System

Abstract

To provide a system that efficiently generates an advertisement content and cooperates with media.SOLUTION: A system according to an embodiment comprises a receiving part, a generation part, and a cooperation part. The receiving part receives an input from a user. The generation part generates an advertisement content on the basis of information received by the receiving part. The cooperation part cooperates the advertisement generated by the generation part with media.SELECTED DRAWING: Figure 1

Description

[Technical field]

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

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

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] With conventional technology, generating advertising content and linking it to media required time and effort, making it difficult to carry out the process efficiently.

[0005] The system according to the embodiment aims to efficiently generate advertising content and link it to media. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a linking unit. The reception unit receives an input from a user. The generation unit generates advertising content based on the information received by the reception unit. The linking unit links the advertisement generated by the generation unit to a medium. Effect of the Invention

[0007] The system according to the embodiment can efficiently generate advertising content and link it to media. [Brief description of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Diagram 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. FIG. [Diagram 3] FIG. 11 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Diagram 5] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 13 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 13 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automatic advertisement generation system according to the embodiment of the present invention is a system in which a user inputs the characteristics and budget of a product to be advertised in text using a messaging tool and transmits a product image, and a generation AI automatically generates content such as an advertisement video and image, thereby performing effective marketing within a budget. In the automatic advertisement generation system, a user inputs the characteristics and budget of a product to be advertised in text through a messaging tool and transmits a product image, and a generation AI acquires information for generating an advertisement. Next, the generation AI analyzes the input information and automatically generates content such as an advertisement video and image. The generation AI generates optimal advertisement content based on the product characteristics and budget. The generated advertisement is optimized for effective marketing within a budget. The generation AI adjusts the advertisement delivery destination and delivery frequency according to the budget to maximize the effect. Furthermore, the generated advertisement is linked so that it can be easily posted on media of group companies such as messaging tools and e-commerce sites. A user can easily post the generated advertisement on media, and perform effective marketing. For example, the generated video advertisement can be posted on the timeline of a messaging tool or displayed as a banner advertisement on an e-commerce site. This mechanism allows users to easily generate ads and conduct effective marketing. By utilizing generation AI, the ad generation process is automated, significantly reducing time and effort. In addition, marketing efficiency is improved by generating ads that are as effective as possible within the budget. This allows the automatic ad generation system to allow users to easily generate ads and conduct effective marketing.

[0029] The automatic advertisement generation system according to the embodiment includes a reception unit, a generation unit, and a linking unit. The reception unit receives input from a user. The input from the user includes, for example, text input, voice input, image input, and the like, but is not limited to such examples. The reception unit acquires information for the generation AI to generate an advertisement, for example, by the user inputting the characteristics and budget of a product to be advertised in text through a messaging tool and transmitting a product image. The generation unit uses the generation AI to generate advertisement content based on the information received by the reception unit. The generation AI generates advertisement content using, for example, a deep learning model or a natural language generation model. The generation unit generates optimal advertisement content based on, for example, the characteristics and budget of the product. The generation unit uses the generation AI to adjust the delivery destination and delivery frequency of the advertisement to maximize its effect. The linking unit links the advertisement generated by the generation unit to a media. The media includes, for example, social media, websites, mobile apps, and the like, but is not limited to such examples. The linking unit links the generated advertisement to a media such as a messaging tool or an e-commerce site so that it can be easily posted. As a result, the automatic advertisement generation system according to the embodiment allows the user to easily generate advertisements and perform effective marketing. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input advertisement content generated based on product features and a budget to the generation AI, and cause the generation AI to generate the advertisement content. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the linking unit can link advertisements using an AI model for linking the generated advertisements to media.

[0030] The reception unit receives input from a user. Examples of input from a user include, but are not limited to, text input, voice input, and image input. Specifically, a user inputs the features and budget of a product to be advertised in text through a messaging tool and transmits a product image, thereby obtaining information for the generation AI to generate an advertisement. The reception unit utilizes natural language processing technology and image recognition technology to efficiently process these inputs. For example, in the case of text input, natural language processing technology is used to analyze the user's intention and extract necessary information. In the case of voice input, voice recognition technology is used to convert the voice into text and similarly analyze it. In the case of image input, image recognition technology is used to automatically extract product features and provide them to the generation AI. This allows the reception unit to handle a variety of input formats and enables the user to easily provide information for advertisement generation. In addition, the reception unit can provide feedback on the user's input content in real time and prompt the user to input additional information as necessary. For example, if the information entered by the user is insufficient, the reception unit automatically detects the missing information and sends a notification to the user requesting additional input. This allows the reception unit to reliably collect information necessary for the generation AI to generate high-quality advertising content. Furthermore, the reception unit safely manages the user's input data and takes appropriate security measures to protect privacy. For example, the input data is encrypted and access control is strictly managed. This allows the user to provide information with peace of mind.

[0031] The generation unit uses the generation AI to generate advertising content based on the information accepted by the acceptance unit. The generation AI generates advertising content using, for example, a deep learning model or a natural language generation model. Specifically, the generation AI generates optimal advertising content based on the features and budget of the product provided by the user. For example, a deep learning model is used to extract features from an image of the product, and a natural language generation model is used to generate a catchy copy and description that enhances the appeal of the product. The generation unit also uses the generation AI to adjust the delivery destination and delivery frequency of the advertisement to maximize its effectiveness. For example, past advertising data and user behavior data are analyzed to identify the optimal delivery timing and target audience. This allows the generation unit to generate advertising content optimized to the needs of the user and support effective marketing. Furthermore, the generation unit evaluates the quality of the generated advertising content and makes corrections and improvements as necessary. For example, a process can be introduced in which human experts review the advertising content generated by the generation AI and confirm the quality. This allows the generation unit to provide high-quality advertising content and improve user satisfaction. In addition, the generation unit continuously updates the learning data of the generation AI to improve the accuracy and effectiveness of advertisement generation. For example, by adding data reflecting new product information and market trends and retraining the generation AI model, it is possible to always generate advertisement content based on the latest information. This allows the generation unit to continuously improve the performance of the automatic advertisement generation system.

[0032] The linking unit links the advertisement generated by the generation unit to a media. Examples of the media include, but are not limited to, social media, websites, and mobile apps. Specifically, the linking unit links the generated advertisement to media such as messaging tools and e-commerce sites so that the advertisement can be easily posted. For example, in the case of social media, the linking unit automatically posts advertisement content and effectively distributes it to a target audience. In addition, in the case of a website, the linking unit displays it as an advertisement banner or a pop-up advertisement to attract the user's attention. In addition, in the case of a mobile app, the linking unit displays it as an in-app advertisement and provides personalized advertisements based on the user's behavior data. In this way, the linking unit can effectively distribute the generated advertisement to various media and support the user's marketing activities. In addition, the linking unit can monitor the delivery results of the advertisement in real time and evaluate the effectiveness. For example, the click-through rate and conversion rate of the advertisement are analyzed to quantitatively evaluate the effectiveness of the advertisement. In this way, the linking unit can provide feedback to the generation unit to maximize the effectiveness of the advertisement and support the improvement of the advertisement content. Furthermore, the collaboration unit can flexibly adjust the advertisement delivery schedule and deliver advertisements at optimal timing. For example, by delivering advertisements in accordance with specific events or campaigns, effective marketing can be achieved. In this way, the collaboration unit can support flexible advertisement delivery in accordance with the user's marketing strategy and maximize the effectiveness of the automatic advertisement generation system.

