system

Abstract

The system according to this embodiment aims to enable companies with limited financial resources and other resources to implement effective marketing strategies. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, a linking unit, an advertising unit, a response unit, and a selection unit. The reception unit receives input materials. The generation unit generates content based on the materials received by the reception unit. The linking unit links the content generated by the generation unit with other services. The advertising unit delivers advertisements based on the content generated by the generation unit. The response unit provides customer support based on the content generated by the generation unit. The selection unit selects promotional proposals and collaborators based on the content generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult for companies with insufficient financial strength and resources to implement effective marketing strategies.

[0005] The system according to the embodiment aims to enable companies with insufficient financial strength and resources to implement effective marketing strategies.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a generation unit, a linking unit, an advertising unit, a response unit, and a selection unit. The reception unit receives input materials. The generation unit generates content based on the materials input by the reception unit. The linking unit links the content generated by the generation unit with other services. The advertising unit delivers advertisements based on the content generated by the generation unit. The response unit provides customer support based on the content generated by the generation unit. The selection unit selects promotional proposals and collaborators based on the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can enable companies with limited financial resources to implement effective marketing strategies. [Brief explanation of the drawing]

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

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

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

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

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

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The marketing automation tool according to an embodiment of the present invention is a system that supports the business growth of customers by combining three agents. In this marketing automation tool, materials (images and videos) provided by the business owner are input into an interactive agent, and a content generation and integration agent generates content based on the materials and stores it in the official account. The generated content is automatically integrated with other services. Next, an advertising submission and customer support agent utilizes the generated content to automatically design and execute advertising distribution and customer support. Finally, a planning, creation, and collaboration partner selection agent selects promotional plans and collaboration partners based on the customer's marketing challenges, supporting business expansion. This makes it possible for companies with limited financial resources to efficiently grow their businesses. For example, a restaurant submits images and videos to promote a new menu, generates content through the interactive agent, and stores it in the official account. The generated content is automatically integrated with other services, and the advertising submission and customer support agent automatically handles advertising distribution and customer support. Furthermore, the planning, creation, and collaboration partner selection agent selects promotional plans and collaboration partners, supporting business expansion. This allows restaurants to efficiently promote new menu items, handle customer interactions, and grow their businesses. Marketing automation tools, in turn, can efficiently support their clients' business growth.

[0029] The marketing automation tool according to this embodiment comprises a reception unit, a generation unit, a linking unit, an advertising unit, a response unit, and a selection unit. The reception unit inputs materials provided by the business owner. These materials include, but are not limited to, images, videos, and text. The reception unit provides, for example, an interface for uploading images and videos provided by the business owner. The reception unit can also provide a form for inputting text information provided by the business owner. The generation unit generates content based on the materials input by the reception unit using a generation AI. The generation unit generates, for example, advertising banners and promotional videos based on images and videos provided by the business owner using a generation AI. The generation unit can also generate articles and blog posts based on text information provided by the business owner using a generation AI. Some or all of the above processing in the generation unit is performed using a generation AI. The linking unit links the content generated by the generation unit with other services. The linking unit stores the generated content in an official account and automatically links it with other services. The linking unit can also distribute the generated content to other platforms. Some or all of the above-mentioned processes in the Collaboration Department are performed using AI. The Advertising Department delivers advertisements based on the content generated by the Generation Department. For example, the Advertising Department delivers targeted advertisements based on the generated content. The Advertising Department can also deliver retargeting advertisements based on the generated content. Some or all of the above-mentioned processes in the Advertising Department are performed using AI. The Response Department handles customer support based on the content generated by the Generation Department. For example, the Response Department handles customer support using a chatbot based on the generated content. The Response Department can also handle email support based on the generated content. Some or all of the above-mentioned processes in the Response Department are performed using AI. The Selection Department selects promotional proposals and collaboration partners based on the content generated by the Generation Department. For example, the Selection Department selects campaign plans based on the generated content. The Selection Department can also select collaboration partners based on the generated content. Some or all of the above-mentioned processes in the Selection Department are performed using AI.As a result, the marketing automation tool according to this embodiment can automate everything from receiving materials and generating content to linking, distributing advertisements, handling customer inquiries, and selecting promotional plans and collaboration partners, enabling efficient business growth.

[0030] The reception desk inputs materials provided by the business owner. These materials include, but are not limited to, images, videos, and text. The reception desk provides an interface for uploading images and videos provided by the business owner. Specifically, business owners can access a dedicated web portal and easily upload image and video files using drag-and-drop functionality. The reception desk can also provide a form for inputting text information provided by the business owner. This form includes a text editor for business owners to input ad copy and article content, and allows for formatting and style adjustments. Furthermore, the reception desk provides fields for inputting metadata of the materials provided by the business owner. For example, by entering titles, descriptions, and tags for images and videos, the materials can be efficiently managed in subsequent processing. This allows the reception desk to efficiently collect diverse materials provided by business owners and smoothly hand them over to the next processing step.

[0031] The generation unit uses generation AI to generate content based on materials entered by the reception unit. For example, the generation unit uses generation AI to generate advertising banners and promotional videos based on images and videos provided by business owners. Specifically, the generation AI analyzes the content of provided images using image recognition technology and automatically generates appropriate designs and layouts. For example, it analyzes the color tone and composition of images to create visually appealing banners. The generation AI also analyzes provided videos using video editing technology and edits promotional videos and adds effects. For example, it extracts important scenes from videos and arranges them in an effective order to create promotional videos that capture the viewer's attention. Furthermore, the generation unit can also use generation AI to generate articles and blog posts based on text information provided by business owners. The generation AI analyzes the content of provided text using natural language processing technology and generates articles with appropriate writing style and structure. For example, it automatically creates articles that take SEO into consideration based on provided keywords and themes. In this way, the generation unit can efficiently generate high-quality content based on materials provided by business owners and support their marketing activities.

[0032] The Integration Unit integrates the content generated by the Generation Unit with other services. For example, the Integration Unit stores the generated content in an official account and automatically integrates it with other services. Specifically, the Integration Unit has the functionality to automatically post the generated content to the official website and social media accounts. For example, by automatically publishing a generated blog post to the blog section of the official website and posting a link to social media accounts, information can be delivered to a wide range of users. The Integration Unit can also distribute the generated content to other platforms. For example, by uploading a generated promotional video to a video sharing site and distributing it to an advertising network, the target audience can be reached. Some or all of the above processes in the Integration Unit are performed using AI. The AI ​​analyzes the optimal posting time and format for each platform and automatically adjusts them to maximize the effectiveness of the content. This allows the Integration Unit to efficiently distribute the generated content and enhance the effectiveness of marketing activities.

[0033] The advertising department delivers advertisements based on content generated by the content creation department. For example, the advertising department delivers targeted advertisements based on the generated content. Specifically, the advertising department analyzes the attributes and behavioral history of the target audience based on the generated banners and promotional videos, and delivers the most suitable advertisements. For example, by displaying highly relevant advertisements to users in specific age groups or with specific interests, the effectiveness of the advertisements is maximized. The advertising department can also deliver retargeting advertisements based on the generated content. Retargeting advertisements are a method of displaying advertisements again to users who have previously visited the website or performed specific actions. The advertising department uses AI to analyze users' behavioral history and deliver retargeting advertisements at the optimal time. This allows the advertising department to deliver effective advertisements based on the generated content and improve the results of marketing activities.

