System

Abstract

An object of a system according to an embodiment is to efficiently generate information of a coupon or a stamp card and provide the information to a customer.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a pop generation unit. The reception unit inputs information of a coupon or a stamp card. The analysis unit analyzes the information input by the reception unit. The generation unit generates a title or a description based on the information analyzed by the analysis unit. A POP generation part analyzes a photograph provided from a store side and generates an in-store POP.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 techniques have had the problem of making it difficult to efficiently generate coupon and stamp card information and provide it to customers.

[0005] The system according to the embodiment aims to efficiently generate coupon and stamp card information and provide it to customers. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a pop generation unit. The reception unit inputs coupon or stamp card information. The analysis unit analyzes the information input by the reception unit. The generation unit generates a title or description based on the information analyzed by the analysis unit. The pop generation unit analyzes photos provided by the store and generates in-store pops. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently generate coupon and stamp card information and provide it to customers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) In an embodiment of the present invention, a system inputs coupon or stamp card information, analyzes it using a generation AI to generate titles and descriptions, and generates in-store POPs using photos provided by the store. In this system, sales representatives input coupon or stamp card information to provide to customers, and the generation AI analyzes the information to generate appropriate titles and descriptions. Furthermore, details of the in-store POPs are generated using photos provided by the store. For example, if a sales representative inputs information about a "10% off coupon," the generation AI generates a title such as "Limited Time Only! 10% Off Coupon" and a description such as "Use this coupon to enjoy great deals on your shopping now!" The AI ​​then combines this with a photo of the store to generate the in-store POP. This reduces sales representatives' work time and provides customers with attractive information. This system reduces sales representatives' work time and improves customer satisfaction. Furthermore, the use of in-store POPs is expected to improve the effectiveness of in-store promotions and increase user usage.

[0029] A coupon generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a pop generation unit. The reception unit inputs coupon or stamp card information. The coupon or stamp card information includes, but is not limited to, the coupon discount rate and the stamp card benefits. The reception unit inputs detailed information, such as the coupon content, expiration date, and stamp card benefits. The analysis unit analyzes the information input by the reception unit. The analysis may be performed using, but is not limited to, text analysis or image analysis. The analysis unit analyzes the coupon content and extracts information for generating an appropriate title and description. The generation unit generates a title and description based on the information analyzed by the analysis unit. The generation unit generates an attractive title and description based on the coupon or stamp card content using, for example, a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates a title and description such as, for example, "Limited Time Only! 10% Off Coupon" or "Collect Stamps and Get Great Benefits!" The pop generation unit analyzes photos provided by the store and combines detailed information from coupons and stamp cards to generate in-store pops. The pop generation unit uses, for example, image analysis technology to analyze the provided photos and combine detailed information from coupons and stamp cards to generate attractive in-store pops. For example, the unit generates an in-store pop by combining a photo of the store with information such as "Limited Time Only! 10% Off Coupon." This enables efficient input, analysis, generation, and pop generation of coupon and stamp card information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit inputs the information analyzed by the analysis unit into a generation AI, which then generates titles and descriptions. This allows the system to reduce sales staff work time and improve customer satisfaction. Furthermore, the use of in-store pops is expected to improve in-store promotional effectiveness and increase user usage.

[0030] The generation unit can generate an attractive title or description based on the content of the coupon or stamp card. The generation unit generates an attractive title or description based on the content of the coupon or stamp card, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates titles and descriptions such as, for example, "Limited Time Only! 10% Off Coupon" or "Collect Stamps and Get Great Benefits!". The generation unit can also use the generation AI to generate titles and descriptions that include elements that attract customers' interest based on the content of the coupon or stamp card. For example, the generation unit generates an attractive catchphrase based on the coupon discount rate or the benefits of the stamp card. This allows for generating an attractive title or description based on the content of the coupon or stamp card, thereby providing customers with attractive information. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input information analyzed by the analysis unit into the generation AI, which then generates the title or description.

[0031] The pop generation unit can analyze photos provided by the store and combine them with detailed information from coupons and stamp cards to generate in-store pops. The pop generation unit can, for example, use image analysis technology to analyze the provided photos and combine them with detailed information from coupons and stamp cards to generate attractive in-store pops. For example, the pop generation unit can combine a photo of the store with information such as "Limited Time Only! 10% Off Coupon" to generate an in-store pop. The pop generation unit can also use a generation AI to analyze the provided photos and combine detailed information from coupons and stamp cards to generate in-store pops. For example, the generation AI analyzes the content of the photos and generates visually appealing in-store pops based on the information from the coupons and stamp cards. This improves the effectiveness of in-store promotions by analyzing photos provided by the store and combining detailed information from the coupons and stamp cards to generate in-store pops. Some or all of the above-described processing in the pop generation unit can be performed using, for example, AI, or without AI. For example, the pop generation unit can input provided photos into a generation AI, which then analyzes the content of the photos and generates an in-store pop.

[0032] The reception unit can input detailed information such as the coupon content or expiration date, stamp card benefits, etc. The reception unit inputs detailed information such as the coupon content, expiration date, and stamp card benefits. The detailed information includes, but is not limited to, the coupon discount rate, stamp card benefit content, and terms of use. For example, the reception unit inputs the coupon content, and the generation AI analyzes the information to generate an appropriate title and description. The reception unit can also input the stamp card benefit content, and the generation AI analyzes the information to generate an attractive title and description. Thus, accurate information can be provided by inputting detailed information such as the coupon content, expiration date, and stamp card benefits. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit inputs detailed information such as the coupon content, expiration date, and stamp card benefits to the generation AI, and the generation AI analyzes the information to generate an appropriate title and description.

[0033] The generation unit can provide the generated title and description to the customer. The generation unit can provide the title and description generated using, for example, a generation AI to the customer. Methods of providing the title and description include, but are not limited to, email, app notification, and display on a website. For example, the generation unit can send the generated title and description to the customer by email. The generation unit can also provide the generated title and description to the customer by app notification. Furthermore, the generation unit can display the generated title and description on a website and provide it to the customer. This improves customer satisfaction by providing the generated title and description to the customer. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can send the title and description generated by the generation AI to the customer by email.

[0034] The pop generation unit can display the generated in-store pop. The pop generation unit displays the in-store pop generated, for example, using a generation AI. Display methods include, for example, digital signage, printed materials, and display on a website, but are not limited to these examples. The pop generation unit, for example, displays the generated in-store pop on digital signage. The pop generation unit can also display the generated in-store pop as printed material. Furthermore, the pop generation unit can display the generated in-store pop on a website and provide it to customers. This improves the promotional effectiveness within the store by displaying the generated in-store pop. Some or all of the above-mentioned processing in the pop generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the pop generation unit can display the in-store pop generated by the generation AI on digital signage.

[0035] The reception unit can analyze past coupon and stamp card usage histories and select the optimal information input method. The reception unit, for example, analyzes past coupon and stamp card usage histories and selects the optimal information input method. The analysis can be performed, for example, using AI, but is not limited to such an example. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past usage history. Furthermore, the reception unit can analyze the user's past usage history and select the most efficient input method. In this way, the optimal information input method can be provided to the user by analyzing the past usage history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input past usage history data to a generation AI, which can select the optimal input method.