[0033] The generation unit can generate advertising content by the generation AI. The generation unit generates advertising content using, for example, the generation AI. The generation AI generates advertising content using, for example, a deep learning model or a natural language generation model. The generation AI generates optimal advertising content based on product features and budget. For example, the generation AI generates video advertisements that emphasize product features and banner advertisements that use product images. The generation AI adjusts the delivery destination and delivery frequency of advertisements to maximize their effectiveness. For example, when the budget is limited, the generation AI realizes effective marketing by delivering advertisements to a specific target demographic. As a result, the generation of advertising content is automated by using the generation AI. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs advertising content generated based on product features and budget to the generation AI, and causes the generation AI to generate advertising content.

[0034] The generation unit can generate appropriate advertising content based on the product features and budget. The generation unit generates advertising content based on, for example, the product features and budget. The generation unit uses the generation AI to generate optimal advertising content based on the product features and budget. For example, the generation AI generates video advertisements that emphasize the product features and banner advertisements that use product images. The generation AI adjusts the delivery destination and delivery frequency of advertisements based on the product features and budget to maximize the effect. For example, when the budget is limited, the generation AI realizes effective marketing by delivering advertisements to a specific target group. As a result, effective marketing is possible by generating optimal advertising content based on the product features and budget. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the advertising content generated based on the product features and budget to the generation AI, and causes the generation AI to generate the advertising content.

[0035] The linking unit can link the generated advertisement to at least one of the media of a messaging tool or an e-commerce site. For example, the linking unit links the generated advertisement to a media such as a messaging tool or an e-commerce site. The media includes, but is not limited to, for example, social media, a website, a mobile app, and the like. The linking unit links the generated advertisement to a media such as a messaging tool or an e-commerce site so that the generated advertisement can be easily posted. For example, the generated video advertisement can be posted on the timeline of the messaging tool or displayed as a banner advertisement on the e-commerce site. This enables effective delivery of the advertisement by linking the generated advertisement to the media. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can link the advertisement using an AI model for linking the generated advertisement to the media.

[0036] The generation unit can adjust the advertisement by the generation AI. The generation unit adjusts the advertisement by using, for example, the generation AI. The generation AI adjusts, for example, the content, design, and delivery timing of the advertisement. The generation AI adjusts the delivery destination and delivery frequency of the advertisement based on the product features and budget to maximize the effect. For example, when the budget is limited, the generation AI realizes effective marketing by delivering advertisements to a specific target group. In this way, the optimization of advertisements is automated by using the generation AI. A part or all of the above-mentioned processing in the generation unit may be performed by using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the advertisement content generated based on the product features and budget into the generation AI, and causes the generation AI to generate the advertisement content.

[0037] The generation unit can adjust the destination and frequency of advertisement delivery by the generation AI. The generation unit adjusts the destination and frequency of advertisement delivery by using the generation AI, for example. The generation AI adjusts the destination and frequency of advertisement delivery to maximize the effect, for example. The generation AI adjusts the destination and frequency of advertisement delivery based on the characteristics and budget of the product. For example, when the budget is limited, the generation AI realizes effective marketing by delivering advertisements to a specific target demographic. As a result, the adjustment of the destination and frequency of advertisement delivery is automated by using the generation AI. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the advertisement content generated based on the characteristics and budget of the product into the generation AI, and causes the generation AI to generate the advertisement content.

[0038] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an optimal input method. The reception unit, for example, automatically displays product features and budgets frequently input by the user in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest product features and budgets to be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. A part 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 the user's past input history data to the generation AI and cause the generation AI to execute a suggestion of an optimal input method.

[0039] The reception unit can filter the input contents based on the user's current project and field of interest at the time of input. The reception unit, for example, filters the input contents based on the user's current project and field of interest at the time of input. The reception unit, for example, preferentially displays product features and budgets related to a project currently being carried out by the user. The reception unit can also suggest related product features and budgets based on the user's field of interest. Furthermore, the reception unit can analyze the user's past project history and filter related input contents. As a result, highly relevant information can be provided by filtering the input contents based on the user's current project and field of interest. A part 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 the user's project data to the generation AI and cause the generation AI to filter the related input contents.

[0040] The reception unit can preferentially accept highly relevant input contents in consideration of the geographical location information of the user at the time of input. For example, the reception unit preferentially accepts highly relevant input contents in consideration of the geographical location information of the user at the time of input. For example, when the user is in a specific area, the reception unit preferentially displays product features and budgets related to the area. In addition, the reception unit can also suggest related product features and budgets based on the user's current location. Furthermore, the reception unit can analyze the user's past location information and preferentially accept related input contents. In this way, highly relevant information can be provided by considering the user's geographical location information. A part 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 the user's location information data to the generation AI and cause the generation AI to execute the suggestion of related input contents.

[0041] The reception unit can analyze the user's social media activity at the time of input and suggest related input contents. The reception unit can, for example, analyze the user's social media activity at the time of input and suggest related input contents. The reception unit can, for example, suggest features and budgets related to products shared by the user on social media. The reception unit can also predict and suggest features and budgets of products of interest from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity history and suggest related input contents. In this way, related information can be provided by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of related input contents.

[0042] The generation unit can adjust the level of detail of the generation based on the importance of the product when generating the advertisement content. For example, the generation unit adjusts the level of detail of the generation based on the importance of the product when generating the advertisement content. For example, in the case of an important product, the generation unit generates advertisement content including a detailed description. In addition, in the case of a product with low importance, the generation unit can also generate concise advertisement content. Furthermore, the generation unit can adjust the length and level of detail of the advertisement content according to the importance of the product. As a result, appropriate advertisement content can be generated by adjusting the level of detail of the generation based on the importance of the product. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input product importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0043] The generation unit can apply different generation algorithms depending on the product category when generating the advertisement content. For example, the generation unit applies different generation algorithms depending on the product category when generating the advertisement content. For example, in the case of electronic products, the generation unit generates advertisement content that emphasizes technical features. In addition, in the case of fashion products, the generation unit can also generate advertisement content that emphasizes visuals. Furthermore, in the case of food products, the generation unit can also generate advertisement content that emphasizes taste and quality. In this way, effective advertisement content can be generated by applying different generation algorithms depending on the product category. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input product category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0044] The generation unit can determine the priority of generation based on the submission time of the product when generating the advertisement content. The generation unit determines the priority of generation based on the submission time of the product when generating the advertisement content, for example. For example, in the case of an urgent product, the generation unit generates advertisement content with the highest priority. In addition, in the case of a product whose submission time is far away, the generation unit can generate advertisement content later. Furthermore, the generation unit can adjust the generation order of advertisement content according to the submission time. In this way, by determining the priority of generation based on the submission time of the product, advertisement content can be generated at an appropriate time. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time data of the product to the generation AI and cause the generation AI to execute the determination of the priority of generation.