[0034] The support unit handles customer inquiries based on content generated by the generation unit. For example, the support unit uses a chatbot to handle customer inquiries based on the generated content. Specifically, the support unit has a function that allows a chatbot to automatically answer customer questions based on generated FAQs and support articles. For example, when a customer enters a question about a product into the chatbot, the chatbot refers to the generated content and provides an appropriate answer. The support unit can also handle email inquiries based on the generated content. For example, it can automatically create a reply email based on a generated template to respond quickly to customer inquiry emails. Some or all of the above processes in the support unit are performed using AI. The AI ​​analyzes customer inquiries and generates optimal answers, thereby improving customer satisfaction. This allows the support unit to handle customer inquiries efficiently and effectively based on the generated content, improving the customer experience.

[0035] The selection department selects promotional proposals and collaboration partners based on the content generated by the generation department. For example, the selection department selects campaign plans based on the generated content. Specifically, the selection department analyzes the content of the generated content and the attributes of the target audience to plan the optimal campaign. For example, it plans promotional campaigns tailored to specific seasons or events to effectively reach the target audience. The selection department can also select collaboration partners based on the generated content. For example, it selects collaboration partners that match the theme and brand image of the generated content and conducts joint promotional activities. Some or all of the above processes in the selection department are performed using AI. The AI ​​analyzes past data and market trends to select the optimal promotional proposals and collaboration partners, thereby maximizing the effectiveness of marketing activities. This allows the selection department to select effective promotional proposals and collaboration partners based on the generated content, supporting business growth.

[0036] The generation unit can generate content using a generation AI. For example, the generation unit can use the generation AI to generate advertising banners and promotional videos based on images and videos provided by the business owner. For example, the generation unit can generate an advertising banner when the generation AI receives a prompt such as "Generate an advertising banner based on this image." The generation unit can also generate articles and blog posts based on text information provided by the business owner using the generation AI. For example, the generation unit can generate a blog post when the generation AI receives a prompt such as "Generate a blog post based on this text." This improves the accuracy and efficiency of content generation by using a generation AI. Some or all of the above processes in the generation unit are performed using a generation AI.

[0037] The integration unit can automatically integrate the generated content with other services. For example, the integration unit stores the generated content in an official account and automatically integrates it with other services. For example, when storing the generated content in an official account, the integration unit uses APIs to integrate with other services. The integration unit can also distribute the generated content to other platforms. For example, the integration unit automatically distributes the generated content to other platforms based on a schedule. This reduces the effort required for integration work by automatically integrating the generated content with other services. Some or all of the above processes in the integration unit may be performed using AI or not.

[0038] The advertising department can deliver advertisements based on the generated content. For example, the advertising department can deliver targeted advertisements based on the generated content. For example, when the advertising department delivers targeted advertisements based on the generated content, it can deliver advertisements based on user attribute information. The advertising department can also deliver retargeting advertisements based on the generated content. For example, when the advertising department delivers retargeting advertisements based on the generated content, it can deliver advertisements based on the user's behavior history. In this way, the effectiveness of advertisements can be maximized by delivering advertisements based on the generated content. Some or all of the above processes in the advertising department may be performed using AI or not.

[0039] The support unit can provide customer support based on the generated content. For example, the support unit can provide customer support using a chatbot based on the generated content. For example, when providing customer support using a chatbot based on the generated content, the support unit will automatically answer user questions. The support unit can also provide email support based on the generated content. For example, when providing email support based on the generated content, the support unit will automatically reply to user inquiries. This improves the efficiency and quality of customer support by providing customer support based on the generated content. Some or all of the above processes in the support unit may be performed using AI or not.

[0040] The selection unit can select promotional proposals and collaboration partners based on the generated content. For example, the selection unit can select campaign plans based on the generated content. For example, when selecting campaign plans based on the generated content, the selection unit can select campaign plans based on user attribute information. The selection unit can also select collaboration partners based on the generated content. For example, when selecting collaboration partners based on the generated content, the selection unit can select industry leaders or startup companies. In this way, by selecting promotional proposals and collaboration partners based on the generated content, it is possible to support business growth. Some or all of the above processing in the selection unit may be performed using AI or not.

[0041] The reception department can analyze a user's past material submission history and select the optimal reception method. For example, the reception department may prioritize accepting materials of types that the user has frequently submitted in the past. For example, the reception department may predict materials to be submitted at specific times based on a user's past submission history and concentrate reception during those times. The reception department can also analyze a user's past submission history and suggest the optimal reception method (online, offline, etc.). In this way, by analyzing a user's past material submission history, the reception department can select the optimal reception method and achieve efficient material reception. Some or all of the above processes in the reception department may be performed using AI or not.

[0042] The reception unit can filter materials upon receipt based on the user's current projects and areas of interest. For example, the reception unit can prioritize accepting only materials related to the user's current ongoing projects. For example, the reception unit can filter and accept highly relevant materials based on the user's areas of interest. The reception unit can also prioritize accepting necessary materials according to the progress of the user's projects. This allows for the priority acceptance of highly relevant materials by filtering materials based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not.

[0043] The reception unit can prioritize accepting materials that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize accepting materials related to that region. For example, based on the user's geographical location, the reception unit can accept materials that match the trends of each region. Furthermore, if the user is on the move, the reception unit can prioritize accepting relevant materials closest to the user's current location. In this way, by accepting materials while considering the user's geographical location, it is possible to prioritize accepting materials that match the trends of each region. Some or all of the above processing in the reception unit may be performed using AI, or it may be performed without using AI.

[0044] The reception desk can analyze the user's social media activity when receiving materials and accept relevant materials. For example, the reception desk can prioritize accepting relevant materials based on content shared by the user on social media. For example, the reception desk can prioritize accepting influential materials by considering the user's number of social media followers and engagement rate. The reception desk can also analyze the content of the user's social media posts and accept relevant materials. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant materials. Some or all of the above processing in the reception desk may be performed using AI or not.

[0045] The generation unit can adjust the level of detail of the generated content based on the importance of the materials. For example, for high-importance materials, the generation unit will generate content that includes detailed explanations and high-quality visuals. For example, for low-importance materials, the generation unit will generate content that includes concise explanations and simple visuals. The generation unit can also adjust the length and amount of information in the generated content according to the importance of the materials. This allows for efficient content generation by adjusting the level of detail based on the importance of the materials. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0046] The generation unit can apply different generation algorithms depending on the category of the material when generating content. For example, the generation unit can apply an image processing algorithm to image materials to generate high-quality visual content. For example, the generation unit can apply a video editing algorithm to video materials to generate engaging video content. Furthermore, the generation unit can also apply a natural language processing algorithm to text materials to generate easy-to-read text content. In this way, by applying different generation algorithms depending on the category of material, the optimal content can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0047] The generation unit can determine the generation priority based on the submission timing of materials when generating content. For example, the generation unit can determine the generation priority based on the order in which materials were submitted. For example, the generation unit can prioritize the generation of materials that are of high urgency. The generation unit can also prioritize the generation of materials submitted within a specific time period based on the submission timing. This allows for efficient content generation by determining the generation priority based on the submission timing of materials. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0048] The generation unit can adjust the generation order based on the relevance of the materials when generating content. For example, the generation unit can prioritize generating highly relevant materials. For example, it can postpone the generation of less relevant materials. The generation unit can also optimize the generation order based on the relevance of the materials. This allows for efficient content generation by adjusting the generation order based on the relevance of the materials. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0049] The integration unit can improve the accuracy of integration by considering the interrelationships between content during integration. For example, the integration unit prioritizes integrating related content. For example, the integration unit analyzes the interrelationships between content and selects the optimal integration method. The integration unit can also improve the accuracy of integration based on the relevance of content. In this way, the accuracy of integration can be improved by considering the interrelationships between content. Some or all of the above processing in the integration unit may be performed using AI or not.

[0050] The integration unit can perform integration while considering the attribute information of the content submitter. For example, the integration unit can select the optimal integration method based on the submitter's attribute information (age, gender, occupation, etc.). For example, the integration unit can prioritize integrating highly relevant content by considering the submitter's attribute information. The integration unit can also improve the accuracy of integration based on the submitter's attribute information. In this way, optimal integration can be achieved by considering the attribute information of the content submitter. Some or all of the above processing in the integration unit may be performed using AI or not.

