis unit can analyze seasonal customer emotional data and propose campaign content that appeals to emotions. For example, the data analysis unit uses generative AI to collect and analyze seasonal customer emotional data. For example, it proposes emotional campaigns such as a new life support campaign in spring and a summer sale in summer. The data analysis unit also performs emotional analysis based on seasonal customer purchase history and behavioral data to propose optimal campaign content. For example, it identifies customer preferences from past data and proposes campaigns that match those preferences. The data analysis unit also analyzes seasonal customer emotional data in real time and proposes emotional campaign content that matches the latest trends. For example, it proposes campaigns that match seasonal events and trends. This makes it possible to increase sales promotion effectiveness by proposing campaign content based on seasonal customer emotions.

[0069] The data analysis unit can analyze the effectiveness of a store's past seasonal campaigns and propose campaigns that incorporate the most successful elements. For example, the data analysis unit uses generative AI to collect and analyze data from a store's past seasonal campaigns. For example, it extracts elements from past successful campaigns and proposes new campaigns based on those elements. The data analysis unit also evaluates the effectiveness of past campaigns and identifies the factors that contributed to their success. For example, if a particular catchphrase or promotional method was effective, it can incorporate it into a new campaign. The data analysis unit also proposes effective campaign content according to the season or event based on data from past seasonal campaigns. For example, it can propose a new campaign based on the factors that made a past Christmas campaign successful. This makes it possible to maximize sales promotion effectiveness by proposing campaigns that incorporate past success factors.

[0070] The data analysis unit can analyze local event data and propose campaigns linked to local events. For example, the data analysis unit uses generation AI to collect and analyze local event data. For example, it proposes campaigns that coincide with local festivals and festivals. The data analysis unit also proposes campaign content that will attract customer interest based on local event data. For example, it proposes campaigns that feature products and services related to local events. The data analysis unit also analyzes local event data in real time and proposes campaigns that coincide with the latest events. For example, when a new local event is announced, it proposes a campaign that coincides with it. In this way, by proposing campaigns linked to local events, it is possible to realize community-based sales promotion activities.

[0071] The data analysis unit can analyze online store data and propose campaigns that link online and offline. For example, the data analysis unit uses a generation AI to collect and analyze data from a store's online store. For example, the data analysis unit can launch a similar campaign in-store based on best-selling items in the online store. The data analysis unit also proposes campaign content that links online and offline based on the online store data. For example, it can propose a campaign where items purchased online can be picked up in-store. The data analysis unit also analyzes online store data in real time and proposes campaigns that are effective both online and offline. For example, it can propose a campaign where online promotional codes can be used in-store. In this way, by proposing campaigns that link online and offline, it is possible to achieve integrated sales promotion activities.

[0072] The data analysis unit can analyze data from partner companies and propose joint campaigns. For example, the data analysis unit uses a generation AI to collect and analyze data from a store's partner companies. For example, it proposes a campaign to be carried out in collaboration with the partner company. The data analysis unit also proposes the content of the joint campaign based on the partner company's data. For example, it proposes a campaign linked to the partner company's products or services. The data analysis unit also analyzes partner company data in real time and proposes joint campaigns that are in line with the latest trends. For example, it proposes a campaign to coincide with the release of a new product by the partner company. In this way, by proposing joint campaigns with partner companies, it is possible to create a synergistic effect.

[0073] The data analysis unit can analyze the emotions of store staff and propose campaigns that will motivate the staff most. The data analysis unit, for example, uses an emotion estimation function to collect and analyze emotional data of store staff. For example, it proposes campaigns that will motivate the staff most. The data analysis unit also optimizes the campaign content based on the emotional data of the staff. For example, it proposes campaigns that have positive emotions in the staff. The data analysis unit also analyzes the emotional data of the staff in real time and proposes campaigns that will motivate the staff most. For example, it adjusts the campaign content according to changes in the emotional state of the staff. In this way, it is possible to increase the motivation of the staff by proposing campaigns based on the emotions of the staff.