[0045] The generation unit can adjust the order of generation based on the relevance of the products when generating the advertisement content. For example, the generation unit adjusts the order of generation based on the relevance of the products when generating the advertisement content. For example, in the case of a highly relevant product, the generation unit generates advertisement content with the highest priority. In addition, in the case of a lowly relevant product, the generation unit can generate advertisement content later. Furthermore, the generation unit can adjust the order of generation of advertisement content according to the relevance of the products. As a result, by adjusting the order of generation based on the relevance of the products, effective advertisement content can be generated. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input product relevance data to the generation AI and cause the generation AI to adjust the order of generation.

[0046] The linking unit can select an appropriate linking method by referring to the user's past linking history at the time of advertisement linking. For example, the linking unit selects an appropriate linking method by referring to the user's past linking history at the time of advertisement linking. For example, the linking unit preferentially proposes a linking method that the user has used in the past. The linking unit can also select the most effective linking method from the user's past linking history. Furthermore, the linking unit can analyze the user's past linking history and propose an optimal linking method. In this way, the optimal linking method can be selected by referring to the user's past linking history. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the user's linking history data to the generation AI and cause the generation AI to select the optimal linking method.

[0047] The collaboration unit can customize the collaboration content based on the user's current marketing strategy at the time of advertising collaboration. For example, the collaboration unit customizes the collaboration content based on the user's current marketing strategy at the time of advertising collaboration. For example, the collaboration unit proposes an optimal advertising collaboration method based on the user's current marketing strategy. The collaboration unit can also customize the content of advertising collaboration according to the user's marketing strategy. Furthermore, the collaboration unit can analyze the user's marketing strategy and select an optimal advertising collaboration method. This enables effective advertising delivery by customizing the collaboration content based on the user's current marketing strategy. A part or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's marketing strategy data to the generation AI and cause the generation AI to execute customization of the collaboration content.

[0048] The linking unit can select an appropriate linking method in consideration of the geographical location information of the user at the time of advertisement linking. For example, the linking unit selects an appropriate linking method in consideration of the geographical location information of the user at the time of advertisement linking. For example, when the user is in a specific area, the linking unit preferentially links advertisements related to the area. The linking unit can also propose an optimal advertisement linking method based on the user's current location. Furthermore, the linking unit can analyze the user's past location information and select an optimal advertisement linking method. In this way, the optimal advertisement linking method can be selected by considering the user's geographical location information. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the user's location information data to the generation AI and cause the generation AI to select the optimal linking method.

[0049] The linking unit can analyze the user's social media activity and propose a means of linking at the time of advertising linking. For example, the linking unit analyzes the user's social media activity and proposes a means of linking at the time of advertising linking. The linking unit proposes, for example, a linking method related to an advertisement shared by the user on social media. The linking unit can also select an optimal advertising linking method from the user's social media activity. Furthermore, the linking unit can analyze the user's social media activity history and propose an optimal advertising linking method. In this way, the optimal advertising linking method can be proposed by analyzing the user's social media activity. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the user's social media data to the generation AI and cause the generation AI to execute a proposal of an optimal linking method.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The reception unit can analyze the user's input contents in real time and provide instant feedback based on the input contents. For example, while the user is inputting the features of a product, the reception unit can display additional information and suggestions related to the features. In addition, when the user inputs a budget, the reception unit can instantly present an optimal advertising plan within the budget. Furthermore, when the user submits a product image, the reception unit can analyze the image and provide advice on the quality of the image and appropriate usage. This allows the user to receive instant feedback on the input contents, enabling more effective advertisement generation.

[0052] In the process of generating the advertising content, the generation unit can refer to the user's past advertising generation history and generate an optimal advertisement based on past success cases. For example, by incorporating elements of advertisements that have been highly effective in the past, similar success can be reproduced. In addition, it is also possible to extract advertising methods that are effective for a specific target demographic from the past advertising generation history and reflect them in new advertisements. Furthermore, by analyzing the past advertising generation history, it is also possible to understand the preferences and tendencies of the user and generate advertisements that are customized based on them. In this way, more effective advertising content can be generated by utilizing past success cases.

[0053] When generating the advertisement content, the generation unit can adjust the advertisement taking into consideration the progress of the user's current marketing campaign. For example, an advertisement aimed at increasing awareness is generated in the early stage of the campaign, and an advertisement providing detailed product information is generated in the middle stage. Also, an advertisement including benefits and discount information for promoting purchases can be generated in the final stage of the campaign. Furthermore, it is possible to adjust the frequency and destination of advertisement delivery according to the progress of the campaign. In this way, an optimal advertisement according to the progress of the marketing campaign can be generated.

[0054] When delivering the generated advertisement, the collaboration unit can optimize the timing of delivery by utilizing the behavioral data of the user's target audience. For example, the visibility of the advertisement can be improved by delivering the advertisement during the time period when the target audience is most active. In addition, the collaboration unit can analyze the past behavioral data of the target audience and deliver advertisements that are effective on specific days of the week or times. Furthermore, it is also possible to adjust the frequency of advertisement delivery based on the behavioral patterns of the target audience. In this way, the effectiveness of the advertisement can be maximized by utilizing the behavioral data of the target audience.

[0055] When generating advertisement content, the generation unit can adjust the design and message of the advertisement in consideration of the user's brand image. For example, if the user's brand emphasizes luxury, the generation unit can generate an advertisement incorporating high-quality visuals and sophisticated designs. In addition, if the user's brand emphasizes familiarity, the generation unit can also generate an advertisement including a casual and friendly message. Furthermore, it is also possible to maintain a consistent brand image by reflecting the user's brand color and logo in the advertisement. This makes it possible to generate advertisement content that matches the user's brand image.

[0056] The process flow of the first embodiment will be briefly described below.

[0057] Step 1: The reception unit receives input from the user. User input includes text input, voice input, image input, etc. For example, a user can enter the features and budget of the product they want to advertise in text form through a messaging tool and send an image of the product, and the generation AI will obtain the information needed to generate the advertisement. Step 2: The generation unit uses a generation AI to generate advertising content based on the information received by the reception unit. The generation AI generates advertising content using a deep learning model, a natural language generation model, etc. The generation unit generates optimal advertising content based on the product features and budget, and adjusts the ad delivery destination and delivery frequency. Step 3: The linking unit links the advertisements generated by the generation unit to media. Media include social media, websites, mobile apps, etc. The linking unit links the generated advertisements to media such as messaging tools and e-commerce sites so that they can be easily published.