[0051] The integration unit can perform integration while considering the geographical distribution of content. For example, the integration unit prioritizes integrating geographically close content. For example, the integration unit selects the optimal integration method considering the geographical distribution. Furthermore, the integration unit can improve the accuracy of integration based on the geographical distribution. In this way, optimal integration can be achieved by considering the geographical distribution of content. Some or all of the above processing in the integration unit may be performed using AI or not.

[0052] The integration unit can improve the accuracy of the integration by referring to related literature for the content during the integration process. For example, the integration unit can select the optimal integration method by referring to related literature. For example, the integration unit can improve the accuracy of the integration based on related literature. The integration unit can also optimize the results of the integration by referring to related literature. In this way, the accuracy of the integration can be improved by referring to related literature for the content. Some or all of the above processing in the integration unit may be performed using AI or not.

[0053] The advertising department can predict the current effectiveness of an ad by referring to past advertising data when delivering ads. For example, the advertising department can predict the click-through rate of a current ad based on past advertising data. For example, the advertising department can predict the conversion rate of a current ad by referring to past advertising data. The advertising department can also analyze past advertising data to optimize the effectiveness of current ads. In this way, by referring to past advertising data, it is possible to predict the current effectiveness of an ad and achieve optimal ad delivery. Some or all of the above processes in the advertising department may be performed using AI or not.

[0054] The advertising department can apply different advertising delivery methods to each content category when delivering ads. For example, the advertising department can apply a visually-oriented advertising delivery method to image content. For example, the advertising department can apply a video advertising delivery method to video content. Furthermore, the advertising department can also apply a text advertising delivery method to text content. By applying different advertising delivery methods to each content category, optimal ad delivery can be achieved. Some or all of the above processing in the advertising department may be performed using AI, or not using AI.

[0055] The advertising department can analyze changes in advertisements based on the submission timing of content when delivering ads. For example, the advertising department can analyze changes in the click-through rate of ads based on the submission timing. For example, the advertising department can analyze changes in the conversion rate of ads based on the submission timing. The advertising department can also analyze changes in the effectiveness of ads based on the submission timing. By analyzing changes in advertisements based on the submission timing of content, optimal ad delivery can be achieved. Some or all of the above processes in the advertising department may be performed using AI or not.

[0056] The advertising department can analyze ads by referring to relevant market data for the content when delivering ads. For example, the advertising department can analyze the click-through rate of ads based on relevant market data. For example, the advertising department can analyze the conversion rate of ads by referring to relevant market data. The advertising department can also optimize the effectiveness of ads based on relevant market data. In this way, the effectiveness of ads can be optimized by referring to relevant market data for the content. Some or all of the above processes in the advertising department may be performed using AI or not.

[0057] The response unit can optimize the current response method by referring to past response data when dealing with customers. For example, the response unit optimizes the current response method based on past response data. For example, the response unit improves the current response method by referring to past response data. The response unit can also analyze past response data and select the optimal response method. As a result, by referring to past response data, the current response method can be optimized, leading to more effective customer service. Some or all of the above processes in the response unit may be performed using AI, or they may not be performed using AI.

[0058] The response unit can apply different response methods depending on the content category when handling customer inquiries. For example, it can apply a visually-oriented response method to image content, a video response method to video content, and a text response method to text content. By applying different response methods to each content category, more appropriate customer service can be achieved. Some or all of the above processing in the response unit may be performed using AI or not.

[0059] The response unit can analyze changes in customer responses based on the timing of content submission. For example, the response unit analyzes changes in responses based on submission timing. For example, the response unit analyzes changes in the effectiveness of responses based on submission timing. The response unit can also optimize responses based on submission timing. This allows for more appropriate customer responses by analyzing changes in responses based on the timing of content submission. Some or all of the above processing in the response unit may be performed using AI or not.

[0060] The response unit can analyze customer interactions by referring to relevant market data for the content. For example, the response unit can analyze the effectiveness of the response based on the relevant market data. For example, the response unit can optimize the response by referring to the relevant market data. The response unit can also improve the response based on the relevant market data. This allows for more appropriate customer interactions by referring to relevant market data for the content. Some or all of the above processing in the response unit may be performed using AI or not.

[0061] The selection unit can improve the accuracy of its selection process by considering the interrelationships between content. For example, the selection unit may prioritize selecting related content. For example, the selection unit may analyze the interrelationships between content and select the optimal selection method. The selection unit can also improve the accuracy of its selection process based on the relevance of content. In this way, the accuracy of the selection process can be improved by considering the interrelationships between content. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0062] The selection unit can make selections while considering the attribute information of the content submitter. For example, the selection unit can select the optimal selection method based on the submitter's attribute information (age, gender, occupation, etc.). For example, the selection unit can prioritize the selection of highly relevant promotional proposals and collaboration partners by considering the submitter's attribute information. The selection unit can also improve the accuracy of the selection based on the submitter's attribute information. In this way, optimal selection can be achieved by considering the attribute information of the content submitter. Some or all of the above processes in the selection unit may be performed using AI or not.

[0063] The selection unit can consider the geographical distribution of content when making selections. For example, the selection unit may prioritize promotional proposals or collaboration partners that are geographically close. For example, the selection unit may select the optimal selection method considering the geographical distribution. Furthermore, the selection unit can improve the accuracy of selection based on geographical distribution. This makes it possible to achieve optimal selection by considering the geographical distribution of content. Some or all of the above processes in the selection unit may be performed using AI or not.

[0064] The selection unit can improve the accuracy of its selection process by referring to relevant literature for the content. For example, the selection unit can select the optimal selection method by referring to relevant literature. For example, the selection unit can improve the accuracy of its selection based on relevant literature. The selection unit can also optimize the selection results by referring to relevant literature. In this way, the accuracy of the selection can be improved by referring to relevant literature for the content. Some or all of the above processes in the selection unit may be performed using AI or not.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The reception department can analyze a user's past material submission history and select the optimal reception method. For example, it can prioritize the acceptance of materials that the user has frequently submitted in the past. It can also predict materials that will be submitted during specific time periods based on the user's past submission history and concentrate reception during those times. Furthermore, it can analyze the user's past submission history and suggest the optimal reception method (online, offline, etc.). In this way, by analyzing the user's past material submission history, the optimal reception method can be selected, resulting in efficient material reception. Some or all of the above processes in the reception department may be performed using AI, or they may not.

[0067] The reception unit can filter materials upon receipt based on the user's current projects and areas of interest. For example, it can prioritize receiving only materials related to the user's current project. It can filter and accept materials that are highly relevant based on the user's areas of interest. It can also prioritize receiving necessary materials according to the progress of the user's project. This allows for the priority acceptance of highly relevant materials by filtering materials based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or it may not be performed using AI.

[0068] The generation unit can adjust the level of detail generated based on the importance of the source material during content generation. For example, for highly important source material, it generates content that includes detailed explanations and high-quality visuals. For less important source material, it generates content that includes concise explanations and simple visuals. It can also adjust the length and amount of information in the generated content according to the importance of the source material. This allows for efficient content generation by adjusting the level of detail based on the importance of the source material. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0069] The integration unit can improve the accuracy of integration by considering the interrelationships between content during the integration process. For example, it can prioritize integrating related content. It can analyze the interrelationships between content and select the optimal integration method. It can also improve the accuracy of integration based on the relevance of content. In this way, the accuracy of integration can be improved by considering the interrelationships between content. Some or all of the above processing in the integration unit may be performed using AI or not.

