system

Abstract

The system according to this embodiment aims to learn the trends of a company's past taglines and generate new taglines. [Solution] The system according to the embodiment comprises an acquisition unit, a learning unit, and a generation unit. The acquisition unit acquires past taglines of companies. The learning unit learns the trends of past taglines acquired by the acquisition unit. The generation unit generates new taglines based on the trends learned by the learning unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 that responds 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, the tendency of a company's past taglines has not been sufficiently learned to effectively generate new taglines, and there is room for improvement.

[0005] The system according to the embodiment aims to learn the tendency of a company's past taglines and generate new taglines.

Means for Solving the Problems

[0006] The system according to the embodiment includes an acquisition unit, a learning unit, and a generation unit. The acquisition unit acquires a company's past taglines. The learning unit learns the tendency of the past taglines acquired by the acquisition unit. The generation unit generates new taglines based on the tendency learned by the learning unit. [Effects of the Invention]

[0007] The system according to this embodiment can learn the trends of a company's past taglines and generate new taglines. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 tagline generation system according to an embodiment of the present invention is a system that analyzes the trends of a company's past taglines and generates new taglines based on the company's strategy and branding intentions. This tagline generation system collects taglines from a company's past advertising campaigns and marketing materials, uses AI to analyze patterns and success stories of past taglines, and learns trends that match the company's strategy and branding requirements. Next, it uses generation AI to generate new taglines based on the company's strategy and branding intentions. This enables creative and effective branding while maintaining consistency in brand messaging. For example, the tagline generation system collects taglines from a company's past advertising campaigns and marketing materials. For example, advertising campaigns include online advertisements, television advertisements, and print advertisements. Marketing materials include presentation materials, marketing reports, and consumer survey results. Next, the tagline generation system learns the trends of the collected past taglines. For example, the AI ​​analyzes frequently occurring keywords, success rates, and consumer responses, and learns trends that match the company's strategy and branding requirements. Next, the tagline generation system generates new taglines based on the learned trends. For example, the generative AI uses natural language generation models and machine learning algorithms to generate new taglines that reflect a company's vision, mission statement, and brand guidelines. The generated taglines are also used in suggesting brand logos and corporate websites, providing a consistent brand message. For instance, the generated taglines are applied to company logos, product logos, and campaign logos. They are also applied to corporate websites such as homepages, product pages, and company information pages. In this way, the tagline generation system learns the trends of a company's past taglines and generates new taglines, achieving creative and effective branding while maintaining consistency in brand messaging.This allows the tagline generation system to learn from the trends of a company's past taglines and generate new taglines, thereby achieving creative and effective branding while maintaining consistency in brand messaging.

[0029] The tagline generation system according to the embodiment comprises an acquisition unit, a learning unit, and a generation unit. The acquisition unit acquires a company's past taglines. A company's past taglines include, but are not limited to, examples such as a specific period, a specific campaign, or a specific product line. The acquisition unit collects taglines from, for example, a company's past advertising campaigns and marketing materials. Advertising campaigns include, for example, online advertisements, television advertisements, and print advertisements. Marketing materials include, for example, presentation materials, marketing reports, and consumer survey results. The learning unit learns the trends of the past taglines acquired by the acquisition unit. The learning unit analyzes patterns and success stories of past taglines using, for example, AI, and learns trends that match the company's strategy and branding requirements. The AI ​​analyzes, for example, frequently occurring keywords, success rates, and consumer responses. The generation unit generates new taglines based on the trends learned by the learning unit. The generation unit generates new taglines based on the company's strategy and branding intentions using, for example, a generation AI. The generation AI uses, for example, a natural language generation model or a machine learning algorithm. The generation unit also utilizes the generated taglines in proposing brand logos and corporate websites. Brand logos include, for example, company logos, product logos, and campaign logos. Corporate websites include, for example, homepages, product pages, and company information pages. As a result, the tagline generation system according to this embodiment learns the trends of a company's past taglines and generates new taglines, thereby achieving creative and effective branding while maintaining consistency in brand messaging.