[0058] (Example 2) The automatic advertisement generation system according to the embodiment of the present invention is a system in which a user inputs the characteristics and budget of a product to be advertised in text using a messaging tool and transmits a product image, and a generation AI automatically generates content such as an advertisement video and image, thereby performing effective marketing within a budget. In the automatic advertisement generation system, a user inputs the characteristics and budget of a product to be advertised in text through a messaging tool and transmits a product image, and a generation AI acquires information for generating an advertisement. Next, the generation AI analyzes the input information and automatically generates content such as an advertisement video and image. The generation AI generates optimal advertisement content based on the product characteristics and budget. The generated advertisement is optimized for effective marketing within a budget. The generation AI adjusts the advertisement delivery destination and delivery frequency according to the budget to maximize the effect. Furthermore, the generated advertisement is linked so that it can be easily posted on media of group companies such as messaging tools and e-commerce sites. A user can easily post the generated advertisement on media, and perform effective marketing. For example, the generated video advertisement can be posted on the timeline of a messaging tool or displayed as a banner advertisement on an e-commerce site. This mechanism allows users to easily generate ads and conduct effective marketing. By utilizing generation AI, the ad generation process is automated, significantly reducing time and effort. In addition, marketing efficiency is improved by generating ads that are as effective as possible within the budget. This allows the automatic ad generation system to allow users to easily generate ads and conduct effective marketing.

[0059] The automatic advertisement generation system according to the embodiment includes a reception unit, a generation unit, and a linking unit. The reception unit receives input from a user. The input from the user includes, for example, text input, voice input, image input, and the like, but is not limited to such examples. The reception unit acquires information for the generation AI to generate an advertisement, for example, by the user inputting the characteristics and budget of a product to be advertised in text through a messaging tool and transmitting a product image. The generation unit uses the generation AI to generate advertisement content based on the information received by the reception unit. The generation AI generates advertisement content using, for example, a deep learning model or a natural language generation model. The generation unit generates optimal advertisement content based on, for example, the characteristics and budget of the product. The generation unit uses the generation AI to adjust the delivery destination and delivery frequency of the advertisement to maximize its effect. The linking unit links the advertisement generated by the generation unit to a media. The media includes, for example, social media, websites, mobile apps, and the like, but is not limited to such examples. The linking unit links the generated advertisement to a media such as a messaging tool or an e-commerce site so that it can be easily posted. As a result, the automatic advertisement generation system according to the embodiment allows the user to easily generate advertisements and perform effective marketing. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can input advertisement content generated based on product features and a budget to the generation AI, and cause the generation AI to generate the advertisement content. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the linking unit can link advertisements using an AI model for linking the generated advertisements to media.

[0060] The reception unit receives input from a user. Examples of input from a user include, but are not limited to, text input, voice input, and image input. Specifically, a user inputs the features and budget of a product to be advertised in text through a messaging tool and transmits a product image, thereby obtaining information for the generation AI to generate an advertisement. The reception unit utilizes natural language processing technology and image recognition technology to efficiently process these inputs. For example, in the case of text input, natural language processing technology is used to analyze the user's intention and extract necessary information. In the case of voice input, voice recognition technology is used to convert the voice into text and similarly analyze it. In the case of image input, image recognition technology is used to automatically extract product features and provide them to the generation AI. This allows the reception unit to handle a variety of input formats and enables the user to easily provide information for advertisement generation. In addition, the reception unit can provide feedback on the user's input content in real time and prompt the user to input additional information as necessary. For example, if the information entered by the user is insufficient, the reception unit automatically detects the missing information and sends a notification to the user requesting additional input. This allows the reception unit to reliably collect information necessary for the generation AI to generate high-quality advertising content. Furthermore, the reception unit safely manages the user's input data and takes appropriate security measures to protect privacy. For example, the input data is encrypted and access control is strictly managed. This allows the user to provide information with peace of mind.

[0061] The generation unit uses the generation AI to generate advertising content based on the information accepted by the acceptance unit. The generation AI generates advertising content using, for example, a deep learning model or a natural language generation model. Specifically, the generation AI generates optimal advertising content based on the features and budget of the product provided by the user. For example, a deep learning model is used to extract features from an image of the product, and a natural language generation model is used to generate a catchy copy and description that enhances the appeal of the product. The generation unit also uses the generation AI to adjust the delivery destination and delivery frequency of the advertisement to maximize its effectiveness. For example, past advertising data and user behavior data are analyzed to identify the optimal delivery timing and target audience. This allows the generation unit to generate advertising content optimized to the needs of the user and support effective marketing. Furthermore, the generation unit evaluates the quality of the generated advertising content and makes corrections and improvements as necessary. For example, a process can be introduced in which human experts review the advertising content generated by the generation AI and confirm the quality. This allows the generation unit to provide high-quality advertising content and improve user satisfaction. In addition, the generation unit continuously updates the learning data of the generation AI to improve the accuracy and effectiveness of advertisement generation. For example, by adding data reflecting new product information and market trends and retraining the generation AI model, it is possible to always generate advertisement content based on the latest information. This allows the generation unit to continuously improve the performance of the automatic advertisement generation system.

[0062] The linking unit links the advertisement generated by the generation unit to a media. Examples of the media include, but are not limited to, social media, websites, and mobile apps. Specifically, the linking unit links the generated advertisement to media such as messaging tools and e-commerce sites so that the advertisement can be easily posted. For example, in the case of social media, the linking unit automatically posts advertisement content and effectively distributes it to a target audience. In addition, in the case of a website, the linking unit displays it as an advertisement banner or a pop-up advertisement to attract the user's attention. In addition, in the case of a mobile app, the linking unit displays it as an in-app advertisement and provides personalized advertisements based on the user's behavior data. In this way, the linking unit can effectively distribute the generated advertisement to various media and support the user's marketing activities. In addition, the linking unit can monitor the delivery results of the advertisement in real time and evaluate the effectiveness. For example, the click-through rate and conversion rate of the advertisement are analyzed to quantitatively evaluate the effectiveness of the advertisement. In this way, the linking unit can provide feedback to the generation unit to maximize the effectiveness of the advertisement and support the improvement of the advertisement content. Furthermore, the collaboration unit can flexibly adjust the advertisement delivery schedule and deliver advertisements at optimal timing. For example, by delivering advertisements in accordance with specific events or campaigns, effective marketing can be achieved. In this way, the collaboration unit can support flexible advertisement delivery in accordance with the user's marketing strategy and maximize the effectiveness of the automatic advertisement generation system.

[0063] The generation unit can generate advertising content by the generation AI. The generation unit generates advertising content using, for example, the generation AI. The generation AI generates advertising content using, for example, a deep learning model or a natural language generation model. The generation AI generates optimal advertising content based on product features and budget. For example, the generation AI generates video advertisements that emphasize product features and banner advertisements that use product images. The generation AI adjusts the delivery destination and delivery frequency of advertisements to maximize their effectiveness. For example, when the budget is limited, the generation AI realizes effective marketing by delivering advertisements to a specific target demographic. As a result, the generation of advertising content is automated by using the generation AI. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs advertising content generated based on product features and budget to the generation AI, and causes the generation AI to generate advertising content.