[0036] The reception unit can filter coupon and stamp card information based on the user's current purchasing trends and areas of interest when the coupon or stamp card information is input. For example, the reception unit can filter coupons and stamp cards based on the user's current purchasing trends and areas of interest when the coupon or stamp card information is input. The filtering can be performed using, for example, AI, but is not limited to, this example. For example, the reception unit can preferentially display coupons and stamp cards related to products recently purchased by the user. The reception unit can also filter related coupons and stamp cards based on the user's areas of interest. Furthermore, the reception unit can analyze the user's purchase history and suggest coupons and stamp cards that are of the user's greatest interest. Thus, filtering based on the user's purchasing trends and areas of interest can provide highly relevant information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input purchase history data to a generation AI, which can then perform the filtering.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting information for a coupon or stamp card. For example, when inputting information for a coupon or stamp card, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.). The selection can be performed using, for example, AI, but is not limited to, such an example. For example, if the user prefers voice input, the reception unit can preferentially provide voice input. Furthermore, if the user prefers text input, the reception unit can preferentially provide text input. Furthermore, if the user prefers image input, the reception unit can preferentially provide image input. This improves user convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI, which can select the optimal input means.

[0038] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting coupon or stamp card information. For example, when inputting coupon or stamp card information, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information. The geographical location information can be acquired, for example, using GPS data or a location information service, but is not limited to these examples. For example, the reception unit can prioritize displaying coupons and stamp cards for stores close to the user's current location. If the user is in a specific area, the reception unit can also display coupons and stamp cards related to that area. Furthermore, the reception unit can suggest the most relevant coupons and stamp cards based on the user's location information. This allows highly relevant information to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input location information data to a generation AI, which can select highly relevant information.

[0039] The reception unit can analyze the user's social media activity and input related information when inputting coupon or stamp card information. For example, the reception unit can analyze the user's social media activity and input related information when inputting coupon or stamp card information. The analysis can be performed, for example, using AI, but is not limited to this example. For example, the reception unit can display coupons or stamp cards related to locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related coupons or stamp cards. Furthermore, the reception unit can also suggest related coupons or stamp cards based on the activities of the user's friends on social media. This allows for the provision of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input social media data to a generation AI, which can select relevant information.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting information for a coupon or stamp card. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting information for a coupon or stamp card. Reflecting the feedback can be performed, for example, using AI, but is not limited to this example. For example, the reception unit can preferentially provide an input method that the user has previously preferred. The reception unit can also suggest an optimal input method based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input past feedback data to a generation AI, which can select the optimal input method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the coupon or stamp card during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the coupon or stamp card during analysis. The importance evaluation can be performed using, for example, AI, but is not limited to, this example. For example, the analysis unit can perform a detailed analysis of coupons or stamp cards with high importance. The analysis unit can also perform a concise analysis of coupons or stamp cards with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the coupon or stamp card. By adjusting the level of detail of the analysis based on the importance of the coupon or stamp card, important information can be analyzed in detail. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input importance data to a generation AI, which can then adjust the level of detail of the analysis.

[0042] The analysis unit can apply different analysis algorithms depending on the category of the coupon or stamp card during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the coupon or stamp card during analysis. Category classification is performed using, for example, AI, but is not limited to this example. For example, the analysis unit can apply an analysis algorithm dedicated to restaurants to coupons for restaurants. The analysis unit can also apply an analysis algorithm dedicated to retail stores to coupons for retail stores. Furthermore, the analysis unit can apply an analysis algorithm dedicated to service industries to coupons for service industries. In this way, by applying different analysis algorithms depending on the category of the coupon or stamp card, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data to a generation AI, which then applies an analysis algorithm according to the category.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The use of past analysis results can be performed, for example, using AI, but is not limited to such an example. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and propose an optimal analysis method. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0044] The analysis unit can determine the analysis priority based on the submission time of the coupon or stamp card during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the coupon or stamp card during analysis. The evaluation of the submission time is performed, for example, using AI, but is not limited to this example. For example, the analysis unit prioritizes the analysis of coupons or stamp cards submitted early. The analysis unit can also postpone coupons or stamp cards submitted late. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the coupon or stamp card. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input submission time data into a generation AI, which then determines the analysis priority.

[0045] The analysis unit can adjust the order of analysis based on the relevance of coupons and stamp cards during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of coupons and stamp cards during analysis. The evaluation of relevance is performed, for example, using AI, but is not limited to such an example. The analysis unit, for example, prioritizes analysis of highly relevant coupons and stamp cards. The analysis unit can also postpone analysis of less relevant coupons and stamp cards. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of coupons and stamp cards. In this way, by adjusting the order of analysis based on the relevance of coupons and stamp cards, highly relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data to a generation AI, which can then adjust the order of analysis.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The evaluation of the level of expertise can be performed, for example, using AI, but is not limited to such an example. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input expertise level data into a generation AI, which can then adjust the use of technical terms.

[0047] The generation unit can adjust the level of detail of the title and description generated based on the content of the coupon or stamp card during generation. For example, the generation unit adjusts the level of detail of the title and description generated based on the content of the coupon or stamp card during generation. The adjustment of the level of detail is performed, for example, using AI, but is not limited to this example. For example, the generation unit generates detailed titles and descriptions for coupons and stamp cards with high importance. The generation unit can also generate concise titles and descriptions for coupons and stamp cards with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the title and description according to the content of the coupon or stamp card. This allows appropriate information to be provided by adjusting the level of detail of the title and description based on the content of the coupon or stamp card. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input content data of the coupon or stamp card into the generation AI, which can then adjust the level of detail.

[0048] The generation unit can apply different generation algorithms depending on the category of the coupon or stamp card during generation. For example, the generation unit applies different generation algorithms depending on the category of the coupon or stamp card during generation. Category classification is performed using, for example, AI, but is not limited to this example. For example, the generation unit can apply a generation algorithm dedicated to restaurants to coupons for restaurants. The generation unit can also apply a generation algorithm dedicated to retail stores to coupons for retail stores. Furthermore, the generation unit can apply a generation algorithm dedicated to service industries to coupons for service industries. In this way, by applying different generation algorithms depending on the category of the coupon or stamp card, more accurate titles and descriptions can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data to the generation AI, and the generation AI can apply a generation algorithm according to the category.

[0049] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The use of past generation results can be performed, for example, using AI, but is not limited to such an example. For example, the generation unit can adjust the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and propose an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input past generation result data into the generation AI, which can improve the accuracy of generation.

[0050] The generation unit can determine the priority of the titles and descriptions to be generated based on the submission time of the coupon or stamp card at the time of generation. For example, the generation unit determines the priority of the titles and descriptions to be generated based on the submission time of the coupon or stamp card at the time of generation. Evaluation of the submission time is performed, for example, using AI, but is not limited to such an example. For example, the generation unit prioritizes the generation of coupons and stamp cards submitted earlier. The generation unit can also postpone coupons and stamp cards submitted later. Furthermore, the generation unit can dynamically adjust the generation priority based on the submission time. This enables efficient generation by determining the generation priority based on the submission time of the coupon or stamp card. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input submission time data into the generation AI, and the generation AI can determine the generation priority.