[0070] The advertising department can predict the current effectiveness of an ad by referring to past advertising data when delivering ads. For example, it can predict the click-through rate of a current ad based on past advertising data. It can also predict the conversion rate of a current ad by referring to past advertising data. Furthermore, it can analyze past advertising data to optimize the effectiveness of the current ad. In this way, by referring to past advertising data, it is possible to predict the current effectiveness of an ad and achieve optimal ad delivery. Some or all of the above processes in the advertising department may be performed using AI, or they may not be performed using AI.

[0071] The following briefly describes the processing flow for example form 1.

[0072] Step 1: The reception desk inputs the materials provided by the business owner. These materials include images, videos, and text. The reception desk provides an interface for uploading images and videos provided by the business owner, as well as a form for entering text information. Step 2: The generation unit uses generation AI to generate content based on the materials entered by the reception unit. For example, it generates advertising banners and promotional videos based on images and videos provided by the business owner, and generates articles and blog posts based on text information. Step 3: The integration unit integrates the content generated by the generation unit with other services. For example, it stores the generated content in an official account and automatically integrates it with other services or distributes it to other platforms. Step 4: The advertising department delivers ads based on the content generated by the content creation department. For example, they deliver targeted ads and retargeting ads based on the generated content. Step 5: The support unit handles customer inquiries based on the content generated by the generation unit. For example, it might use a chatbot or email to handle customer inquiries based on the generated content. Step 6: The selection team selects promotional plans and collaborators based on the content generated by the generation team. For example, they select campaign plans and collaborators based on the generated content.

[0073] (Example of form 2) The marketing automation tool according to an embodiment of the present invention is a system that supports the business growth of customers by combining three agents. In this marketing automation tool, materials (images and videos) provided by the business owner are input into an interactive agent, and a content generation and integration agent generates content based on the materials and stores it in the official account. The generated content is automatically integrated with other services. Next, an advertising submission and customer support agent utilizes the generated content to automatically design and execute advertising distribution and customer support. Finally, a planning, creation, and collaboration partner selection agent selects promotional plans and collaboration partners based on the customer's marketing challenges, supporting business expansion. This makes it possible for companies with limited financial resources to efficiently grow their businesses. For example, a restaurant submits images and videos to promote a new menu, generates content through the interactive agent, and stores it in the official account. The generated content is automatically integrated with other services, and the advertising submission and customer support agent automatically handles advertising distribution and customer support. Furthermore, the planning, creation, and collaboration partner selection agent selects promotional plans and collaboration partners, supporting business expansion. This allows restaurants to efficiently promote new menu items, handle customer interactions, and grow their businesses. Marketing automation tools, in turn, can efficiently support their clients' business growth.

[0074] The marketing automation tool according to this embodiment comprises a reception unit, a generation unit, a linking unit, an advertising unit, a response unit, and a selection unit. The reception unit inputs materials provided by the business owner. These materials include, but are not limited to, images, videos, and text. The reception unit provides, for example, an interface for uploading images and videos provided by the business owner. The reception unit can also provide a form for inputting text information provided by the business owner. The generation unit generates content based on the materials input by the reception unit using a generation AI. The generation unit generates, for example, advertising banners and promotional videos based on images and videos provided by the business owner using a generation AI. The generation unit can also generate articles and blog posts based on text information provided by the business owner using a generation AI. Some or all of the above processing in the generation unit is performed using a generation AI. The linking unit links the content generated by the generation unit with other services. The linking unit stores the generated content in an official account and automatically links it with other services. The linking unit can also distribute the generated content to other platforms. Some or all of the above-mentioned processes in the Collaboration Department are performed using AI. The Advertising Department delivers advertisements based on the content generated by the Generation Department. For example, the Advertising Department delivers targeted advertisements based on the generated content. The Advertising Department can also deliver retargeting advertisements based on the generated content. Some or all of the above-mentioned processes in the Advertising Department are performed using AI. The Response Department handles customer support based on the content generated by the Generation Department. For example, the Response Department handles customer support using a chatbot based on the generated content. The Response Department can also handle email support based on the generated content. Some or all of the above-mentioned processes in the Response Department are performed using AI. The Selection Department selects promotional proposals and collaboration partners based on the content generated by the Generation Department. For example, the Selection Department selects campaign plans based on the generated content. The Selection Department can also select collaboration partners based on the generated content. Some or all of the above-mentioned processes in the Selection Department are performed using AI.As a result, the marketing automation tool according to this embodiment can automate everything from receiving materials and generating content to linking, distributing advertisements, handling customer inquiries, and selecting promotional plans and collaboration partners, enabling efficient business growth.

[0075] The reception desk inputs materials provided by the business owner. These materials include, but are not limited to, images, videos, and text. The reception desk provides an interface for uploading images and videos provided by the business owner. Specifically, business owners can access a dedicated web portal and easily upload image and video files using drag-and-drop functionality. The reception desk can also provide a form for inputting text information provided by the business owner. This form includes a text editor for business owners to input ad copy and article content, and allows for formatting and style adjustments. Furthermore, the reception desk provides fields for inputting metadata of the materials provided by the business owner. For example, by entering titles, descriptions, and tags for images and videos, the materials can be efficiently managed in subsequent processing. This allows the reception desk to efficiently collect diverse materials provided by business owners and smoothly hand them over to the next processing step.

[0076] The generation unit uses generation AI to generate content based on materials entered by the reception unit. For example, the generation unit uses generation AI to generate advertising banners and promotional videos based on images and videos provided by business owners. Specifically, the generation AI analyzes the content of provided images using image recognition technology and automatically generates appropriate designs and layouts. For example, it analyzes the color tone and composition of images to create visually appealing banners. The generation AI also analyzes provided videos using video editing technology and edits promotional videos and adds effects. For example, it extracts important scenes from videos and arranges them in an effective order to create promotional videos that capture the viewer's attention. Furthermore, the generation unit can also use generation AI to generate articles and blog posts based on text information provided by business owners. The generation AI analyzes the content of provided text using natural language processing technology and generates articles with appropriate writing style and structure. For example, it automatically creates articles that take SEO into consideration based on provided keywords and themes. In this way, the generation unit can efficiently generate high-quality content based on materials provided by business owners and support their marketing activities.

[0077] The Integration Unit integrates the content generated by the Generation Unit with other services. For example, the Integration Unit stores the generated content in an official account and automatically integrates it with other services. Specifically, the Integration Unit has the functionality to automatically post the generated content to the official website and social media accounts. For example, by automatically publishing a generated blog post to the blog section of the official website and posting a link to social media accounts, information can be delivered to a wide range of users. The Integration Unit can also distribute the generated content to other platforms. For example, by uploading a generated promotional video to a video sharing site and distributing it to an advertising network, the target audience can be reached. Some or all of the above processes in the Integration Unit are performed using AI. The AI ​​analyzes the optimal posting time and format for each platform and automatically adjusts them to maximize the effectiveness of the content. This allows the Integration Unit to efficiently distribute the generated content and enhance the effectiveness of marketing activities.

[0078] The advertising department delivers advertisements based on content generated by the content creation department. For example, the advertising department delivers targeted advertisements based on the generated content. Specifically, the advertising department analyzes the attributes and behavioral history of the target audience based on the generated banners and promotional videos, and delivers the most suitable advertisements. For example, by displaying highly relevant advertisements to users in specific age groups or with specific interests, the effectiveness of the advertisements is maximized. The advertising department can also deliver retargeting advertisements based on the generated content. Retargeting advertisements are a method of displaying advertisements again to users who have previously visited the website or performed specific actions. The advertising department uses AI to analyze users' behavioral history and deliver retargeting advertisements at the optimal time. This allows the advertising department to deliver effective advertisements based on the generated content and improve the results of marketing activities.