[0074] The data analysis unit can analyze customer emotional data and propose personalized services based on emotions. For example, the data analysis unit uses generative AI to collect and analyze customer emotional data. For example, it extracts emotional data from customers' social media posts and reviews and proposes services that elicit positive emotions. The data analysis unit also performs emotional analysis based on customers' purchase history and behavioral data to propose services that customers can most easily relate to. For example, it identifies customer preferences from past purchase data and proposes services that match those preferences. The data analysis unit also analyzes customer emotional data in real time and proposes services that appeal to emotions according to seasons and events. For example, it proposes warm-hearted services during the Christmas season. In this way, customer satisfaction can be improved by proposing services based on customers' emotions.

[0075] The data analysis unit analyzes a customer's purchase history, predicts when the next purchase will be made, and can propose promotions at the appropriate time. For example, the data analysis unit uses a generation AI to collect and analyze customer purchase history data. For example, it identifies a customer's purchasing cycle and predicts when the next purchase will be made. The data analysis unit also identifies a customer's purchasing pattern based on the purchase history data and proposes promotions at the optimal time. For example, it runs promotions for products that are purchased regularly. The data analysis unit also analyzes purchase history data in real time, predicts when the next purchase will be made, and proposes promotions. For example, it runs promotions to coincide with the time when a customer purchases a specific product. This makes it possible to propose promotions that coincide with the customer's purchasing timing, thereby increasing the effectiveness of sales promotions.

[0076] The data analysis unit can analyze a customer's lifestyle data and propose services that match their lifestyle. For example, the data analysis unit uses generative AI to collect and analyze customer lifestyle data. For example, it proposes services that match the customer's hobbies and interests. The data analysis unit also identifies a customer's lifestyle patterns based on the lifestyle data and proposes optimal services. For example, it proposes health-related products and services to health-conscious customers. The data analysis unit also analyzes lifestyle data in real time and proposes services that match the customer's lifestyle. For example, it adjusts the content of services in response to changes in the customer's lifestyle. This makes it possible to propose services that match the customer's lifestyle, thereby improving customer satisfaction.

[0077] The data analysis unit can analyze customers' social media data and suggest personalized promotions on social media. For example, the data analysis unit uses generative AI to collect and analyze customers' social media data. For example, it suggests personalized promotions based on the content of customers' posts and their reactions. The data analysis unit also suggests promotional content that will attract customers' interest based on the social media data. For example, it may run promotions related to topics that interest customers. The data analysis unit also analyzes social media data in real time and suggests personalized promotions that match the latest trends. For example, it may run promotions related to topics that customers have recently taken an interest in. This makes it possible to attract customers' interest by suggesting personalized promotions on social media.

[0078] The data analysis unit can analyze customer loyalty program data and propose special services for loyalty members. For example, the data analysis unit uses generative AI to collect and analyze customer loyalty program data. For example, it proposes special services for loyalty members. The data analysis unit also identifies members' purchasing history and behavioral patterns based on the loyalty program data and proposes optimal services. For example, it proposes benefits and events exclusive to members. The data analysis unit also proposes special services for loyalty members in real time and provides services that attract members' interest. For example, it proposes personalized services based on the member's purchasing history. In this way, customer loyalty can be improved by proposing special services for loyalty members.

[0079] The data analysis unit can analyze customer emotions in real time and provide services according to those emotions. The data analysis unit, for example, uses an emotion estimation function to collect and analyze customer emotion data in real time. For example, it analyzes the customer's facial expressions and voice and provides services according to those emotions. The data analysis unit also proposes personalized services according to the customer's emotions based on the customer's emotion data. For example, it proposes products and services that the customer has positive emotions about. The data analysis unit also analyzes the customer's emotion data in real time and adjusts the service content according to changes in emotions. For example, it updates the service content every time the customer's emotions change. In this way, customer satisfaction can be improved by providing services according to the customer's emotions.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The data analysis unit can analyze the emotional data of a store's customer base and propose catchy slogans and designs that will most resonate with them. For example, the generative AI collects and analyzes the emotional data of a store's customer base. For example, it extracts emotional data from customers' social media posts and reviews and generates catchy slogans that evoke positive emotions. The data analysis unit also performs emotional analysis based on customers' purchase history and behavioral data to propose designs and catchy slogans that customers will most easily relate to. For example, it identifies customer preferences from past purchase data and generates pop advertisements that match them. The data analysis unit also analyzes customer emotional data in real time to generate catchy slogans and designs that appeal to emotions according to seasons and events. For example, it proposes warm designs for the Christmas season. This makes it possible to increase sales promotion effectiveness by proposing catchy slogans and designs based on customer emotions.