[0051] The generation unit can adjust the order of titles and descriptions to be generated based on the relevance of coupons and stamp cards during generation. The generation unit, for example, adjusts the order of titles and descriptions to be generated based on the relevance of coupons and stamp cards during generation. The evaluation of relevance is performed, for example, using AI, but is not limited to this example. For example, the generation unit prioritizes generating highly relevant coupons and stamp cards. The generation unit can also postpone less relevant coupons and stamp cards. Furthermore, the generation unit can dynamically adjust the generation order based on the relevance of coupons and stamp cards. Thus, by adjusting the generation order based on the relevance of coupons and stamp cards, highly relevant information can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevance data into the generation AI, which can then adjust the generation order.

[0052] The generation unit can adjust the use of technical terms in the title and description to be generated according to the user's level of expertise at the time of generation. For example, the generation unit can adjust the use of technical terms in the title and description to be generated according to the user's level of expertise at the time of generation. The evaluation of the expertise level can be performed, for example, using AI, but is not limited to such an example. For example, if the user has specialized knowledge, the generation unit can generate titles and descriptions that use a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the generation unit can generate concise and easy-to-understand titles and descriptions. Furthermore, the generation unit can dynamically adjust the use of technical terms in the title and description according to the user's level of expertise. This allows the user to be provided with information that is easy to understand by adjusting the use of technical terms in the title and description according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input expertise level data into the generation AI, which can then adjust the use of technical terms.

[0053] When generating an in-store POP, the POP generation unit can adjust the level of detail of the POP to be generated based on the importance of the photo provided by the store. For example, when generating an in-store POP, the POP generation unit adjusts the level of detail of the POP to be generated based on the importance of the photo provided by the store. The importance evaluation is performed, for example, using AI, but is not limited to this example. For example, the POP generation unit generates a detailed POP for a photo with high importance. The POP generation unit can also generate a concise POP for a photo with low importance. Furthermore, the POP generation unit can dynamically adjust the level of detail of the POP according to the importance of the photo. This allows appropriate information to be provided by adjusting the level of detail of the POP based on the importance of the photo provided by the store. Some or all of the above-described processing in the POP generation unit may be performed using AI, for example, or without AI. For example, the POP generation unit can input photo importance data into the generation AI, which can then adjust the level of detail.

[0054] When generating in-store POPs, the POP generation unit can apply different generation algorithms depending on the category of the store. For example, when generating in-store POPs, the POP generation unit applies different generation algorithms depending on the category of the store. Category classification is performed using, for example, AI, but is not limited to this example. For example, the POP generation unit applies a generation algorithm dedicated to restaurants to POPs for restaurants. The POP generation unit can also apply a generation algorithm dedicated to retail stores to POPs for retail stores. Furthermore, the POP generation unit can also apply a generation algorithm dedicated to the service industry to POPs for service industries. In this way, by applying different generation algorithms depending on the category of the store, in-store POPs with higher accuracy can be generated. Some or all of the above-mentioned processing in the POP generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the POP generation unit can input category data into a generation AI, which then applies a generation algorithm according to the category.

[0055] When generating in-store POPs, the POP generation unit can improve the accuracy of generation by referring to past generation results. When generating in-store POPs, the POP generation unit can improve the accuracy of generation by referring to past generation results, for example. The use of past generation results can be performed, for example, using AI, but is not limited to such an example. For example, the POP generation unit can adjust the generation algorithm based on past generation results. The POP generation unit can also improve the accuracy of generation by referring to past generation results. Furthermore, the POP generation unit can analyze past generation results and propose an optimal generation method. In this way, the accuracy of generation can be improved by referring to past generation results. Some or all of the above-mentioned processing in the POP generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the POP generation unit can input past generation result data into the generation AI, which can improve the accuracy of generation.

[0056] When generating in-store POPs, the POP generation unit can determine the priority of POPs to be generated based on the store's availability period. For example, when generating in-store POPs, the POP generation unit determines the priority of POPs to be generated based on the store's availability period. The evaluation of the availability period is performed, for example, using AI, but is not limited to this example. For example, the POP generation unit prioritizes the generation of POPs for stores with earlier availability periods. The POP generation unit can also postpone the generation of POPs for stores with later availability periods. Furthermore, the POP generation unit can dynamically adjust the generation priority based on the availability period. This enables efficient generation by determining the generation priority based on the store's availability period. Some or all of the above-mentioned processing in the POP generation unit may be performed using AI, for example, or may be performed without using AI. For example, the POP generation unit can input availability period data into a generation AI, which can then determine the generation priority.

[0057] The pop generation unit can adjust the order of pops to be generated based on the relevance of stores when generating in-store pops. For example, the pop generation unit adjusts the order of pops to be generated based on the relevance of stores when generating in-store pops. The evaluation of relevance is performed, for example, using AI, but is not limited to such an example. For example, the pop generation unit prioritizes generating pops for highly relevant stores. The pop generation unit can also postpone generating pops for less relevant stores. Furthermore, the pop generation unit can dynamically adjust the order of generation based on the relevance of stores. In this way, by adjusting the order of generation based on the relevance of stores, highly relevant information can be generated preferentially. Some or all of the above-mentioned processing in the pop generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pop generation unit can input relevance data into a generation AI, which can adjust the order of generation.

[0058] When generating in-store POPs, the POP generation unit can adjust the use of technical terms in the POPs generated according to the store's level of expertise. For example, when generating in-store POPs, the POP generation unit adjusts the use of technical terms in the POPs generated according to the store's level of expertise. The evaluation of the expertise level is performed, for example, using AI, but is not limited to this example. For example, if the store has technical expertise, the POP generation unit generates a POP that uses a lot of technical terms. Furthermore, if the store does not have technical expertise, the POP generation unit can also generate a concise and easy-to-understand POP. Furthermore, the POP generation unit can dynamically adjust the use of technical terms in the POPs according to the store's level of expertise. This allows the store to provide information that is easy to understand. Some or all of the above-described processing in the POP generation unit may be performed using AI, for example, or without AI. For example, the POP generation unit can input expertise level data into the generation AI, which then adjusts the use of technical terms.

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

[0060] The reception unit can analyze the user's purchase history and automatically input coupon and stamp card information based on past purchase patterns. For example, if the user has frequently purchased products from a specific brand in the past, coupon information for that brand can be input preferentially. Also, if the user prefers to purchase products from a specific category, coupon information related to that category can be automatically input. Furthermore, it is also possible to automatically input the most frequently used coupon information based on the user's purchase history. This improves user convenience by automatically inputting coupon and stamp card information based on the user's purchase history, and enables the provision of more personalized services.

[0061] The generation unit can generate titles and descriptions that include elements that attract the user's interest based on the content of the coupon or stamp card. For example, the generation unit generates an attractive catchphrase based on the coupon discount rate or the benefits of the stamp card. The generation unit can also analyze the user's past usage history and generate titles and descriptions using the most effective expressions. Furthermore, the generation unit can generate titles and descriptions that match the season or event. In this way, by generating attractive titles and descriptions based on the content of the coupon or stamp card, it is possible to provide attractive information to customers.