[0030] The acquisition unit retrieves a company's past taglines. These taglines may include, but are not limited to, specific periods, campaigns, or product lines. The acquisition unit collects taglines from, for example, a company's past advertising campaigns and marketing materials. Advertising campaigns include, for example, online ads, television ads, and print ads. Marketing materials include, for example, presentation materials, marketing reports, and consumer survey results. Specifically, the acquisition unit accesses the company's digital archives and marketing databases to extract data from past advertising campaigns. For online advertising, this includes website banner ads, social media posts, and email marketing content. For television advertising, it includes broadcasted commercials, scripts, and viewership data. For print advertising, it collects images and text data from advertisements published in magazines and newspapers. Regarding marketing materials, if internal presentation materials, marketing reports, and consumer survey results are stored in digital format, this data is retrieved. This allows the acquisition unit to build a comprehensive dataset of a company's past taglines, providing the information necessary for the learning unit's analysis in the next step. Furthermore, the data acquisition unit can utilize web scraping technology and APIs to automate the data collection process. This allows for the acquisition of the latest data in real time, improving the overall efficiency of the system.

[0031] The learning unit learns the trends of past taglines acquired by the acquisition unit. For example, the learning unit uses AI to analyze patterns and success stories of past taglines and learns trends that match the company's strategy and branding requirements. The AI ​​analyzes, for example, frequently occurring keywords, success rates, and consumer responses. Specifically, it uses natural language processing (NLP) technology to analyze the text data of taglines and extract frequently occurring keywords and phrases. Furthermore, it uses machine learning algorithms to evaluate the success rate and consumer responses of past taglines and identify what elements were effective. For example, consumer response data such as social media engagement data, survey results, and sales data can be analyzed to quantitatively evaluate the effectiveness of taglines. Based on this data, the learning unit learns the characteristics of taglines that match the company's brand strategy and marketing objectives and uses this to generate new taglines. Furthermore, the learning unit can also predict future trends by considering past tagline trends and market changes. In this way, the learning unit can provide a foundation for generating creative taglines that are relevant to the times while maintaining consistency in the company's brand message.

[0032] The generation unit generates new taglines based on trends learned by the learning unit. For example, the generation unit uses generative AI to generate new taglines based on a company's strategy and branding intentions. The generative AI uses, for example, natural language generation models and machine learning algorithms. Specifically, the generative AI receives data obtained from the learning unit as input and generates new taglines that match the company's brand message and marketing objectives. The generative AI uses, for example, an advanced natural language generation model such as LLM, and generates multiple candidate taglines by taking specific requirements and keywords from the company as prompts. The generated taglines are also used to propose brand logos and corporate websites. Brand logos include, for example, company logos, product logos, and campaign logos. Corporate websites include, for example, homepages, product pages, and company information pages. When applying the generated taglines to brand logos and corporate websites, the generation unit can also adjust the design and layout. This allows the generation unit to achieve creative and effective branding while maintaining consistency in the company's brand message. Furthermore, the generation unit can evaluate the effectiveness of the generated taglines and regenerate or modify them as needed. This allows the generation unit to consistently provide optimal taglines and support a company's branding strategy.

[0033] The generation unit can generate new taglines based on a company's strategy and branding intentions using generative AI. For example, the generation unit generates new taglines based on a company's strategy and branding intentions using generative AI. The generative AI uses, for example, natural language generation models and machine learning algorithms. The generative AI can generate new taglines that reflect a company's vision, mission statement, brand guidelines, etc. This allows for the generation of new taglines based on a company's strategy and branding intentions. For example, the generative AI takes a company's vision and mission statement as input to reflect a company's strategy and branding intentions and generates taglines based on them. The generative AI considers a company's brand guidelines and generates new taglines while maintaining consistency in brand messaging. The generative AI learns the trends of a company's past taglines and generates new taglines based on those trends. For example, the generative AI analyzes patterns and success stories of past taglines and generates new taglines based on them. To reflect a company's strategy and branding intentions, the generative AI takes a company's vision and mission statement as input and generates taglines based on them. Generative AI considers a company's brand guidelines and generates new taglines while maintaining consistency in brand messaging. It learns the trends of a company's past taglines and generates new ones based on those trends. For example, it analyzes past tagline patterns and success stories and generates new taglines based on them. This allows for the generation of new taglines that align with a company's strategy and branding intentions.