[0082] The data analysis unit can analyze the emotions of store staff and generate pop-up advertisements that the staff can recommend with the most confidence. For example, the emotion estimation function is used to collect and analyze emotional data of store staff. For example, pop-up advertisements are generated based on products that the staff can recommend with confidence. The data analysis unit also optimizes the catchphrase and design of the pop-up advertisement based on the emotional data of the staff. For example, it proposes advertisements that highlight products that the staff have positive feelings about. The data analysis unit also analyzes the emotional data of the staff in real time and generates pop-up advertisements based on products that the staff can recommend with the most confidence. For example, it adjusts the content of the advertisement according to changes in the emotional state of the staff. In this way, by generating pop-up advertisements based on the emotions of the staff, it is possible to carry out promotional activities that the staff can recommend with confidence.

[0083] The data analysis unit can analyze consumer emotional data and generate emotionally appealing ad copy and images. For example, the generative AI collects and analyzes consumer emotional data. For example, it extracts emotional data from social media and review sites and generates ad copy that evokes positive emotions. The data analysis unit also performs emotional analysis based on consumers' purchase history and behavioral data, and suggests emotionally appealing ad copy and images. For example, it identifies consumer preferences from past purchase data and generates ads that match those preferences. The data analysis unit also analyzes consumer emotional data in real time and generates emotionally appealing ad copy and images according to seasons and events. For example, it suggests warm-hearted ads during the Christmas season. This allows for the generation of ad copy and images based on consumer emotions, thereby increasing the effectiveness of sales promotions.

[0084] The data analysis unit can analyze customer emotional data and propose personalized services based on emotions. For example, generative AI collects and analyzes customer emotional data. For example, it extracts emotional data from customers' social media posts and reviews and proposes services that elicit positive emotions. The data analysis unit also performs emotional analysis based on customers' purchase history and behavioral data to propose services that customers will most likely empathize with. For example, it identifies customer preferences from past purchase data and proposes services that match those preferences. The data analysis unit also analyzes customer emotional data in real time and proposes services that appeal to emotions according to seasons and events. For example, it proposes warm-hearted services during the Christmas season. This makes it possible to propose services based on customers' emotions, thereby improving customer satisfaction.

[0085] The data analysis unit can analyze customer emotions in real time and provide services according to those emotions. For example, an emotion estimation function is used to collect and analyze customer emotion data in real time. For example, the emotion estimation function can analyze the customer's facial expressions and voice to provide services according to those emotions. The data analysis unit can also propose personalized services according to the customer's emotions based on the customer's emotion data. For example, it can propose products and services that the customer has positive emotions about. The data analysis unit can also analyze the customer's emotion data in real time and adjust the service content according to changes in emotions. For example, it can update the service content every time the customer's emotions change. This makes it possible to provide services according to the customer's emotions, thereby improving customer satisfaction.

[0086] The data analysis unit can analyze photos of a store's exterior and interior and propose pop-up advertising designs that match them. For example, the generation AI collects and analyzes photos of a store's exterior and interior. For example, it generates pop-up advertising that matches the store's color scheme and design. The data analysis unit also proposes pop-up advertising that is optimal for specific locations based on store interior data. For example, it optimizes the design of advertisements to be placed around the cash register or near the entrance. The data analysis unit also generates pop-up advertising with designs that catch the eye of passersby based on store exterior data. For example, it proposes color schemes and fonts that match the store's exterior. This allows it to propose pop-up advertising that matches the store's exterior and interior, making it possible to carry out promotional activities that suit the store's atmosphere.

[0087] The data analysis unit can link with the store's social media accounts and generate pop-up advertisements based on posts that have received a positive response on social media. For example, the generation AI collects and analyzes data from the store's social media accounts. For example, it generates pop-up advertisements based on posts that have received a positive response on social media. The data analysis unit also suggests catchy slogans and designs that will attract customer attention based on the social media post data. For example, it reflects posts that have received a lot of likes on social media in advertisements. The data analysis unit also analyzes social media response data in real time to generate pop-up advertisements that match the latest trends. For example, it suggests advertisements that incorporate topics that are trending on social media. This allows promotional activities to attract customer attention by generating pop-up advertisements based on posts that have received a positive response on social media.