[0062] The reception unit can input optimal coupon and stamp card information taking into account the user's geographical location information. For example, if the user is in a specific area, coupon information related to that area is input preferentially. Also, if the user is near a specific store, coupon information for that store can be automatically input. Furthermore, it is also possible to suggest the most relevant coupon information based on the user's location information. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0063] The pop generation unit can adjust the content of in-store pop-ups based on the inventory status of products offered by the store. For example, for products with abundant stock, it generates pop-ups that actively promote them. For products with low stock, it can also generate pop-ups that emphasize a sense of exclusivity. Furthermore, for products that are out of stock, it can also generate pop-ups that include suggestions for alternative products. This allows for effective promotions by adjusting the content of in-store pop-ups based on the inventory status of the store.

[0064] The reception unit can analyze the user's social media activity and input information about related coupons and stamp cards. For example, it prioritizes inputting coupon information related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and suggest related coupon information. It can also suggest related coupon information based on the activity of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0065] The pop generation unit can customize the design of in-store pops according to the type of service provided by the store. For example, in the case of a restaurant, it can generate a pop with an appetizing design. In addition, in the case of a retail store, it can also generate a pop with a design that highlights the appeal of the product. Furthermore, in the case of the service industry, it can also generate a pop with a design that emphasizes the features of the service. In this way, by customizing the design of in-store pops according to the type of service provided by the store, effective promotions can be carried out.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs coupon or stamp card information. Detailed information such as the coupon discount rate and the benefits of the stamp card can be input. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using methods such as text analysis and image analysis to analyze the content of the coupon and extract information to generate an appropriate title and description. Step 3: The generator generates a title and description based on the information analyzed by the analyzer. Using the generation AI, it generates an attractive title and description based on the content of the coupon or stamp card. Step 4: The pop-up generation unit analyzes the photo provided by the store and combines detailed information from the coupon and stamp card to generate an in-store pop-up. Image analysis technology is used to combine the provided photo with information from the coupon and stamp card to generate an attractive in-store pop-up.

[0068] (Example 2) In an embodiment of the present invention, a system inputs coupon or stamp card information, analyzes it using a generation AI to generate titles and descriptions, and generates in-store POPs using photos provided by the store. In this system, sales representatives input coupon or stamp card information to provide to customers, and the generation AI analyzes the information to generate appropriate titles and descriptions. Furthermore, details of the in-store POPs are generated using photos provided by the store. For example, if a sales representative inputs information about a "10% off coupon," the generation AI generates a title such as "Limited Time Only! 10% Off Coupon" and a description such as "Use this coupon to enjoy great deals on your shopping now!" The AI ​​then combines this with a photo of the store to generate the in-store POP. This reduces sales representatives' work time and provides customers with attractive information. This system reduces sales representatives' work time and improves customer satisfaction. Furthermore, the use of in-store POPs is expected to improve the effectiveness of in-store promotions and increase user usage.

[0069] A coupon generation system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a pop generation unit. The reception unit inputs coupon or stamp card information. The coupon or stamp card information includes, but is not limited to, the coupon discount rate and the stamp card benefits. The reception unit inputs detailed information, such as the coupon content, expiration date, and stamp card benefits. The analysis unit analyzes the information input by the reception unit. The analysis may be performed using, but is not limited to, text analysis or image analysis. The analysis unit analyzes the coupon content and extracts information for generating an appropriate title and description. The generation unit generates a title and description based on the information analyzed by the analysis unit. The generation unit generates an attractive title and description based on the coupon or stamp card content using, for example, a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates a title and description such as, for example, "Limited Time Only! 10% Off Coupon" or "Collect Stamps and Get Great Benefits!" The pop generation unit analyzes photos provided by the store and combines detailed information from coupons and stamp cards to generate in-store pops. The pop generation unit uses, for example, image analysis technology to analyze the provided photos and combine detailed information from coupons and stamp cards to generate attractive in-store pops. For example, the unit generates an in-store pop by combining a photo of the store with information such as "Limited Time Only! 10% Off Coupon." This enables efficient input, analysis, generation, and pop generation of coupon and stamp card information. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without AI. For example, the generation unit inputs the information analyzed by the analysis unit into a generation AI, which then generates titles and descriptions. This allows the system to reduce sales staff work time and improve customer satisfaction. Furthermore, the use of in-store pops is expected to improve in-store promotional effectiveness and increase user usage.

[0070] The generation unit can generate an attractive title or description based on the content of the coupon or stamp card. The generation unit generates an attractive title or description based on the content of the coupon or stamp card, for example, using a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. The generation unit generates titles and descriptions such as, for example, "Limited Time Only! 10% Off Coupon" or "Collect Stamps and Get Great Benefits!". The generation unit can also use the generation AI to generate titles and descriptions that include elements that attract customers' interest based on the content of the coupon or stamp card. For example, the generation unit generates an attractive catchphrase based on the coupon discount rate or the benefits of the stamp card. This allows for generating an attractive title or description based on the content of the coupon or stamp card, thereby providing customers with attractive information. Some or all of the above-described processing in the generation unit may be performed using, for example, an AI. For example, the generation unit can input information analyzed by the analysis unit into the generation AI, which then generates the title or description.

[0071] The pop generation unit can analyze photos provided by the store and combine them with detailed information from coupons and stamp cards to generate in-store pops. The pop generation unit can, for example, use image analysis technology to analyze the provided photos and combine them with detailed information from coupons and stamp cards to generate attractive in-store pops. For example, the pop generation unit can combine a photo of the store with information such as "Limited Time Only! 10% Off Coupon" to generate an in-store pop. The pop generation unit can also use a generation AI to analyze the provided photos and combine detailed information from coupons and stamp cards to generate in-store pops. For example, the generation AI analyzes the content of the photos and generates visually appealing in-store pops based on the information from the coupons and stamp cards. This improves the effectiveness of in-store promotions by analyzing photos provided by the store and combining detailed information from the coupons and stamp cards to generate in-store pops. Some or all of the above-described processing in the pop generation unit can be performed using, for example, AI, or without AI. For example, the pop generation unit can input provided photos into a generation AI, which then analyzes the content of the photos and generates an in-store pop.

[0072] The reception unit can input detailed information such as the coupon content or expiration date, stamp card benefits, etc. The reception unit inputs detailed information such as the coupon content, expiration date, and stamp card benefits. The detailed information includes, but is not limited to, the coupon discount rate, stamp card benefit content, and terms of use. For example, the reception unit inputs the coupon content, and the generation AI analyzes the information to generate an appropriate title and description. The reception unit can also input the stamp card benefit content, and the generation AI analyzes the information to generate an attractive title and description. Thus, accurate information can be provided by inputting detailed information such as the coupon content, expiration date, and stamp card benefits. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit inputs detailed information such as the coupon content, expiration date, and stamp card benefits to the generation AI, and the generation AI analyzes the information to generate an appropriate title and description.