[0064] The generation unit can generate appropriate advertising content based on the product features and budget. The generation unit generates advertising content based on, for example, the product features and budget. The generation unit uses the generation AI to generate optimal advertising content based on the product features and budget. For example, the generation AI generates video advertisements that emphasize the product features and banner advertisements that use product images. The generation AI adjusts the delivery destination and delivery frequency of advertisements based on the product features and budget to maximize the effect. For example, when the budget is limited, the generation AI realizes effective marketing by delivering advertisements to a specific target group. As a result, effective marketing is possible by generating optimal advertising content based on the product features and budget. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the advertising content generated based on the product features and budget to the generation AI, and causes the generation AI to generate the advertising content.

[0065] The linking unit can link the generated advertisement to at least one of the media of a messaging tool or an e-commerce site. For example, the linking unit links the generated advertisement to a media such as a messaging tool or an e-commerce site. The media includes, but is not limited to, for example, social media, a website, a mobile app, and the like. The linking unit links the generated advertisement to a media such as a messaging tool or an e-commerce site so that the generated advertisement can be easily posted. For example, the generated video advertisement can be posted on the timeline of the messaging tool or displayed as a banner advertisement on the e-commerce site. This enables effective delivery of the advertisement by linking the generated advertisement to the media. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can link the advertisement using an AI model for linking the generated advertisement to the media.

[0066] The generation unit can adjust the advertisement by the generation AI. The generation unit adjusts the advertisement by using, for example, the generation AI. The generation AI adjusts, for example, the content, design, and delivery timing of the advertisement. The generation AI adjusts the delivery destination and delivery frequency of the advertisement based on the product features and budget to maximize the effect. For example, when the budget is limited, the generation AI realizes effective marketing by delivering advertisements to a specific target group. In this way, the optimization of advertisements is automated by using the generation AI. A part or all of the above-mentioned processing in the generation unit may be performed by using, for example, the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the advertisement content generated based on the product features and budget into the generation AI, and causes the generation AI to generate the advertisement content.

[0067] The generation unit can adjust the destination and frequency of advertisement delivery by the generation AI. The generation unit adjusts the destination and frequency of advertisement delivery by using the generation AI, for example. The generation AI adjusts the destination and frequency of advertisement delivery to maximize the effect, for example. The generation AI adjusts the destination and frequency of advertisement delivery based on the characteristics and budget of the product. For example, when the budget is limited, the generation AI realizes effective marketing by delivering advertisements to a specific target demographic. As a result, the adjustment of the destination and frequency of advertisement delivery is automated by using the generation AI. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the generation unit inputs the advertisement content generated based on the characteristics and budget of the product into the generation AI, and causes the generation AI to generate the advertisement content.

[0068] The reception unit can estimate the user's emotion and adjust the display method of the input interface based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and adjusts the display method of the input interface based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, the reception unit provides a simple interface and minimizes the input procedure. Also, when the user is relaxed, the reception unit provides detailed input options and suggests a customizable input method. Furthermore, when the user is in a hurry, the reception unit prioritizes voice input and enables the user to quickly input product features and a budget. This improves user convenience by adjusting the display method of the input interface according to the user's emotion. A part or all of the above-mentioned 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 to the generation AI and cause the generation AI to execute emotion estimation.

[0069] The reception unit can analyze the user's past input history and suggest an appropriate input method. The reception unit, for example, analyzes the user's past input history and suggests an optimal input method. The reception unit, for example, automatically displays product features and budgets frequently input by the user in the past as candidates. The reception unit can also preferentially suggest input methods (such as voice and text) that the user has used in the past. Furthermore, the reception unit can predict and suggest product features and budgets to be used in a specific time period from the user's past input history. In this way, the optimal input method can be suggested by analyzing the user's past input history. A part 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 the user's past input history data to the generation AI and cause the generation AI to execute a suggestion of an optimal input method.

[0070] The reception unit can filter the input contents based on the user's current project and field of interest at the time of input. The reception unit, for example, filters the input contents based on the user's current project and field of interest at the time of input. The reception unit, for example, preferentially displays product features and budgets related to a project currently being carried out by the user. The reception unit can also suggest related product features and budgets based on the user's field of interest. Furthermore, the reception unit can analyze the user's past project history and filter related input contents. As a result, highly relevant information can be provided by filtering the input contents based on the user's current project and field of interest. A part 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 the user's project data to the generation AI and cause the generation AI to filter the related input contents.

[0071] The reception unit can estimate the user's emotion and determine the priority of the input contents based on the estimated user's emotion. The reception unit, for example, estimates the user's emotion and determines the priority of the input contents based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is stressed, the reception unit preferentially displays important input items to enable quick input. In addition, when the user is relaxed, the reception unit provides detailed input items and suggests a customizable input method. Furthermore, when the user is in a hurry, the reception unit preferentially displays the most important input items to enable quick input. In this way, important information can be quickly input by determining the priority of the input contents according to the user's emotion. A part or all of the above-mentioned processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0072] The reception unit can preferentially accept highly relevant input contents in consideration of the geographical location information of the user at the time of input. For example, the reception unit preferentially accepts highly relevant input contents in consideration of the geographical location information of the user at the time of input. For example, when the user is in a specific area, the reception unit preferentially displays product features and budgets related to the area. In addition, the reception unit can also suggest related product features and budgets based on the user's current location. Furthermore, the reception unit can analyze the user's past location information and preferentially accept related input contents. In this way, highly relevant information can be provided by considering the user's geographical location information. A part 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 the user's location information data to the generation AI and cause the generation AI to execute the suggestion of related input contents.

[0073] The reception unit can analyze the user's social media activity at the time of input and suggest related input contents. The reception unit can, for example, analyze the user's social media activity at the time of input and suggest related input contents. The reception unit can, for example, suggest features and budgets related to products shared by the user on social media. The reception unit can also predict and suggest features and budgets of products of interest from the user's social media activity. Furthermore, the reception unit can analyze the user's social media activity history and suggest related input contents. In this way, related information can be provided by analyzing the user's social media activity. Part or all of the above-mentioned processing in the reception unit can be performed, for example, using AI or can be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to execute the suggestion of related input contents.

[0074] The generation unit can estimate the user's emotion and adjust the expression method of the advertisement content to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the expression method of the advertisement content to be generated based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is relaxed, the generation unit generates an advertisement video that proceeds at a leisurely pace. Also, when the user is in a hurry, the generation unit generates an advertisement video that emphasizes the shortest route. Furthermore, when the user is excited, the generation unit generates an advertisement video that adds a visually stimulating effect. In this way, by adjusting the expression method of the advertisement content according to the user's emotion, a more effective advertisement can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0075] The generation unit can adjust the level of detail of the generation based on the importance of the product when generating the advertisement content. For example, the generation unit adjusts the level of detail of the generation based on the importance of the product when generating the advertisement content. For example, in the case of an important product, the generation unit generates advertisement content including a detailed description. In addition, in the case of a product with low importance, the generation unit can also generate concise advertisement content. Furthermore, the generation unit can adjust the length and level of detail of the advertisement content according to the importance of the product. As a result, appropriate advertisement content can be generated by adjusting the level of detail of the generation based on the importance of the product. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI. For example, the generation unit can input product importance data to the generation AI and cause the generation AI to adjust the level of detail of the generation.