[0088] The data analysis unit can analyze access data from a store's website and generate advertisements tailored to the time periods with the most visitors. For example, the generation AI collects and analyzes access data from a store's website. For example, it generates advertisements tailored to the time periods with the most visitors. The data analysis unit also identifies visitor behavior patterns based on website access data and suggests optimal ad copy and images. For example, it generates advertisements tailored to pages with the most visitors during specific time periods. The data analysis unit also analyzes access data in real time and generates advertisements that are optimal for the time periods with the most visitors. For example, it automatically displays advertisements tailored to peak hours. This enables effective online marketing by displaying advertisements at the optimal time periods based on website access data.

[0089] The data analysis unit can analyze competitors' advertising data and generate unique advertisements that can compete with rivals. For example, the generation AI collects and analyzes the advertising data of a store's competitors. For example, it identifies the factors that make the competitors' advertisements successful and generates unique advertisements that can compete with them. The data analysis unit also suggests advertising copy and images that emphasize the store's strengths based on the competitors' advertising data. For example, it reflects the store's points of differentiation from competitors in the advertisement. The data analysis unit also analyzes competitors' advertising data in real time and generates unique advertisements that are in line with the latest trends. For example, it suggests advertisements that counter competitors' new campaigns. This allows the store to increase its competitiveness by analyzing competitors' advertising data and generating unique advertisements.

[0090] The data analysis unit can analyze a store's email marketing data and generate digital advertisements linked to email campaigns. For example, the generation AI collects and analyzes a store's email marketing data. For example, it generates digital advertisements that match the content of the email campaign. The data analysis unit also analyzes customer responses based on the email marketing data and suggests optimal advertising copy and images. For example, it reflects content with high email open rates and click rates in the advertisement. The data analysis unit also generates digital advertisements linked to email campaigns in real time and suggests advertisements that will attract customer interest. For example, it adjusts advertisements based on response data after emails are sent. In this way, an integrated marketing strategy can be realized by generating digital advertisements linked to email campaigns.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The data analysis unit uses generative AI to analyze the store's sales data and product information. For example, it analyzes best-selling products and sales trends based on sales data, and analyzes product characteristics and inventory status based on product information. It can also combine sales data and product information to perform comprehensive analysis. Step 2: The advertisement generation unit generates effective pop-up advertisements based on the data analyzed by the data analysis unit. For example, it generates pop-up advertisements that highlight best-selling products based on sales data, or pop-up advertisements that highlight product characteristics based on product information. It is also possible to generate comprehensive pop-up advertisements by combining sales data and product information. Step 3: The proposal unit proposes the POP advertisement generated by the advertisement generation unit to the store. For example, the generated POP advertisement can be displayed on the store's display, posted on the store's website or social media account, or distributed to store staff and customers.

[0093] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 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.

[0098] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0099] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0100] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0101] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0102] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0103] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0104] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0107] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0108] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0113] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0114] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0115] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0116] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0117] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0118] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0119] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0121] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0122] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0123] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0129] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0130] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0132] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0133] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0134] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0135] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0137] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0139] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0142] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0144] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0145] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0146] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0150] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0151] 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.

[0152] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0153] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0154] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0155] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0157] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0158] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A data analysis department uses generative AI to analyze store sales data and product information, an advertisement generation unit that generates an effective pop advertisement based on the data analyzed by the data analysis unit; a proposal unit that proposes the pop advertisement generated by the advertisement generation unit to a store. A system characterized by:

2. The data analysis unit Analyze customer sentiment data and propose catchphrases and designs that resonate most with customers.

2. The system of claim 1.

3. The data analysis unit Analyze inventory data in real time to generate optimal pop-up ads based on inventory status 2. The system of claim 1.

4. The data analysis unit Analyze the effectiveness of the store's past promotional campaigns and generate pop-up advertisements incorporating the most successful elements 2. The system of claim 1.

5. The data analysis unit Analyze photos of the store's exterior and interior and propose a pop-up advertising design that matches them 2. The system of claim 1.

6. The data analysis unit Link with the store's social media account and generate pop-up ads based on posts that have received positive responses on social media.

2. The system of claim 1.

7. The data analysis unit Analyzing the emotions of the store staff and generating pop-up advertisements that the staff can recommend with the most confidence 2. The system of claim 1.

8. The data analysis unit Analyzing consumer sentiment data to generate emotionally appealing advertising copy and images 2. The system of claim 1.

Images