[0073] The generation unit can provide the generated title and description to the customer. The generation unit can provide the title and description generated using, for example, a generation AI to the customer. Methods of providing the title and description include, but are not limited to, email, app notification, and display on a website. For example, the generation unit can send the generated title and description to the customer by email. The generation unit can also provide the generated title and description to the customer by app notification. Furthermore, the generation unit can display the generated title and description on a website and provide it to the customer. This improves customer satisfaction by providing the generated title and description to the customer. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can send the title and description generated by the generation AI to the customer by email.

[0074] The pop generation unit can display the generated in-store pop. The pop generation unit displays the in-store pop generated, for example, using a generation AI. Display methods include, for example, digital signage, printed materials, and display on a website, but are not limited to these examples. The pop generation unit, for example, displays the generated in-store pop on digital signage. The pop generation unit can also display the generated in-store pop as printed material. Furthermore, the pop generation unit can display the generated in-store pop on a website and provide it to customers. This improves the promotional effectiveness within the store by displaying the generated in-store pop. Some or all of the above-mentioned processing in the pop generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the pop generation unit can display the in-store pop generated by the generation AI on digital signage.

[0075] The reception unit can estimate the user's emotions and adjust the timing of information input for coupons and stamp cards based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and adjusts the timing of information input for coupons and stamp cards based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if the user is feeling stressed, the reception unit can delay the input timing to encourage the user to enter information in a relaxed state. Furthermore, if the user is relaxed, the reception unit can prompt the user to enter information immediately, thereby achieving smooth operation. Furthermore, if the user is in a hurry, the reception unit can accelerate the input timing to allow the user to enter information quickly. By adjusting the timing of information input based on the user's emotions, the user's stress is reduced and smooth operation is achieved. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion and adjust the input timing.

[0076] The reception unit can analyze past coupon and stamp card usage histories and select the optimal information input method. The reception unit, for example, analyzes past coupon and stamp card usage histories and selects the optimal information input method. The analysis can be performed, for example, using AI, but is not limited to such an example. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the user has frequently used in the past. The reception unit can also suggest the optimal input method for a specific time period based on the user's past usage history. Furthermore, the reception unit can analyze the user's past usage history and select the most efficient input method. In this way, the optimal information input method can be provided to the user by analyzing the past usage history. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input past usage history data to a generation AI, which can select the optimal input method.

[0077] The reception unit can filter coupon and stamp card information based on the user's current purchasing trends and areas of interest when the coupon or stamp card information is input. For example, the reception unit can filter coupons and stamp cards based on the user's current purchasing trends and areas of interest when the coupon or stamp card information is input. The filtering can be performed using, for example, AI, but is not limited to, this example. For example, the reception unit can preferentially display coupons and stamp cards related to products recently purchased by the user. The reception unit can also filter related coupons and stamp cards based on the user's areas of interest. Furthermore, the reception unit can analyze the user's purchase history and suggest coupons and stamp cards that are of the user's greatest interest. Thus, filtering based on the user's purchasing trends and areas of interest can provide highly relevant information. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input purchase history data to a generation AI, which can then perform the filtering.

[0078] The reception unit can select the optimal input means depending on the user's input method when inputting information for a coupon or stamp card. For example, when inputting information for a coupon or stamp card, the reception unit selects the optimal input means depending on the user's input method (voice, text, image, etc.). The selection can be performed using, for example, AI, but is not limited to, such an example. For example, if the user prefers voice input, the reception unit can preferentially provide voice input. Furthermore, if the user prefers text input, the reception unit can preferentially provide text input. Furthermore, if the user prefers image input, the reception unit can preferentially provide image input. This improves user convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI, which can select the optimal input means.

[0079] The reception unit can estimate the user's emotions and determine the priority of coupons and stamp cards to be entered based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of coupons and stamp cards to be entered based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, when the user is excited, the reception unit may preferentially display particularly attractive coupons and stamp cards. Furthermore, when the user is relaxed, the reception unit may preferentially display regular coupons and stamp cards. Furthermore, when the user is stressed, the reception unit may preferentially display coupons and stamp cards that are useful for stress reduction. By thus determining the priority of coupons and stamp cards based on the user's emotions, information that is attractive to the user can be preferentially provided. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's emotion data into the generation AI, which can then estimate the emotion and determine priorities.

[0080] The reception unit can prioritize inputting highly relevant information in consideration of the user's geographical location information when inputting coupon or stamp card information. For example, when inputting coupon or stamp card information, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information. The geographical location information can be acquired, for example, using GPS data or a location information service, but is not limited to these examples. For example, the reception unit can prioritize displaying coupons and stamp cards for stores close to the user's current location. If the user is in a specific area, the reception unit can also display coupons and stamp cards related to that area. Furthermore, the reception unit can suggest the most relevant coupons and stamp cards based on the user's location information. This allows highly relevant information to be provided by taking the user's geographical location information into consideration. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input location information data to a generation AI, which can select highly relevant information.

[0081] The reception unit can analyze the user's social media activity and input related information when inputting coupon or stamp card information. For example, the reception unit can analyze the user's social media activity and input related information when inputting coupon or stamp card information. The analysis can be performed, for example, using AI, but is not limited to this example. For example, the reception unit can display coupons or stamp cards related to locations where the user has checked in on social media. The reception unit can also analyze the content of the user's social media posts and suggest related coupons or stamp cards. Furthermore, the reception unit can also suggest related coupons or stamp cards based on the activities of the user's friends on social media. This allows for the provision of highly relevant information by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit can be performed, for example, using AI, or can be performed without using AI. For example, the reception unit can input social media data to a generation AI, which can select relevant information.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting information for a coupon or stamp card. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting information for a coupon or stamp card. Reflecting the feedback can be performed, for example, using AI, but is not limited to this example. For example, the reception unit can preferentially provide an input method that the user has previously preferred. The reception unit can also suggest an optimal input method based on the user's past feedback. Furthermore, the reception unit can customize the input interface by reflecting the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input past feedback data to a generation AI, which can select the optimal input method.

[0083] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit can provide detailed analysis results when the user is relaxed. Furthermore, the analysis unit can provide concise analysis results that focus on the main points when the user is in a hurry. Furthermore, the analysis unit can provide visually appealing analysis results when the user is excited. By adjusting the presentation method of the analysis based on the user's emotion, it is possible to provide analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the presentation method of the analysis.

[0084] The analysis unit can adjust the level of detail of the analysis based on the importance of the coupon or stamp card during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the coupon or stamp card during analysis. The importance evaluation can be performed using, for example, AI, but is not limited to, this example. For example, the analysis unit can perform a detailed analysis of coupons or stamp cards with high importance. The analysis unit can also perform a concise analysis of coupons or stamp cards with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the coupon or stamp card. By adjusting the level of detail of the analysis based on the importance of the coupon or stamp card, important information can be analyzed in detail. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input importance data to a generation AI, which can then adjust the level of detail of the analysis.

[0085] The analysis unit can apply different analysis algorithms depending on the category of the coupon or stamp card during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the coupon or stamp card during analysis. Category classification is performed using, for example, AI, but is not limited to this example. For example, the analysis unit can apply an analysis algorithm dedicated to restaurants to coupons for restaurants. The analysis unit can also apply an analysis algorithm dedicated to retail stores to coupons for retail stores. Furthermore, the analysis unit can apply an analysis algorithm dedicated to service industries to coupons for service industries. In this way, by applying different analysis algorithms depending on the category of the coupon or stamp card, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data to a generation AI, which then applies an analysis algorithm according to the category.