[0076] The generation unit can apply different generation algorithms depending on the product category when generating the advertisement content. For example, the generation unit applies different generation algorithms depending on the product category when generating the advertisement content. For example, in the case of electronic products, the generation unit generates advertisement content that emphasizes technical features. In addition, in the case of fashion products, the generation unit can also generate advertisement content that emphasizes visuals. Furthermore, in the case of food products, the generation unit can also generate advertisement content that emphasizes taste and quality. In this way, effective advertisement content can be generated by applying different generation algorithms depending on the product category. Part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input product category data to the generation AI and cause the generation AI to apply the generation algorithm.

[0077] The generation unit can estimate the user's emotion and adjust the length of the advertisement content to be generated based on the estimated user's emotion. The generation unit, for example, estimates the user's emotion and adjusts the length of the advertisement content to be generated based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is in a hurry, the generation unit generates a short advertisement video that is to the point. Also, when the user is relaxed, the generation unit generates a longer advertisement video that includes detailed explanations. Furthermore, when the user is excited, the generation unit generates an advertisement video with a visually stimulating effect added. As a result, by adjusting the length of the advertisement content according to the user's emotion, a more effective advertisement can be generated. A part or all of the above-mentioned processing in the generation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the generation unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0078] The generation unit can determine the priority of generation based on the submission time of the product when generating the advertisement content. The generation unit determines the priority of generation based on the submission time of the product when generating the advertisement content, for example. For example, in the case of an urgent product, the generation unit generates advertisement content with the highest priority. In addition, in the case of a product whose submission time is far away, the generation unit can generate advertisement content later. Furthermore, the generation unit can adjust the generation order of advertisement content according to the submission time. In this way, by determining the priority of generation based on the submission time of the product, advertisement content can be generated at an appropriate time. Part or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the submission time data of the product to the generation AI and cause the generation AI to execute the determination of the priority of generation.

[0079] The generation unit can adjust the order of generation based on the relevance of the products when generating the advertisement content. For example, the generation unit adjusts the order of generation based on the relevance of the products when generating the advertisement content. For example, in the case of a highly relevant product, the generation unit generates advertisement content with the highest priority. In addition, in the case of a lowly relevant product, the generation unit can generate advertisement content later. Furthermore, the generation unit can adjust the order of generation of advertisement content according to the relevance of the products. As a result, by adjusting the order of generation based on the relevance of the products, effective advertisement content can be generated. A part or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI. For example, the generation unit can input product relevance data to the generation AI and cause the generation AI to adjust the order of generation.

[0080] The linking unit can estimate the user's emotion and adjust the advertisement linking method based on the estimated user's emotion. The linking unit, for example, estimates the user's emotion and adjusts the advertisement linking method based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is relaxed, the linking unit links advertisements at a leisurely pace. Also, when the user is in a hurry, the linking unit links advertisements quickly. Furthermore, when the user is excited, the linking unit links advertisements with visually stimulating effects added. This enables more effective advertisement delivery by adjusting the advertisement linking method according to the user's emotion. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the linking unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0081] The linking unit can select an appropriate linking method by referring to the user's past linking history at the time of advertisement linking. For example, the linking unit selects an appropriate linking method by referring to the user's past linking history at the time of advertisement linking. For example, the linking unit preferentially proposes a linking method that the user has used in the past. The linking unit can also select the most effective linking method from the user's past linking history. Furthermore, the linking unit can analyze the user's past linking history and propose an optimal linking method. In this way, the optimal linking method can be selected by referring to the user's past linking history. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the user's linking history data to the generation AI and cause the generation AI to select the optimal linking method.

[0082] The collaboration unit can customize the collaboration content based on the user's current marketing strategy at the time of advertising collaboration. For example, the collaboration unit customizes the collaboration content based on the user's current marketing strategy at the time of advertising collaboration. For example, the collaboration unit proposes an optimal advertising collaboration method based on the user's current marketing strategy. The collaboration unit can also customize the content of advertising collaboration according to the user's marketing strategy. Furthermore, the collaboration unit can analyze the user's marketing strategy and select an optimal advertising collaboration method. This enables effective advertising delivery by customizing the collaboration content based on the user's current marketing strategy. A part or all of the above-mentioned processing in the collaboration unit may be performed using, for example, AI, or may be performed without using AI. For example, the collaboration unit can input the user's marketing strategy data to the generation AI and cause the generation AI to execute customization of the collaboration content.

[0083] The linking unit can estimate the user's emotion and determine the linking priority of advertisements based on the estimated user's emotion. The linking unit, for example, estimates the user's emotion and determines the linking priority of advertisements based on the estimated user's emotion. The emotion estimation is realized by using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (for example, LLM) or a multimodal generation AI, but is not limited to such examples. For example, when the user is in a hurry, the linking unit links advertisements with the highest priority. Also, when the user is relaxed, the linking unit links advertisements at a normal pace. Furthermore, when the user is excited, the linking unit links advertisements with visually stimulating effects added. This enables more effective advertisement delivery by determining the linking priority of advertisements according to the user's emotion. A part or all of the above-mentioned processing in the linking unit may be performed using, for example, AI, or may be performed without using AI. For example, the linking unit can input the user's emotion data to the generation AI and cause the generation AI to execute emotion estimation.

[0084] The linking unit can select an appropriate linking method in consideration of the geographical location information of the user at the time of advertisement linking. For example, the linking unit selects an appropriate linking method in consideration of the geographical location information of the user at the time of advertisement linking. For example, when the user is in a specific area, the linking unit preferentially links advertisements related to the area. The linking unit can also propose an optimal advertisement linking method based on the user's current location. Furthermore, the linking unit can analyze the user's past location information and select an optimal advertisement linking method. In this way, the optimal advertisement linking method can be selected by considering the user's geographical location information. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the user's location information data to the generation AI and cause the generation AI to select the optimal linking method.

[0085] The linking unit can analyze the user's social media activity and propose a means of linking at the time of advertising linking. For example, the linking unit analyzes the user's social media activity and proposes a means of linking at the time of advertising linking. The linking unit proposes, for example, a linking method related to an advertisement shared by the user on social media. The linking unit can also select an optimal advertising linking method from the user's social media activity. Furthermore, the linking unit can analyze the user's social media activity history and propose an optimal advertising linking method. In this way, the optimal advertising linking method can be proposed by analyzing the user's social media activity. A part or all of the above-mentioned processing in the linking unit may be performed, for example, using AI, or may be performed without using AI. For example, the linking unit can input the user's social media data to the generation AI and cause the generation AI to execute a proposal of an optimal linking method.

[0086] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0087] The reception unit can analyze the user's input contents in real time and provide instant feedback based on the input contents. For example, while the user is inputting the features of a product, the reception unit can display additional information and suggestions related to the features. In addition, when the user inputs a budget, the reception unit can instantly present an optimal advertising plan within the budget. Furthermore, when the user submits a product image, the reception unit can analyze the image and provide advice on the quality of the image and appropriate usage. This allows the user to receive instant feedback on the input contents, enabling more effective advertisement generation.