[0034] The acquisition unit can collect taglines from a company's past advertising campaigns and marketing materials. For example, the acquisition unit collects taglines from a company's past advertising campaigns and marketing materials. Advertising campaigns include, for example, online ads, television ads, and print ads. Marketing materials include, for example, presentation materials, marketing reports, and consumer survey results. By collecting taglines from a company's past advertising campaigns and marketing materials, data for learning past tagline trends can be obtained. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input a company's past advertising campaigns and marketing materials into an AI and have the AI ​​perform the tagline collection. By collecting taglines from a company's past advertising campaigns and marketing materials, data for learning past tagline trends can be obtained.

[0035] The learning unit can analyze past tagline patterns and specific success criteria to learn trends that match the company's strategy and branding requirements. Patterns include, for example, frequently occurring phrases, structural features, and consumer responses. Success criteria include, for example, increased sales, improved brand awareness, and consumer favorability. This allows the learning unit to learn trends that match the company's strategy and branding requirements by analyzing past tagline patterns and success stories. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can input past tagline patterns and success stories into an AI and have the AI ​​learn the trends. This allows companies to learn trends that align with their strategic and branding requirements by analyzing past tagline patterns and success stories.

[0036] The generation unit can also utilize the generated taglines in proposals for brand logos and corporate websites. For example, the generation unit can utilize the generated taglines in proposals for brand logos and corporate websites. Brand logos include, for example, company logos, product logos, and campaign logos. Corporate websites include, for example, homepages, product pages, and company information pages. By utilizing the generated taglines in proposals for brand logos and corporate websites, a consistent brand message can be provided. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the generated taglines into a generation AI and have the generation AI execute proposals for brand logos and corporate websites. The generation unit can input the generated taglines into a generation AI and have the generation AI execute proposals for brand logos and corporate websites. This allows the generated tagline to be used in proposals for brand logos and corporate websites, thereby providing a consistent brand message.

[0037] The acquisition unit can evaluate the effectiveness of a company's past advertising campaigns and preferentially acquire taglines from campaigns with high specific effectiveness criteria. For example, the acquisition unit can evaluate the click-through rate of past advertising campaigns and acquire taglines from campaigns with high click-through rates. For example, the acquisition unit can evaluate the sales growth rate of past advertising campaigns and acquire taglines from campaigns with high sales growth rates. For example, the acquisition unit can evaluate the number of social media shares of past advertising campaigns and acquire taglines from campaigns with a large number of shares. This allows the company to evaluate the effectiveness of its past advertising campaigns and preferentially acquire taglines from campaigns with high effectiveness. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data from past advertising campaigns into AI and have the AI ​​perform the effectiveness evaluation. The acquisition unit can input data from past advertising campaigns into AI and have the AI ​​perform the effectiveness evaluation. This allows the company to evaluate the effectiveness of its past advertising campaigns and preferentially acquire taglines from campaigns with high effectiveness.

[0038] The acquisition unit can filter taglines based on the company's current market situation and the actions of its competitors when acquiring them. For example, the acquisition unit can consider the company's current market share and prioritize acquiring taglines that are effective in markets where the share is low. For example, the acquisition unit can analyze the latest advertising campaigns of competitors and acquire taglines that differentiate the company from competitors. For example, the acquisition unit can consider current market trends and prioritize acquiring taglines that are in line with those trends. By filtering taglines while considering the company's current market situation and the actions of its competitors, it is possible to acquire more effective taglines. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the company's market situation and competitors into AI and have the AI ​​perform the filtering. The acquisition unit can input data on the company's market situation and competitors into AI and have the AI ​​perform the filtering. This allows companies to filter taglines by considering their current market conditions and the actions of their competitors, thereby obtaining more effective taglines.