[0086] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. The use of past analysis results can be performed, for example, using AI, but is not limited to such an example. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by referring to the user's past analysis results. Furthermore, the analysis unit can analyze the user's past analysis results and propose an optimal analysis method. This can improve the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI, or can be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, which can improve the accuracy of the analysis.

[0087] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user emotion. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the analysis unit can provide a short and concise analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. Furthermore, the analysis unit can provide a visually appealing analysis result when the user is excited. By adjusting the length of the analysis based on the user's emotion, an analysis result of an appropriate length for the user can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI, which can estimate the emotion and adjust the length of the analysis.

[0088] The analysis unit can determine the analysis priority based on the submission time of the coupon or stamp card during analysis. The analysis unit, for example, determines the analysis priority based on the submission time of the coupon or stamp card during analysis. The evaluation of the submission time is performed, for example, using AI, but is not limited to this example. For example, the analysis unit prioritizes the analysis of coupons or stamp cards submitted early. The analysis unit can also postpone coupons or stamp cards submitted late. Furthermore, the analysis unit can dynamically adjust the analysis priority based on the submission time. This enables efficient analysis by determining the analysis priority based on the submission time of the coupon or stamp card. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input submission time data into a generation AI, which then determines the analysis priority.

[0089] The analysis unit can adjust the order of analysis based on the relevance of coupons and stamp cards during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of coupons and stamp cards during analysis. The evaluation of relevance is performed, for example, using AI, but is not limited to such an example. The analysis unit, for example, prioritizes analysis of highly relevant coupons and stamp cards. The analysis unit can also postpone analysis of less relevant coupons and stamp cards. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of coupons and stamp cards. In this way, by adjusting the order of analysis based on the relevance of coupons and stamp cards, highly relevant information can be prioritized for analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data to a generation AI, which can then adjust the order of analysis.

[0090] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The evaluation of the level of expertise can be performed, for example, using AI, but is not limited to such an example. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can also provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand by adjusting the use of technical terms in the analysis according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using AI, for example, or without AI. For example, the analysis unit can input expertise level data into a generation AI, which can then adjust the use of technical terms.

[0091] The generation unit can estimate the user's emotions and adjust the expression style of the generated title and description based on the estimated user's emotions. The generation unit, for example, estimates the user's emotions and adjusts the expression style of the generated title and description based on the estimated user's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can generate a title and description with soft expressions when the user is relaxed. Furthermore, the generation unit can generate a title and description that is concise and to the point when the user is in a hurry. Furthermore, the generation unit can generate a title and description with visually appealing expressions when the user is excited. By adjusting the expression style of the title and description based on the user's emotions, it is possible to provide information that is appealing to the user. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the expression method.

[0092] The generation unit can adjust the level of detail of the title and description generated based on the content of the coupon or stamp card during generation. For example, the generation unit adjusts the level of detail of the title and description generated based on the content of the coupon or stamp card during generation. The adjustment of the level of detail is performed, for example, using AI, but is not limited to this example. For example, the generation unit generates detailed titles and descriptions for coupons and stamp cards with high importance. The generation unit can also generate concise titles and descriptions for coupons and stamp cards with low importance. Furthermore, the generation unit can dynamically adjust the level of detail of the title and description according to the content of the coupon or stamp card. This allows appropriate information to be provided by adjusting the level of detail of the title and description based on the content of the coupon or stamp card. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input content data of the coupon or stamp card into the generation AI, which can then adjust the level of detail.

[0093] The generation unit can apply different generation algorithms depending on the category of the coupon or stamp card during generation. For example, the generation unit applies different generation algorithms depending on the category of the coupon or stamp card during generation. Category classification is performed using, for example, AI, but is not limited to this example. For example, the generation unit can apply a generation algorithm dedicated to restaurants to coupons for restaurants. The generation unit can also apply a generation algorithm dedicated to retail stores to coupons for retail stores. Furthermore, the generation unit can apply a generation algorithm dedicated to service industries to coupons for service industries. In this way, by applying different generation algorithms depending on the category of the coupon or stamp card, more accurate titles and descriptions can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input category data to the generation AI, and the generation AI can apply a generation algorithm according to the category.

[0094] The generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. For example, the generation unit can improve the accuracy of generation by referring to the user's past generation results during generation. The use of past generation results can be performed, for example, using AI, but is not limited to such an example. For example, the generation unit can adjust the generation algorithm based on the user's past generation results. The generation unit can also improve the accuracy of generation by referring to the user's past generation results. Furthermore, the generation unit can analyze the user's past generation results and propose an optimal generation method. In this way, the accuracy of generation can be improved by referring to the user's past generation results. Some or all of the above-mentioned processing in the generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the generation unit can input past generation result data into the generation AI, which can improve the accuracy of generation.

[0095] The generation unit can estimate the user's emotions and adjust the length of the generated title and description based on the estimated user emotions. The generation unit, for example, estimates the user's emotions and adjusts the length of the generated title and description based on the estimated user emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit can generate a short and to-the-point title and description when the user is in a hurry. The generation unit can also generate a longer title and description with detailed explanations when the user is relaxed. Furthermore, the generation unit can generate a title and description with visually appealing expressions when the user is excited. By adjusting the length of the title and description based on the user's emotions, it is possible to provide information of an appropriate length for the user. Some or all of the above-described processing in the generation unit can be performed, for example, using AI or without AI. For example, the generation unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the length of the title and description.

[0096] The generation unit can determine the priority of the titles and descriptions to be generated based on the submission time of the coupon or stamp card at the time of generation. For example, the generation unit determines the priority of the titles and descriptions to be generated based on the submission time of the coupon or stamp card at the time of generation. Evaluation of the submission time is performed, for example, using AI, but is not limited to such an example. For example, the generation unit prioritizes the generation of coupons and stamp cards submitted earlier. The generation unit can also postpone coupons and stamp cards submitted later. Furthermore, the generation unit can dynamically adjust the generation priority based on the submission time. This enables efficient generation by determining the generation priority based on the submission time of the coupon or stamp card. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input submission time data into the generation AI, and the generation AI can determine the generation priority.

[0097] The generation unit can adjust the order of titles and descriptions to be generated based on the relevance of coupons and stamp cards during generation. The generation unit, for example, adjusts the order of titles and descriptions to be generated based on the relevance of coupons and stamp cards during generation. The evaluation of relevance is performed, for example, using AI, but is not limited to this example. For example, the generation unit prioritizes generating highly relevant coupons and stamp cards. The generation unit can also postpone less relevant coupons and stamp cards. Furthermore, the generation unit can dynamically adjust the generation order based on the relevance of coupons and stamp cards. Thus, by adjusting the generation order based on the relevance of coupons and stamp cards, highly relevant information can be generated preferentially. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input relevance data into the generation AI, which can then adjust the generation order.