[0088] In the process of generating the advertising content, the generation unit can refer to the user's past advertising generation history and generate an optimal advertisement based on past success cases. For example, by incorporating elements of advertisements that have been highly effective in the past, similar success can be reproduced. In addition, it is also possible to extract advertising methods that are effective for a specific target demographic from the past advertising generation history and reflect them in new advertisements. Furthermore, by analyzing the past advertising generation history, it is also possible to understand the preferences and tendencies of the user and generate advertisements that are customized based on them. In this way, more effective advertising content can be generated by utilizing past success cases.

[0089] When generating the advertisement content, the generation unit can adjust the advertisement taking into consideration the progress of the user's current marketing campaign. For example, an advertisement aimed at increasing awareness is generated in the early stage of the campaign, and an advertisement providing detailed product information is generated in the middle stage. Also, an advertisement including benefits and discount information for promoting purchases can be generated in the final stage of the campaign. Furthermore, it is possible to adjust the frequency and destination of advertisement delivery according to the progress of the campaign. In this way, an optimal advertisement according to the progress of the marketing campaign can be generated.

[0090] When delivering the generated advertisement, the collaboration unit can optimize the timing of delivery by utilizing the behavioral data of the user's target audience. For example, the visibility of the advertisement can be improved by delivering the advertisement during the time period when the target audience is most active. In addition, the collaboration unit can analyze the past behavioral data of the target audience and deliver advertisements that are effective on specific days of the week or times. Furthermore, it is also possible to adjust the frequency of advertisement delivery based on the behavioral patterns of the target audience. In this way, the effectiveness of the advertisement can be maximized by utilizing the behavioral data of the target audience.

[0091] When generating advertisement content, the generation unit can adjust the design and message of the advertisement in consideration of the user's brand image. For example, if the user's brand emphasizes luxury, the generation unit can generate an advertisement incorporating high-quality visuals and sophisticated designs. In addition, if the user's brand emphasizes familiarity, the generation unit can also generate an advertisement including a casual and friendly message. Furthermore, it is also possible to maintain a consistent brand image by reflecting the user's brand color and logo in the advertisement. This makes it possible to generate advertisement content that matches the user's brand image.

[0092] The reception unit can estimate the user's emotions and adjust the confirmation process of the input contents based on the estimated user's emotions. For example, if the user feels anxious, the reception unit can display a detailed confirmation message to confirm the accuracy of the input contents. If the user feels confident, the reception unit can display a concise confirmation message to quickly proceed to the next step. Furthermore, if the user is tired, the reception unit can utilize an auto-completion function to complete the input contents, reducing the burden on the user. In this way, the confirmation process of the input contents can be adjusted according to the user's emotions, thereby improving user convenience.

[0093] The generation unit can estimate the user's emotions and adjust the tone and style of the advertising content based on the estimated user's emotions. For example, if the user is happy, the generation unit can generate an advertisement with a bright and positive tone. Also, if the user is depressed, the generation unit can generate an advertisement with a tone that gives a feeling of encouragement and hope. Furthermore, if the user is excited, the generation unit can generate an advertisement with an energetic and dynamic style. In this way, by generating advertising content with a tone and style according to the user's emotions, it is possible to provide more effective advertising.

[0094] The linking unit can estimate the user's emotions and select an advertisement delivery channel based on the estimated user's emotions. For example, when the user is relaxed, the linking unit delivers advertisements in a relaxing environment such as social media or blogs. When the user is busy, the linking unit can select banner advertisements or push notifications that can convey information in a short time. Furthermore, when the user is excited, the linking unit can select video advertisements or interactive advertisements. In this way, the effectiveness of advertisements can be maximized by selecting the optimal delivery channel according to the user's emotions.

[0095] The generation unit can estimate the user's emotion and adjust the color and visual effects of the advertisement content based on the estimated user's emotion. For example, when the user is relaxed, the generation unit generates an advertisement using calm colors and soft visual effects. When the user is excited, the generation unit can also generate an advertisement using vivid colors and dynamic visual effects. Furthermore, when the user is calm, the generation unit can also generate an advertisement with a simple and sophisticated design. In this way, by adjusting the color and visual effects according to the user's emotion, a visually appealing advertisement can be provided.

[0096] The linking unit can estimate the user's emotions and adjust the advertisement delivery schedule based on the estimated user's emotions. For example, when the user is relaxed, the linking unit delivers advertisements at a leisurely pace. When the user is in a hurry, the linking unit can also deliver advertisements quickly. Furthermore, when the user is excited, the linking unit can also link advertisements with visually stimulating effects. This allows for more effective advertisement delivery by adjusting the advertisement delivery schedule according to the user's emotions.

[0097] The process flow of the second embodiment will be briefly described below.

[0098] Step 1: The reception unit receives input from the user. User input includes text input, voice input, image input, etc. For example, a user can enter the features and budget of the product they want to advertise in text form through a messaging tool and send an image of the product, and the generation AI will obtain the information needed to generate the advertisement. Step 2: The generation unit uses a generation AI to generate advertising content based on the information received by the reception unit. The generation AI generates advertising content using a deep learning model, a natural language generation model, etc. The generation unit generates optimal advertising content based on the product features and budget, and adjusts the ad delivery destination and delivery frequency. Step 3: The linking unit links the advertisements generated by the generation unit to media. Media include social media, websites, mobile apps, etc. The linking unit links the generated advertisements to media such as messaging tools and e-commerce sites so that they can be easily published.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0100] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) are input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may also be an AI agent. In addition, when the processes of each of the above-mentioned parts are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples.In addition, processing performed by AI including the generation AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI including the generation AI.

[0101] Furthermore, the above-described processing by the data processing system 10 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information required for processing from the data processing device 12 or an external device.

[0102] Each of the multiple elements including the above-mentioned reception unit, generation unit, and linking 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 receives product features, budget, and product images input by the user through the messaging tool. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advertising content using a generation AI. The linking unit is realized, for example, by the control unit 46A of the smart device 14, and links the generated advertisement to media. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0106] 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.

[0107] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0108] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0109] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0110] Fig. 4 shows an example of main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0111] 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.

[0112] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0113] In the smart glasses 214, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0114] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0115] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0117] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device.

[0118] Each of the multiple elements including the above-mentioned reception unit, generation unit, and linking unit 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 receives product features, budget, and product images input by the user through a messaging tool. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advertising content using a generation AI. The linking unit is realized, for example, by the control unit 46A of the smart glasses 214, and links the generated advertisement to a media. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0119] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0120] 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.

[0121] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0122] 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.

[0123] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0124] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of an average healthy person).

[0125] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0126] 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.

[0127] 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.

[0128] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0129] In the headset type terminal 314, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50, and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0130] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0131] 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 a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0132] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0133] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device.

[0134] Each of the multiple elements including the above-mentioned reception unit, generation unit, and linking unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314, and receives product features, budget, and product images input by the user through the messaging tool. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advertising content using a generation AI. The linking unit is realized, for example, by the control unit 46A of the headset type terminal 314, and links the generated advertisement to media. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0137] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0138] 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.