[0079] The support unit handles customer inquiries based on content generated by the generation unit. For example, the support unit uses a chatbot to handle customer inquiries based on the generated content. Specifically, the support unit has a function that allows a chatbot to automatically answer customer questions based on generated FAQs and support articles. For example, when a customer enters a question about a product into the chatbot, the chatbot refers to the generated content and provides an appropriate answer. The support unit can also handle email inquiries based on the generated content. For example, it can automatically create a reply email based on a generated template to respond quickly to customer inquiry emails. Some or all of the above processes in the support unit are performed using AI. The AI ​​analyzes customer inquiries and generates optimal answers, thereby improving customer satisfaction. This allows the support unit to handle customer inquiries efficiently and effectively based on the generated content, improving the customer experience.

[0080] The selection department selects promotional proposals and collaboration partners based on the content generated by the generation department. For example, the selection department selects campaign plans based on the generated content. Specifically, the selection department analyzes the content of the generated content and the attributes of the target audience to plan the optimal campaign. For example, it plans promotional campaigns tailored to specific seasons or events to effectively reach the target audience. The selection department can also select collaboration partners based on the generated content. For example, it selects collaboration partners that match the theme and brand image of the generated content and conducts joint promotional activities. Some or all of the above processes in the selection department are performed using AI. The AI ​​analyzes past data and market trends to select the optimal promotional proposals and collaboration partners, thereby maximizing the effectiveness of marketing activities. This allows the selection department to select effective promotional proposals and collaboration partners based on the generated content, supporting business growth.

[0081] The generation unit can generate content using a generation AI. For example, the generation unit can use the generation AI to generate advertising banners and promotional videos based on images and videos provided by the business owner. For example, the generation unit can generate an advertising banner when the generation AI receives a prompt such as "Generate an advertising banner based on this image." The generation unit can also generate articles and blog posts based on text information provided by the business owner using the generation AI. For example, the generation unit can generate a blog post when the generation AI receives a prompt such as "Generate a blog post based on this text." This improves the accuracy and efficiency of content generation by using a generation AI. Some or all of the above processes in the generation unit are performed using a generation AI.

[0082] The integration unit can automatically integrate the generated content with other services. For example, the integration unit stores the generated content in an official account and automatically integrates it with other services. For example, when storing the generated content in an official account, the integration unit uses APIs to integrate with other services. The integration unit can also distribute the generated content to other platforms. For example, the integration unit automatically distributes the generated content to other platforms based on a schedule. This reduces the effort required for integration work by automatically integrating the generated content with other services. Some or all of the above processes in the integration unit may be performed using AI or not.

[0083] The advertising department can deliver advertisements based on the generated content. For example, the advertising department can deliver targeted advertisements based on the generated content. For example, when the advertising department delivers targeted advertisements based on the generated content, it can deliver advertisements based on user attribute information. The advertising department can also deliver retargeting advertisements based on the generated content. For example, when the advertising department delivers retargeting advertisements based on the generated content, it can deliver advertisements based on the user's behavior history. In this way, the effectiveness of advertisements can be maximized by delivering advertisements based on the generated content. Some or all of the above processes in the advertising department may be performed using AI or not.

[0084] The support unit can provide customer support based on the generated content. For example, the support unit can provide customer support using a chatbot based on the generated content. For example, when providing customer support using a chatbot based on the generated content, the support unit will automatically answer user questions. The support unit can also provide email support based on the generated content. For example, when providing email support based on the generated content, the support unit will automatically reply to user inquiries. This improves the efficiency and quality of customer support by providing customer support based on the generated content. Some or all of the above processes in the support unit may be performed using AI or not.

[0085] The selection unit can select promotional proposals and collaboration partners based on the generated content. For example, the selection unit can select campaign plans based on the generated content. For example, when selecting campaign plans based on the generated content, the selection unit can select campaign plans based on user attribute information. The selection unit can also select collaboration partners based on the generated content. For example, when selecting collaboration partners based on the generated content, the selection unit can select industry leaders or startup companies. In this way, by selecting promotional proposals and collaboration partners based on the generated content, it is possible to support business growth. Some or all of the above processing in the selection unit may be performed using AI or not.

[0086] The reception unit can estimate the user's emotions and adjust the timing of material submission based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the submission timing to allow the user to submit the material in a relaxed state. If the user is relaxed, the reception unit can immediately accept the material and begin processing quickly. Conversely, if the user is in a hurry, the reception unit can advance the submission timing to receive the material quickly. By adjusting the timing of material submission according to the user's emotions, the system reduces user stress and achieves efficient material submission. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not.

[0087] The reception department can analyze a user's past material submission history and select the optimal reception method. For example, the reception department may prioritize accepting materials of types that the user has frequently submitted in the past. For example, the reception department may predict materials to be submitted at specific times based on a user's past submission history and concentrate reception during those times. The reception department can also analyze a user's past submission history and suggest the optimal reception method (online, offline, etc.). In this way, by analyzing a user's past material submission history, the reception department can select the optimal reception method and achieve efficient material reception. Some or all of the above processes in the reception department may be performed using AI or not.

[0088] The reception unit can filter materials upon receipt based on the user's current projects and areas of interest. For example, the reception unit can prioritize accepting only materials related to the user's current ongoing projects. For example, the reception unit can filter and accept highly relevant materials based on the user's areas of interest. The reception unit can also prioritize accepting necessary materials according to the progress of the user's projects. This allows for the priority acceptance of highly relevant materials by filtering materials based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI or not.

[0089] The reception desk can estimate the user's emotions and determine the priority of materials to be received based on the estimated emotions. For example, if the user is stressed, the reception desk will accept materials of lower importance later. For example, if the user is relaxed, the reception desk will accept materials of higher importance first. Also, if the user is in a hurry, the reception desk can accept materials of the highest urgency first. This enables efficient material reception by determining the priority of materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI.

[0090] The reception unit can prioritize accepting materials that are highly relevant to the user, taking into account the user's geographical location. For example, if the user is in a specific region, the reception unit will prioritize accepting materials related to that region. For example, based on the user's geographical location, the reception unit can accept materials that match the trends of each region. Furthermore, if the user is on the move, the reception unit can prioritize accepting relevant materials closest to the user's current location. In this way, by accepting materials while considering the user's geographical location, it is possible to prioritize accepting materials that match the trends of each region. Some or all of the above processing in the reception unit may be performed using AI, or it may be performed without using AI.

[0091] The reception desk can analyze the user's social media activity when receiving materials and accept relevant materials. For example, the reception desk can prioritize accepting relevant materials based on content shared by the user on social media. For example, the reception desk can prioritize accepting influential materials by considering the user's number of social media followers and engagement rate. The reception desk can also analyze the content of the user's social media posts and accept relevant materials. In this way, by analyzing the user's social media activity, it is possible to prioritize accepting relevant materials. Some or all of the above processing in the reception desk may be performed using AI or not.

[0092] The generation unit can estimate the user's emotions and adjust the way the generated content is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate content with a soft tone. If the user is excited, for example, the generation unit can generate content using energetic expressions. The generation unit can also generate content with a calm tone if the user is sad. In this way, by adjusting the way the content is expressed according to the user's emotions, more appropriate content can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the generation unit may be performed using AI or not using AI.