[0039] The acquisition unit can prioritize acquiring taglines that are highly relevant, taking into account the company's geographical market presence when acquiring taglines. For example, the acquisition unit can prioritize acquiring taglines that are tailored to the culture and language of a region in which the company is newly entering. For example, the acquisition unit can acquire effective taglines in similar markets based on successful examples in markets where the company already has a strong presence. For example, the acquisition unit can analyze consumer behavior in the region targeted by the company and acquire highly relevant taglines. This allows for the acquisition of more relevant taglines by taking into account the company's geographical market presence. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the company's geographical market data into AI and have the AI ​​perform tagline acquisition. The acquisition unit can input the company's geographical market data into AI and have the AI ​​perform tagline acquisition. This allows for the acquisition of more relevant taglines by taking into account the company's geographical market presence.

[0040] The acquisition unit can analyze a company's social media activities and acquire relevant taglines when acquiring taglines. For example, the acquisition unit can analyze popular posts on a company's social media and acquire relevant taglines. For example, the acquisition unit can analyze the reactions of followers on a company's social media and acquire taglines that receive many positive reactions. For example, the acquisition unit can acquire relevant taglines based on successful campaigns on a company's social media. In this way, relevant taglines can be acquired by analyzing a company's social media activities. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input a company's social media data into AI and have the AI ​​acquire taglines. The acquisition unit can input a company's social media data into AI and have the AI ​​acquire taglines. In this way, relevant taglines can be acquired by analyzing a company's social media activities.

[0041] The learning unit can identify success factors by comparing and analyzing past tagline successes and failures during the learning process. For example, the learning unit can compare and analyze successes and failures based on the click-through rate of past taglines. For example, the learning unit can compare and analyze successes and failures based on the sales growth rate of past taglines. For example, the learning unit can compare and analyze successes and failures based on the number of social media shares of past taglines. By doing so, success factors can be identified by comparing and analyzing past tagline successes and failures. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past tagline data into AI and have the AI ​​identify success factors. The learning unit can input past tagline data into AI and have the AI ​​identify success factors. By doing so, success factors can be identified by comparing and analyzing past tagline successes and failures.

[0042] The learning unit can optimize the learning algorithm based on the history of changes in a company's brand strategy during training. For example, the learning unit can adjust the parameters of the learning algorithm based on the history of changes in a company's brand strategy. For example, the learning unit can weight the training data based on the history of changes in a company's brand strategy. For example, the learning unit can optimize the feedback loop of the learning algorithm based on the history of changes in a company's brand strategy. This allows for more effective learning by optimizing the learning algorithm while considering the history of changes in a company's brand strategy. Some or all of the above processes in the learning unit may be performed using AI, or without AI. For example, the learning unit can input the history of changes in a company's brand strategy into AI and have the AI ​​optimize the learning algorithm. This allows for more effective learning by optimizing the learning algorithm while considering the history of changes in a company's brand strategy.

[0043] The learning unit can weight the training data based on fluctuations in a company's market share during training. For example, if a company's market share is increasing, the learning unit can give a higher weight to success stories. For example, if a company's market share is decreasing, the learning unit can give a higher weight to failure stories. For example, if a company's market share is stable, the learning unit can perform a balanced weighting. This allows for more effective learning by weighting the training data while considering fluctuations in a company's market share. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input company market share data into AI and have the AI ​​perform the weighting. The learning unit can input company market share data into AI and have the AI ​​perform the weighting. This allows for more effective learning by weighting the training data while considering fluctuations in a company's market share.

[0044] The learning unit can adjust its learning algorithm based on the company's industry trends during the learning process. For example, the learning unit can adjust the parameters of the learning algorithm based on industry trends. For example, the learning unit can select learning data based on industry trends. For example, the learning unit can optimize the feedback loop of the learning algorithm based on industry trends. This allows for more effective learning by adjusting the learning algorithm in reference to the company's industry trends. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input industry trend data into the AI ​​and have the AI ​​perform the algorithm adjustments. This allows for more effective learning by adjusting the learning algorithm in reference to the company's industry trends.

[0045] The generation unit can check for consistency with past taglines during generation to maintain consistency in the company's brand message. For example, the generation unit can check for keyword matching between past and new taglines. For example, the generation unit can check for tone and style matching between past and new taglines. For example, the generation unit can check for theme matching between past and new taglines. This ensures consistency in the brand message by checking for consistency with past taglines. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past tagline data into AI and have the AI ​​perform consistency checks. This ensures consistency in the brand message by checking for consistency with past taglines.