[0098] The generation unit can adjust the use of technical terms in the title and description to be generated according to the user's level of expertise at the time of generation. For example, the generation unit can adjust the use of technical terms in the title and description to be generated according to the user's level of expertise at the time of generation. The evaluation of the expertise level can be performed, for example, using AI, but is not limited to such an example. For example, if the user has specialized knowledge, the generation unit can generate titles and descriptions that use a lot of technical terms. Furthermore, if the user does not have specialized knowledge, the generation unit can generate concise and easy-to-understand titles and descriptions. Furthermore, the generation unit can dynamically adjust the use of technical terms in the title and description according to the user's level of expertise. This allows the user to be provided with information that is easy to understand by adjusting the use of technical terms in the title and description according to the user's level of expertise. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input expertise level data into the generation AI, which can then adjust the use of technical terms.

[0099] The pop generation unit can estimate the user's emotions and adjust the expression method of the in-store pop generated based on the estimated user emotions. The pop generation unit, for example, estimates the user's emotions and adjusts the expression method of the in-store pop generated based on the estimated user emotions. Emotion estimation is achieved, for example, by using an emotion estimation function with an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the pop generation unit generates an in-store pop with a gentle expression when the user is relaxed. Furthermore, the pop generation unit can generate an in-store pop that is concise and to the point when the user is in a hurry. Furthermore, the pop generation unit can generate an in-store pop with a visually appealing expression when the user is excited. In this way, by adjusting the expression method of the in-store pop based on the user's emotions, it is possible to provide an in-store pop that is appealing to the user. Some or all of the above-mentioned processing in the pop generation unit may be performed, for example, using AI or without using AI. For example, the pop generation unit can input the user's emotional data into the generation AI, which can then infer the emotion and adjust the expression method.

[0100] When generating an in-store POP, the POP generation unit can adjust the level of detail of the POP to be generated based on the importance of the photo provided by the store. For example, when generating an in-store POP, the POP generation unit adjusts the level of detail of the POP to be generated based on the importance of the photo provided by the store. The importance evaluation is performed, for example, using AI, but is not limited to this example. For example, the POP generation unit generates a detailed POP for a photo with high importance. The POP generation unit can also generate a concise POP for a photo with low importance. Furthermore, the POP generation unit can dynamically adjust the level of detail of the POP according to the importance of the photo. This allows appropriate information to be provided by adjusting the level of detail of the POP based on the importance of the photo provided by the store. Some or all of the above-described processing in the POP generation unit may be performed using AI, for example, or without AI. For example, the POP generation unit can input photo importance data into the generation AI, which can then adjust the level of detail.

[0101] When generating in-store POPs, the POP generation unit can apply different generation algorithms depending on the category of the store. For example, when generating in-store POPs, the POP generation unit applies different generation algorithms depending on the category of the store. Category classification is performed using, for example, AI, but is not limited to this example. For example, the POP generation unit applies a generation algorithm dedicated to restaurants to POPs for restaurants. The POP generation unit can also apply a generation algorithm dedicated to retail stores to POPs for retail stores. Furthermore, the POP generation unit can also apply a generation algorithm dedicated to the service industry to POPs for service industries. In this way, by applying different generation algorithms depending on the category of the store, in-store POPs with higher accuracy can be generated. Some or all of the above-mentioned processing in the POP generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the POP generation unit can input category data into a generation AI, which then applies a generation algorithm according to the category.

[0102] When generating in-store POPs, the POP generation unit can improve the accuracy of generation by referring to past generation results. When generating in-store POPs, the POP generation unit can improve the accuracy of generation by referring to past generation results, for example. The use of past generation results can be performed, for example, using AI, but is not limited to such an example. For example, the POP generation unit can adjust the generation algorithm based on past generation results. The POP generation unit can also improve the accuracy of generation by referring to past generation results. Furthermore, the POP generation unit can analyze past generation results and propose an optimal generation method. In this way, the accuracy of generation can be improved by referring to past generation results. Some or all of the above-mentioned processing in the POP generation unit can be performed, for example, using AI, or can be performed without using AI. For example, the POP generation unit can input past generation result data into the generation AI, which can improve the accuracy of generation.

[0103] The pop generation unit can estimate the user's emotions and adjust the length of the in-store pop generated based on the estimated user emotions. The pop generation unit, for example, estimates the user's emotions and adjusts the length of the in-store pop generated based on the estimated user emotions. Emotion estimation is achieved, for example, by using an emotion estimation function with an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the pop generation unit generates a short and to-the-point in-store pop if the user is in a hurry. The pop generation unit can also generate a longer in-store pop with detailed explanations if the user is relaxed. Furthermore, the pop generation unit can generate an in-store pop with visually appealing expressions if the user is excited. By adjusting the length of the in-store pop based on the user's emotions, it is possible to provide information of an appropriate length to the user. Some or all of the above-described processing in the pop generation unit may be performed, for example, using AI or without AI. For example, the pop generation unit can input the user's emotional data into the generation AI, which can then estimate the emotion and adjust the length of the in-store pop.

[0104] When generating in-store POPs, the POP generation unit can determine the priority of POPs to be generated based on the store's availability period. For example, when generating in-store POPs, the POP generation unit determines the priority of POPs to be generated based on the store's availability period. The evaluation of the availability period is performed, for example, using AI, but is not limited to this example. For example, the POP generation unit prioritizes the generation of POPs for stores with earlier availability periods. The POP generation unit can also postpone the generation of POPs for stores with later availability periods. Furthermore, the POP generation unit can dynamically adjust the generation priority based on the availability period. This enables efficient generation by determining the generation priority based on the store's availability period. Some or all of the above-mentioned processing in the POP generation unit may be performed using AI, for example, or may be performed without using AI. For example, the POP generation unit can input availability period data into a generation AI, which can then determine the generation priority.

[0105] The pop generation unit can adjust the order of pops to be generated based on the relevance of stores when generating in-store pops. For example, the pop generation unit adjusts the order of pops to be generated based on the relevance of stores when generating in-store pops. The evaluation of relevance is performed, for example, using AI, but is not limited to such an example. For example, the pop generation unit prioritizes generating pops for highly relevant stores. The pop generation unit can also postpone generating pops for less relevant stores. Furthermore, the pop generation unit can dynamically adjust the order of generation based on the relevance of stores. In this way, by adjusting the order of generation based on the relevance of stores, highly relevant information can be generated preferentially. Some or all of the above-mentioned processing in the pop generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the pop generation unit can input relevance data into a generation AI, which can adjust the order of generation.