[0093] The generation unit can adjust the level of detail of the generated content based on the importance of the materials. For example, for high-importance materials, the generation unit will generate content that includes detailed explanations and high-quality visuals. For example, for low-importance materials, the generation unit will generate content that includes concise explanations and simple visuals. The generation unit can also adjust the length and amount of information in the generated content according to the importance of the materials. This allows for efficient content generation by adjusting the level of detail based on the importance of the materials. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0094] The generation unit can apply different generation algorithms depending on the category of the material when generating content. For example, the generation unit can apply an image processing algorithm to image materials to generate high-quality visual content. For example, the generation unit can apply a video editing algorithm to video materials to generate engaging video content. Furthermore, the generation unit can also apply a natural language processing algorithm to text materials to generate easy-to-read text content. In this way, by applying different generation algorithms depending on the category of material, the optimal content can be generated. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0095] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated emotions. For example, if the user is in a hurry, the generation unit can generate short, concise content. If the user is relaxed, for example, the generation unit can generate longer content with detailed explanations. The generation unit can also generate content with visually stimulating effects if the user is excited. By adjusting the length of the content according to the user's emotions, more appropriate content can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI or not.

[0096] The generation unit can determine the generation priority based on the submission timing of materials when generating content. For example, the generation unit can determine the generation priority based on the order in which materials were submitted. For example, the generation unit can prioritize the generation of materials that are of high urgency. The generation unit can also prioritize the generation of materials submitted within a specific time period based on the submission timing. This allows for efficient content generation by determining the generation priority based on the submission timing of materials. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0097] The generation unit can adjust the generation order based on the relevance of the materials when generating content. For example, the generation unit can prioritize generating highly relevant materials. For example, it can postpone the generation of less relevant materials. The generation unit can also optimize the generation order based on the relevance of the materials. This allows for efficient content generation by adjusting the generation order based on the relevance of the materials. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI.

[0098] The interaction unit can estimate the user's emotions and adjust the interaction criteria based on the estimated emotions. For example, if the user is relaxed, the interaction unit can apply flexible interaction criteria. For example, if the user is tense, the interaction unit can apply strict interaction criteria. The interaction unit can also increase the frequency of interaction if the user is excited. This allows for more appropriate interaction by adjusting the interaction criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI or not using AI.

[0099] The integration unit can improve the accuracy of integration by considering the interrelationships between content during integration. For example, the integration unit prioritizes integrating related content. For example, the integration unit analyzes the interrelationships between content and selects the optimal integration method. The integration unit can also improve the accuracy of integration based on the relevance of content. In this way, the accuracy of integration can be improved by considering the interrelationships between content. Some or all of the above processing in the integration unit may be performed using AI or not.

[0100] The integration unit can perform integration while considering the attribute information of the content submitter. For example, the integration unit can select the optimal integration method based on the submitter's attribute information (age, gender, occupation, etc.). For example, the integration unit can prioritize integrating highly relevant content by considering the submitter's attribute information. The integration unit can also improve the accuracy of integration based on the submitter's attribute information. In this way, optimal integration can be achieved by considering the attribute information of the content submitter. Some or all of the above processing in the integration unit may be performed using AI or not.

[0101] The interaction unit can estimate the user's emotions and adjust the order in which the interaction results are displayed based on the estimated emotions. For example, if the user is relaxed, the interaction unit may prioritize displaying detailed interaction results. If the user is tense, for example, the interaction unit may prioritize displaying concise interaction results. The interaction unit may also prioritize displaying visually stimulating interaction results if the user is excited. By adjusting the order in which the interaction results are displayed according to the user's emotions, more appropriate interaction results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interaction unit may be performed using AI or not using AI.

[0102] The integration unit can perform integration while considering the geographical distribution of content. For example, the integration unit prioritizes integrating geographically close content. For example, the integration unit selects the optimal integration method considering the geographical distribution. Furthermore, the integration unit can improve the accuracy of integration based on the geographical distribution. In this way, optimal integration can be achieved by considering the geographical distribution of content. Some or all of the above processing in the integration unit may be performed using AI or not.

[0103] The integration unit can improve the accuracy of the integration by referring to related literature for the content during the integration process. For example, the integration unit can select the optimal integration method by referring to related literature. For example, the integration unit can improve the accuracy of the integration based on related literature. The integration unit can also optimize the results of the integration by referring to related literature. In this way, the accuracy of the integration can be improved by referring to related literature for the content. Some or all of the above processing in the integration unit may be performed using AI or not.

[0104] The advertising department can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is relaxed, the advertising department can display ads in a soft tone. If the user is excited, the advertising department can display ads using energetic language. Furthermore, if the user is sad, the advertising department can display ads in a calm tone. This allows for more effective advertising by adjusting how ads are displayed according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not.

[0105] The advertising department can predict the current effectiveness of an ad by referring to past advertising data when delivering ads. For example, the advertising department can predict the click-through rate of a current ad based on past advertising data. For example, the advertising department can predict the conversion rate of a current ad by referring to past advertising data. The advertising department can also analyze past advertising data to optimize the effectiveness of current ads. In this way, by referring to past advertising data, it is possible to predict the current effectiveness of an ad and achieve optimal ad delivery. Some or all of the above processes in the advertising department may be performed using AI or not.

[0106] The advertising department can apply different advertising delivery methods to each content category when delivering ads. For example, the advertising department can apply a visually-oriented advertising delivery method to image content. For example, the advertising department can apply a video advertising delivery method to video content. Furthermore, the advertising department can also apply a text advertising delivery method to text content. By applying different advertising delivery methods to each content category, optimal ad delivery can be achieved. Some or all of the above processing in the advertising department may be performed using AI, or not using AI.

[0107] The advertising department can estimate the user's emotions and adjust the importance of ads based on those emotions. For example, if the user is relaxed, the advertising department will prioritize displaying high-importance ads. If the user is excited, the advertising department will prioritize displaying energetic ads. The advertising department can also prioritize displaying calming ads if the user is sad. By adjusting the importance of ads according to the user's emotions, more effective advertising can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not.

[0108] The advertising department can analyze changes in advertisements based on the submission timing of content when delivering ads. For example, the advertising department can analyze changes in the click-through rate of ads based on the submission timing. For example, the advertising department can analyze changes in the conversion rate of ads based on the submission timing. The advertising department can also analyze changes in the effectiveness of ads based on the submission timing. By analyzing changes in advertisements based on the submission timing of content, optimal ad delivery can be achieved. Some or all of the above processes in the advertising department may be performed using AI or not.

[0109] The advertising department can analyze ads by referring to relevant market data for the content when delivering ads. For example, the advertising department can analyze the click-through rate of ads based on relevant market data. For example, the advertising department can analyze the conversion rate of ads by referring to relevant market data. The advertising department can also optimize the effectiveness of ads based on relevant market data. In this way, the effectiveness of ads can be optimized by referring to relevant market data for the content. Some or all of the above processes in the advertising department may be performed using AI or not.

[0110] The response unit can estimate the user's emotions and adjust its customer service approach based on the estimated emotions. For example, if the user is relaxed, the response unit will respond in a soft tone. If the user is excited, the response unit will respond energetically. The response unit can also respond in a calm tone if the user is sad. By adjusting the customer service approach according to the user's emotions, more appropriate customer service can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not using AI.

[0111] The response unit can optimize the current response method by referring to past response data when dealing with customers. For example, the response unit optimizes the current response method based on past response data. For example, the response unit improves the current response method by referring to past response data. The response unit can also analyze past response data and select the optimal response method. As a result, by referring to past response data, the current response method can be optimized, leading to more effective customer service. Some or all of the above processes in the response unit may be performed using AI, or they may not be performed using AI.

[0112] The response unit can apply different response methods depending on the content category when handling customer inquiries. For example, it can apply a visually-oriented response method to image content, a video response method to video content, and a text response method to text content. By applying different response methods to each content category, more appropriate customer service can be achieved. Some or all of the above processing in the response unit may be performed using AI or not.

[0113] The response unit can estimate the user's emotions and determine the priority of customer service based on the estimated emotions. For example, if the user is relaxed, the response unit will prioritize high-priority responses. If the user is excited, the response unit will prioritize energetic responses. The response unit can also prioritize calm-toned responses if the user is sad. By determining the priority of customer service according to the user's emotions, more appropriate customer service can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not.