[0139] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs the voice according to instructions from the processor 46.

[0140] Camera 42 is a small digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (e.g., an imaging range defined by an angle of view equivalent to the width of the field of vision of a typical healthy person).

[0141] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is performed in a secure state.

[0142] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and legs. The posture and behavior of the robot 414 are controlled by controlling the motors of the arms, hands, and legs. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0143] 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.

[0144] 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.

[0145] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the emotion of the user using the emotion identification model 59 and perform identification processing using the user's emotion. In the emotion estimation function (emotion identification function) using the emotion identification model 59, various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, are performed, but are not limited to such examples. In addition, the estimation and prediction of the emotion also include, for example, analysis of the emotion.

[0146] In the robot 414, the specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads out the specific program 60 from the storage 50, and executes the specific program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0147] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (such as a prediction result) using the data generation model 58 by communicating with the server device having the data generation model 58. In addition, the data processing device 12 may be a server device, or may be a terminal device (such as a mobile phone, a robot, or a home appliance) owned by a user.

[0148] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0149] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image (e.g., still image data or video data) is input. The data generation model 58 infers the input inference data according to the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, and image data. The data generation model 58 includes, for example, a text generation AI, an image generation AI, and a multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing while using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction, and in this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. In the data processing device 12 and the like, the data generation model 58 includes a plurality of types, and the data generation model 58 includes an AI other than the generation AI. The AI ​​other than the generation AI is, for example, a linear regression, a logistic regression, a decision tree, a random forest, a support vector machine (SVM), a k-means clustering, a convolutional neural network (CNN), a recurrent neural network (RNN), a generative adversarial network (GAN), or a naive Bayes, and can perform various processes, but is not limited to such examples. The AI ​​may be an AI agent. In addition, when the processes of each part described above are performed by an AI, the processes are performed in part or in whole by the AI, but are not limited to such examples. In addition, the processes performed by the AI ​​including the generation AI may be replaced with rule-based processes, and the rule-based processes may be replaced with processes performed by the AI ​​including the generation AI.

[0150] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0151] Each of the multiple elements including the above-mentioned reception unit, generation unit, and linking unit 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 receives product features, budget, and product images input by the user through a messaging tool. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and generates advertising content using a generation AI. The linking unit is realized, for example, by the control unit 46A of the robot 414, and links the generated advertisement to media. The correspondence between each unit and the device or control unit is not limited to the above-mentioned example, and various changes are possible.

[0152] The emotion identification model 59 as an emotion engine may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 59 may determine the emotion of the user according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the emotion of the robot, and the identification processing unit 290 may perform identification processing using the emotion of the robot.

[0153] FIG. 9 is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0157] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs the user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of the user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in Fig. 10. Fig. 10 shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0159] 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.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0162] 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.

[0163] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing a specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0164] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments are described separately, but some or all of these embodiments may be combined. The smart device 14, the smart glasses 214, the headset terminal 314, and the robot 414 are only examples, and they may be combined with each other, or may be other devices. In the above example, the first and second embodiments are described separately, but they may be combined.

[0168] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replaced with respect to the description and illustrations shown above, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the description and illustrations shown above omit explanations of technical common sense that do not require particular explanation in enabling the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference.

[0170] (Appendix 1) A reception unit that receives an input from a user; a generating unit that generates advertising content based on the information received by the receiving unit; A linking unit that links the advertisement generated by the generation unit to a medium; Equipped A system characterized by: (Appendix 2) The generation unit is Generate advertising content using generative AI 2. The system of claim 1, (Appendix 3) The generation unit is Generate appropriate ad content based on product features and budget 2. The system of claim 1, (Appendix 4) The linking portion is Linking the generated advertisement to at least one of a messaging tool or an e-commerce site. 2. The system of claim 1, (Appendix 5) The generation unit is Tailor your ads with generative AI 2. The system of claim 1, (Appendix 6) The generation unit is Generative AI adjusts ad delivery destinations and frequency 2. The system of claim 1, (Appendix 7) The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion. 2. The system of claim 1, (Appendix 8) The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1, (Appendix 9) The reception unit is As you type, filter your input based on your current projects and interests 2. The system of claim 1, (Appendix 10) The reception unit is Infer user emotions and prioritize input content based on the inferred user emotions 2. The system of claim 1, (Appendix 11) The reception unit is When typing, it takes into account the user's geographic location to prioritize relevant input 2. The system of claim 1, (Appendix 12) The reception unit is Analyze your social media activity and suggest relevant entries as you type 2. The system of claim 1, (Appendix 13) The generation unit is To estimate a user's emotion and adjust a method of expressing advertising content based on the estimated user's emotion. 2. The system of claim 1, (Appendix 14) The generation unit is When generating ad content, adjust the level of detail based on the importance of the product 2. The system of claim 1, (Appendix 15) The generation unit is When generating advertising content, different generation algorithms are applied depending on the product category. 2. The system of claim 1, (Appendix 16) The generation unit is To estimate a user's emotion and adjust the length of advertisement content to be generated based on the estimated user's emotion. 2. The system of claim 1, (Appendix 17) The generation unit is When generating ad content, prioritize generation based on when the product was submitted 2. The system of claim 1, (Appendix 18) The generation unit is When generating ad content, adjust the order of generation based on product relevance 2. The system of claim 1, (Appendix 19) The linking portion is Inferring user emotions and adjusting advertising coordination methods based on the estimated user emotions 2. The system of claim 1, (Appendix 20) The linking portion is When linking ads, select the appropriate linking method by referring to the user's past linking history. 2. The system of claim 1, (Appendix 21) The linking portion is When integrating ads, customize the integration based on the user's current marketing strategy 2. The system of claim 1, (Appendix 22) The linking portion is To estimate a user's emotion and determine a priority order of advertisements based on the estimated user's emotion. 2. The system of claim 1, (Appendix 23) The linking portion is When linking advertisements, select the appropriate linking method taking into account the user's geographic location information. 2. The system of claim 1, (Appendix 24) The linking portion is When linking ads, analyze users' social media activity to suggest linking methods 2. The system of claim 1, [Explanation of symbols]

[0171] 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 unit that receives an input from a user; a generating unit that generates advertising content based on the information received by the receiving unit; A linking unit that links the advertisement generated by the generation unit to a medium; Equipped A system characterized by:

2. The generation unit is Generate advertising content using generative AI 2. The system of claim 1 .

3. The generation unit is Generate appropriate ad content based on product features and budget 2. The system of claim 1 .

4. The linking portion is Linking the generated advertisement to at least one of a messaging tool or an e-commerce site.

2. The system of claim 1 .

5. The generation unit is Tailoring ads with generative AI 2. The system of claim 1 .

6. The generation unit is Adjust ad delivery destination and frequency using generative AI 2. The system of claim 1 .

7. The reception unit is To estimate a user's emotion and adjust a display method of an input interface based on the estimated user's emotion.

2. The system of claim 1 .

8. The reception unit is Analyze the user's past input history and suggest appropriate input methods 2. The system of claim 1 .

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