[0106] When generating in-store POPs, the POP generation unit can adjust the use of technical terms in the POPs generated according to the store's level of expertise. For example, when generating in-store POPs, the POP generation unit adjusts the use of technical terms in the POPs generated according to the store's level of expertise. The evaluation of the expertise level is performed, for example, using AI, but is not limited to this example. For example, if the store has technical expertise, the POP generation unit generates a POP that uses a lot of technical terms. Furthermore, if the store does not have technical expertise, the POP generation unit can also generate a concise and easy-to-understand POP. Furthermore, the POP generation unit can dynamically adjust the use of technical terms in the POPs according to the store's level of expertise. This allows the store to provide information that is easy to understand. Some or all of the above-described processing in the POP generation unit may be performed using AI, for example, or without AI. For example, the POP generation unit can input expertise level data into the generation AI, which then adjusts the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and POP generation unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and inputs information on coupons and stamp cards. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a title and description based on the analyzed information. The POP generation unit is realized by the control unit 46A of the smart device 14 and analyzes photos provided by the store and combines detailed information on coupons and stamp cards to generate in-store POPs. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, analysis unit, generation unit, and POP generation unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and inputs information on coupons and stamp cards. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a title and description based on the analyzed information. The POP generation unit is realized by the control unit 46A of the smart glasses 214 and analyzes photos provided by the store and combines detailed information on coupons and stamp cards to generate in-store POPs. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, and POP generation unit is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset terminal 314 and inputs information on coupons and stamp cards. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a title and description based on the analyzed information. The POP generation unit is realized by the control unit 46A of the headset terminal 314 and analyzes photos provided by the store and combines detailed information on coupons and stamp cards to generate in-store POPs. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, generation unit, and POP generation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and inputs information on coupons and stamp cards. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a title and description based on the analyzed information. The POP generation unit is realized by the control unit 46A of the robot 414 and analyzes photos provided by the store and combines detailed information on the coupons and stamp cards to generate in-store POPs.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] The reception unit can analyze the user's purchase history and automatically input coupon and stamp card information based on past purchase patterns. For example, if the user has frequently purchased products from a specific brand in the past, coupon information for that brand can be input preferentially. Also, if the user prefers to purchase products from a specific category, coupon information related to that category can be automatically input. Furthermore, it is also possible to automatically input the most frequently used coupon information based on the user's purchase history. This improves user convenience by automatically inputting coupon and stamp card information based on the user's purchase history, and enables the provision of more personalized services.

[0109] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, the analysis results can be displayed in a concise summary. If the user is relaxed, detailed analysis results can be provided. Furthermore, if the user is excited, the analysis results can be displayed using visually appealing graphics. In this way, by adjusting the display method of the analysis results based on the user's emotions, it is possible to provide information that is easy for the user to understand.

[0110] The generation unit can generate titles and descriptions that include elements that attract the user's interest based on the content of the coupon or stamp card. For example, the generation unit generates an attractive catchphrase based on the coupon discount rate or the benefits of the stamp card. The generation unit can also analyze the user's past usage history and generate titles and descriptions using the most effective expressions. Furthermore, the generation unit can generate titles and descriptions that match the season or event. In this way, by generating attractive titles and descriptions based on the content of the coupon or stamp card, it is possible to provide attractive information to customers.

[0111] The pop generation unit can estimate the user's emotions and adjust the design of the in-store pop based on the estimated user's emotions. For example, if the user is relaxed, a pop with soft colors and a simple design can be generated. Alternatively, if the user is excited, a pop with vivid colors and a dynamic design can be generated. Furthermore, if the user is feeling stressed, a pop with calm colors and a simple design can be generated. In this way, by adjusting the design of the in-store pop based on the user's emotions, it is possible to provide in-store pop that is appealing to the user.

[0112] The reception unit can input optimal coupon and stamp card information taking into account the user's geographical location information. For example, if the user is in a specific area, coupon information related to that area is input preferentially. Also, if the user is near a specific store, coupon information for that store can be automatically input. Furthermore, it is also possible to suggest the most relevant coupon information based on the user's location information. In this way, highly relevant information can be provided by taking into account the user's geographical location information.

[0113] The generation unit can estimate the user's emotions and adjust the tone of the generated titles and descriptions based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate titles and descriptions with a calm tone. If the user is excited, the generation unit can generate titles and descriptions with an energetic tone. Furthermore, if the user is stressed, the generation unit can generate titles and descriptions with a reassuring tone. In this way, by adjusting the tone of the titles and descriptions based on the user's emotions, it is possible to provide information that is appealing to the user.

[0114] The pop generation unit can adjust the content of in-store pop-ups based on the inventory status of products offered by the store. For example, for products with abundant stock, it generates pop-ups that actively promote them. For products with low stock, it can also generate pop-ups that emphasize a sense of exclusivity. Furthermore, for products that are out of stock, it can also generate pop-ups that include suggestions for alternative products. This allows for effective promotions by adjusting the content of in-store pop-ups based on the inventory status of the store.

[0115] The reception unit can analyze the user's social media activity and input information about related coupons and stamp cards. For example, it prioritizes inputting coupon information related to places where the user has checked in on social media. It can also analyze the content of the user's social media posts and suggest related coupon information. It can also suggest related coupon information based on the activity of the user's friends on social media. In this way, highly relevant information can be provided by analyzing the user's social media activity.

[0116] The generation unit can estimate the user's emotions and adjust the format of the generated title and description based on the estimated user's emotions. For example, if the user is relaxed, the generation unit can generate titles and descriptions in an easy-to-read format. If the user is in a hurry, the generation unit can generate titles and descriptions in a concise and to-the-point format. Furthermore, if the user is excited, the generation unit can generate titles and descriptions in a visually appealing format. In this way, by adjusting the format of titles and descriptions based on the user's emotions, it is possible to provide appropriate information to the user.

[0117] The pop generation unit can customize the design of in-store pops according to the type of service provided by the store. For example, in the case of a restaurant, it can generate a pop with an appetizing design. In addition, in the case of a retail store, it can also generate a pop with a design that highlights the appeal of the product. Furthermore, in the case of the service industry, it can also generate a pop with a design that emphasizes the features of the service. In this way, by customizing the design of in-store pops according to the type of service provided by the store, effective promotions can be carried out.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit inputs coupon or stamp card information. Detailed information such as the coupon discount rate and the benefits of the stamp card can be input. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using methods such as text analysis and image analysis to analyze the content of the coupon and extract information to generate an appropriate title and description. Step 3: The generator generates a title and description based on the information analyzed by the analyzer. Using the generation AI, it generates an attractive title and description based on the content of the coupon or stamp card. Step 4: The pop-up generation unit analyzes the photo provided by the store and combines detailed information from the coupon and stamp card to generate an in-store pop-up. Image analysis technology is used to combine the provided photo with information from the coupon and stamp card to generate an attractive in-store pop-up.

[0120] 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.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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."

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] [Explanation of symbols]

[0192] 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 reception section for inputting coupon or stamp card information; an analysis unit that analyzes the information input by the reception unit; a generation unit that generates a title or description based on the information analyzed by the analysis unit; A pop generation unit that analyzes the photos provided by the store and generates in-store pops. A system characterized by:

2. The generation unit Generate compelling titles or descriptions based on the contents of a coupon or stamp card 2. The system of claim 1.

3. The pop generation unit Analyzes photos provided by stores and combines them with coupon and stamp card details to generate in-store pop-ups 2. The system of claim 1.

4. The reception unit Enter details such as coupon details or expiration date, stamp card benefits, etc.

2. The system of claim 1.

5. The generation unit Provide generated titles and descriptions to customers 2. The system of claim 1.

6. The pop generation unit Display the generated in-store pop 2. The system of claim 1.

7. The reception unit Estimates user emotions and adjusts the timing of coupon and stamp card information input based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze past coupon and stamp card usage history and select the optimal information entry method 2. The system of claim 1.

Images