[0114] The response unit can analyze changes in customer responses based on the timing of content submission. For example, the response unit analyzes changes in responses based on submission timing. For example, the response unit analyzes changes in the effectiveness of responses based on submission timing. The response unit can also optimize responses based on submission timing. This allows for more appropriate customer responses by analyzing changes in responses based on the timing of content submission. Some or all of the above processing in the response unit may be performed using AI or not.

[0115] The response unit can analyze customer interactions by referring to relevant market data for the content. For example, the response unit can analyze the effectiveness of the response based on the relevant market data. For example, the response unit can optimize the response by referring to the relevant market data. The response unit can also improve the response based on the relevant market data. This allows for more appropriate customer interactions by referring to relevant market data for the content. Some or all of the above processing in the response unit may be performed using AI or not.

[0116] The selection unit can estimate the user's emotions and determine the priority of promotional proposals and collaboration partners based on the estimated emotions. For example, if the user is relaxed, the selection unit will prioritize high-priority promotional proposals and collaboration partners. If the user is excited, the selection unit will prioritize energetic promotional proposals and collaboration partners. If the user is sad, the selection unit can also prioritize promotional proposals and collaboration partners with a calm tone. This allows for more appropriate selection by determining the priority of promotional proposals and collaboration partners according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not.

[0117] The selection unit can improve the accuracy of its selection process by considering the interrelationships between content. For example, the selection unit may prioritize selecting related content. For example, the selection unit may analyze the interrelationships between content and select the optimal selection method. The selection unit can also improve the accuracy of its selection process based on the relevance of content. In this way, the accuracy of the selection process can be improved by considering the interrelationships between content. Some or all of the above-described processes in the selection unit may be performed using AI or not.

[0118] The selection unit can make selections while considering the attribute information of the content submitter. For example, the selection unit can select the optimal selection method based on the submitter's attribute information (age, gender, occupation, etc.). For example, the selection unit can prioritize the selection of highly relevant promotional proposals and collaboration partners by considering the submitter's attribute information. The selection unit can also improve the accuracy of the selection based on the submitter's attribute information. In this way, optimal selection can be achieved by considering the attribute information of the content submitter. Some or all of the above processes in the selection unit may be performed using AI or not.

[0119] The selection unit can estimate the user's emotions and adjust the display method of selected promotional proposals and collaborators based on the estimated user emotions. For example, if the user is relaxed, the selection unit can display detailed promotional proposals and collaborators. If the user is excited, the selection unit can display energetic promotional proposals and collaborators. Furthermore, if the user is sad, the selection unit can display promotional proposals and collaborators in a calm tone. In this way, a more appropriate display can be achieved by adjusting the display method of promotional proposals and collaborators according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not using AI.

[0120] The selection unit can consider the geographical distribution of content when making selections. For example, the selection unit may prioritize promotional proposals or collaboration partners that are geographically close. For example, the selection unit may select the optimal selection method considering the geographical distribution. Furthermore, the selection unit can improve the accuracy of selection based on geographical distribution. This makes it possible to achieve optimal selection by considering the geographical distribution of content. Some or all of the above processes in the selection unit may be performed using AI or not.

[0121] The selection unit can improve the accuracy of its selection process by referring to relevant literature for the content. For example, the selection unit can select the optimal selection method by referring to relevant literature. For example, the selection unit can improve the accuracy of its selection based on relevant literature. The selection unit can also optimize the selection results by referring to relevant literature. In this way, the accuracy of the selection can be improved by referring to relevant literature for the content. Some or all of the above processes in the selection unit may be performed using AI or not.

[0122] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0123] The reception unit can estimate the user's emotions and adjust the timing of material submission based on the estimated emotions. For example, if the user is stressed, the submission timing can be delayed to allow the user to submit the material in a relaxed state. If the user is relaxed, the material can be accepted immediately to begin processing quickly. Conversely, if the user is in a hurry, the submission timing can be advanced to receive the material quickly. By adjusting the timing of material submission according to the user's emotions, this reduces user stress and enables efficient material submission. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not.

[0124] The generation unit can estimate the user's emotions and adjust the way the generated content is expressed based on the estimated emotions. For example, if the user is relaxed, it can generate content with a soft tone. If the user is excited, it can generate content using energetic expressions. It can also generate content with a calm tone if the user is sad. By adjusting the way the content is expressed according to the user's emotions, more appropriate content can be generated. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not using AI.

[0125] The interaction unit can estimate the user's emotions and adjust the interaction criteria based on the estimated emotions. For example, if the user is relaxed, flexible interaction criteria may be applied. If the user is tense, strict interaction criteria may be applied. Furthermore, if the user is excited, the frequency of interaction may be increased. This allows for more appropriate interaction by adjusting the interaction criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the interaction unit may be performed using AI or not.

[0126] The advertising department can estimate the user's emotions and adjust how ads are displayed based on those emotions. For example, if the user is relaxed, ads with a soft tone can be displayed. If the user is excited, ads with energetic expressions can be displayed. If the user is sad, ads with a calm tone can also be displayed. By adjusting how ads are displayed according to the user's emotions, more effective advertising can be provided. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the advertising department may be performed using AI or not.

[0127] The response unit can estimate the user's emotions and adjust its customer service approach based on the estimated emotions. For example, if the user is relaxed, it can respond in a soft tone. If the user is excited, it can respond energetically. If the user is sad, it can respond in a calm tone. By adjusting the customer service approach according to the user's emotions, more appropriate customer service can be achieved. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the response unit may be performed using AI or not.

[0128] The reception department can analyze a user's past material submission history and select the optimal reception method. For example, it can prioritize the acceptance of materials that the user has frequently submitted in the past. It can also predict materials that will be submitted during specific time periods based on the user's past submission history and concentrate reception during those times. Furthermore, it can analyze the user's past submission history and suggest the optimal reception method (online, offline, etc.). In this way, by analyzing the user's past material submission history, the optimal reception method can be selected, resulting in efficient material reception. Some or all of the above processes in the reception department may be performed using AI, or they may not.

[0129] The reception unit can filter materials upon receipt based on the user's current projects and areas of interest. For example, it can prioritize receiving only materials related to the user's current project. It can filter and accept materials that are highly relevant based on the user's areas of interest. It can also prioritize receiving necessary materials according to the progress of the user's project. This allows for the priority acceptance of highly relevant materials by filtering materials based on the user's current projects and areas of interest. Some or all of the above processing in the reception unit may be performed using AI, or it may not be performed using AI.

[0130] The generation unit can adjust the level of detail generated based on the importance of the source material during content generation. For example, for highly important source material, it generates content that includes detailed explanations and high-quality visuals. For less important source material, it generates content that includes concise explanations and simple visuals. It can also adjust the length and amount of information in the generated content according to the importance of the source material. This allows for efficient content generation by adjusting the level of detail based on the importance of the source material. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without using a generation AI.

[0131] The integration unit can improve the accuracy of integration by considering the interrelationships between content during the integration process. For example, it can prioritize integrating related content. It can analyze the interrelationships between content and select the optimal integration method. It can also improve the accuracy of integration based on the relevance of content. In this way, the accuracy of integration can be improved by considering the interrelationships between content. Some or all of the above processing in the integration unit may be performed using AI or not.

[0132] The advertising department can predict the current effectiveness of an ad by referring to past advertising data when delivering ads. For example, it can predict the click-through rate of a current ad based on past advertising data. It can also predict the conversion rate of a current ad by referring to past advertising data. Furthermore, it can analyze past advertising data to optimize the effectiveness of the current ad. In this way, by referring to past advertising data, it is possible to predict the current effectiveness of an ad and achieve optimal ad delivery. Some or all of the above processes in the advertising department may be performed using AI, or they may not be performed using AI.

[0133] The following briefly describes the processing flow for example form 2.

[0134] Step 1: The reception desk inputs the materials provided by the business owner. These materials include images, videos, and text. The reception desk provides an interface for uploading images and videos provided by the business owner, as well as a form for entering text information. Step 2: The generation unit uses generation AI to generate content based on the materials entered by the reception unit. For example, it generates advertising banners and promotional videos based on images and videos provided by the business owner, and generates articles and blog posts based on text information. Step 3: The integration unit integrates the content generated by the generation unit with other services. For example, it stores the generated content in an official account and automatically integrates it with other services or distributes it to other platforms. Step 4: The advertising department delivers ads based on the content generated by the content creation department. For example, they deliver targeted ads and retargeting ads based on the generated content. Step 5: The support unit handles customer inquiries based on the content generated by the generation unit. For example, it might use a chatbot or email to handle customer inquiries based on the generated content. Step 6: The selection team selects promotional plans and collaborators based on the content generated by the generation team. For example, they select campaign plans and collaborators based on the generated content.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0137] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0138] Each of the multiple elements described above, including the reception unit, generation unit, collaboration unit, advertising unit, response unit, and selection unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for inputting materials provided by the business owner. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates content using a generation AI. The collaboration unit is implemented by, for example, the control unit 46A of the smart device 14 and collaborates the generated content with other services. The advertising unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and delivers advertisements based on the generated content. The response unit is implemented by, for example, the control unit 46A of the smart device 14 and provides customer support based on the generated content. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects promotional proposals and collaboration partners based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0140] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0141] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0142] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0143] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0145] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0146] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0147] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0148] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0149] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0150] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0151] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0153] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0154] Each of the multiple elements described above, including the reception unit, generation unit, collaboration unit, advertising unit, response unit, and selection unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for inputting materials provided by the business owner. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates content using a generation AI. The collaboration unit is implemented, for example, by the control unit 46A of the smart glasses 214 and collaborates the generated content with other services. The advertising unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and delivers advertisements based on the generated content. The response unit is implemented, for example, by the control unit 46A of the smart glasses 214 and provides customer support based on the generated content. The selection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and selects promotional proposals and collaboration partners based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0156] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0157] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0158] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0159] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0161] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0162] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0163] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0164] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0165] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0167] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0168] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0169] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0170] Each of the multiple elements described above, including the reception unit, generation unit, collaboration unit, advertising unit, response unit, and selection unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for inputting materials provided by the business owner. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates content using a generation AI. The collaboration unit is implemented by, for example, the control unit 46A of the headset terminal 314 and collaborates the generated content with other services. The advertising unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and delivers advertisements based on the generated content. The response unit is implemented by, for example, the control unit 46A of the headset terminal 314 and provides customer support based on the generated content. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects promotional proposals and collaboration partners based on the generated content. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0171] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0172] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0173] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0174] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0175] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0176] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0177] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0178] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0179] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0180] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0181] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0182] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0183] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0184] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0185] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0186] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0187] Each of the multiple elements described above, including the reception unit, generation unit, collaboration unit, advertising unit, response unit, and selection unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for inputting materials provided by the business owner. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates content using a generation AI. The collaboration unit is implemented by, for example, the control unit 46A of the robot 414 and collaborates the generated content with other services. The advertising unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and delivers advertisements based on the generated content. The response unit is implemented by, for example, the control unit 46A of the robot 414 and provides customer support based on the generated content. The selection unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and selects promotional proposals and collaboration partners based on the generated content. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0188] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0189] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0190] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0191] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0192] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0193] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0194] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0195] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0196] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0197] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0198] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0199] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0200] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0201] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0202] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0203] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0204] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0205] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0206] (Note 1) The reception area for inputting materials, A generation unit that generates content based on the materials input by the aforementioned reception unit, A linking unit that links the content generated by the generation unit with other services, An advertising unit delivers advertisements based on the content generated by the aforementioned generation unit, A response unit that handles customer interactions based on the content generated by the generation unit, The system includes a selection unit that selects promotional proposals and collaboration partners based on the content generated by the generation unit. A system characterized by the following features. (Note 2) The generating unit is Generate content using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned linkage unit is, The generated content is automatically integrated with other services. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned advertising department, Ads are delivered based on the generated content. The system described in Appendix 1, characterized by the features described herein. (Note 5) The corresponding part is, Customer support is provided based on the generated content. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned selection unit is Based on the generated content, we select promotional plans and potential partners. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of material submissions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past material submission history to select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving materials, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of the materials to accept based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving materials, the system prioritizes accepting materials that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving material, the system analyzes the user's social media activity and accepts relevant material. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is It estimates user emotions and adjusts how generated content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When generating content, adjust the level of detail based on the importance of the source material. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is When generating content, different generation algorithms are applied depending on the category of the material. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When generating content, the generation priority is determined based on when the materials were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is When generating content, adjust the generation order based on the relevance of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the collaboration criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned linkage unit is, When integrating, we improve the accuracy of the integration by considering the interrelationships between content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned linkage unit is, When integrating, the system takes into account the attribute information of the content submitter. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the order in which the results of the interaction are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned linkage unit is, When integrating, the geographical distribution of the content is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned linkage unit is, When linking, we improve the accuracy of the linking by referring to related literature for the content. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advertising department, It estimates the user's emotions and adjusts how ads are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned advertising department, When delivering ads, past ad data is referenced to predict the current ad's effectiveness. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned advertising department, When delivering ads, different ad delivery methods are applied depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned advertising department, It estimates the user's emotions and adjusts the importance of ads based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned advertising department, When delivering ads, we analyze changes in the ads based on when the content was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned advertising department, When delivering ads, we analyze them by referring to relevant market data for the content. The system described in Appendix 1, characterized by the features described herein. (Note 31) The corresponding part is, It estimates the user's emotions and adjusts customer service methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The corresponding part is, When dealing with customers, we optimize the current response method by referring to past response data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The corresponding part is, When dealing with customers, apply different response methods depending on the content category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The corresponding part is, It estimates the user's emotions and determines the priority of customer service based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The corresponding part is, When dealing with customers, we analyze how responses change based on the timing of content submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The corresponding part is, When interacting with customers, we analyze the interaction by referring to relevant market data for the content. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned selection unit is We estimate user emotions and prioritize promotional proposals and collaboration partners based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned selection unit is When selecting content, consider the interrelationships between them to improve the accuracy of the selection process. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned selection unit is During the selection process, the attribute information of the content submitter will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned selection unit is We estimate user sentiment and adjust the display methods for selected promotional proposals and collaborators based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned selection unit is When making a selection, the geographical distribution of the content should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned selection unit is During the selection process, we refer to relevant literature related to the content to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0207] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area for inputting materials, A generation unit that generates content based on the materials input by the aforementioned reception unit, A linking unit that links the content generated by the generation unit with other services, An advertising unit delivers advertisements based on the content generated by the aforementioned generation unit, A response unit that handles customer interactions based on the content generated by the generation unit, The system includes a selection unit that selects promotional proposals and collaboration partners based on the content generated by the generation unit. A system characterized by the following features.

2. The generating unit is Generate content using generative AI. The system according to feature 1.

3. The aforementioned linkage unit is, The generated content is automatically integrated with other services. The system according to feature 1.

4. The aforementioned advertising department, Ads are delivered based on the generated content. The system according to feature 1.

5. The corresponding part is, Customer support is provided based on the generated content. The system according to feature 1.

6. The aforementioned selection unit is Based on the generated content, we select promotional plans and potential partners. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the user's emotions and adjusts the timing of material submissions based on those emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past material submission history to select the most suitable submission method. The system according to feature 1.

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