[0046] The generation unit can customize the tagline during generation, taking into account the characteristics of the company's target market. For example, the generation unit can use language appropriate to the age group of the target market. For example, the generation unit can use expressions that match the culture and values ​​of the target market. For example, the generation unit can include messages that match the purchasing behavior of the target market. This allows for the generation of more effective taglines by customizing them to take into account the characteristics of the company's target market. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input target market data into AI and have the AI ​​perform the tagline customization. The generation unit can input target market data into AI and have the AI ​​perform the tagline customization. This allows for the generation of more effective taglines by customizing them to take into account the characteristics of the company's target market.

[0047] The generation unit can determine the priority of taglines based on the company's advertising campaign schedule during generation. For example, the generation unit can generate taglines to match the most recent advertising campaign. For example, the generation unit can generate taglines considering the long-term advertising campaign schedule. For example, the generation unit can generate taglines tailored to seasons or events. This allows for the generation of more effective taglines by determining the priority of taglines based on the company's advertising campaign schedule. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input the advertising campaign schedule into AI and have the AI ​​prioritize the taglines. This allows for the generation of more effective taglines by determining the priority of taglines based on the company's advertising campaign schedule.

[0048] The generation unit can incorporate elements to differentiate itself by referencing the taglines of its competitors during the generation process. For example, the generation unit can analyze the keywords of competitors' taglines and incorporate new keywords to differentiate itself. For example, the generation unit can analyze the tone and style of competitors' taglines and incorporate its own tone and style. For example, the generation unit can analyze the themes of competitors' taglines and incorporate new themes to differentiate itself. This allows for the generation of more effective taglines by incorporating elements to differentiate itself by referencing the taglines of its competitors. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on competitors' taglines into AI and have the AI ​​implement the elements of differentiation. The generation unit can input data on competitors' taglines into AI and have the AI ​​implement the elements of differentiation. This allows companies to create more effective taglines by referencing the taglines of their competitors and incorporating elements to differentiate themselves.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The tagline generation system can further compare and analyze past successful and unsuccessful taglines of a company to identify success factors. For example, it can compare and analyze successful and unsuccessful taglines based on their click-through rates, sales growth rates, and social media shares. By comparing and analyzing past successful and unsuccessful taglines, the system can identify success factors and generate more effective taglines.

[0051] The tagline generation system can further filter taglines based on a company's current market situation and competitor activities. For example, it can consider a company's current market share and prioritize taglines that are effective in markets where the share is low. It can analyze the latest advertising campaigns of competitors and acquire taglines that differentiate the company from competitors. It can consider current market trends and prioritize taglines that align with those trends. In this way, by filtering taglines while considering a company's current market situation and competitor activities, it is possible to acquire more effective taglines.

[0052] The tagline generation system can also prioritize acquiring highly relevant taglines by considering a company's geographical market presence. For example, it can prioritize acquiring taglines tailored to the culture and language of a newly entered region. It can acquire effective taglines for similar markets based on a company's success in existing markets. It can analyze consumer behavior in target regions and acquire highly relevant taglines. By considering a company's geographical market presence when acquiring taglines, it can acquire more relevant taglines.

[0053] The tagline generation system can further analyze a company's social media activities and retrieve relevant taglines. For example, it can analyze a company's popular social media posts and retrieve relevant taglines. It can analyze the reactions of a company's social media followers and retrieve taglines that receive many positive responses. It can retrieve relevant taglines based on successful examples of a company's social media campaigns. In short, by analyzing a company's social media activities, it can retrieve relevant taglines.

[0054] The tagline generation system can further optimize its learning algorithm based on the history of changes in a company's brand strategy. For example, the parameters of the learning algorithm can be adjusted based on the history of changes in the company's brand strategy. The weighting of the training data can be adjusted based on the history of changes in the company's brand strategy. The feedback loop of the learning algorithm can be optimized based on the history of changes in the company's brand strategy. As a result, by optimizing the learning algorithm while considering the history of changes in the company's brand strategy, more effective learning can be achieved.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The acquisition team retrieves the company's past taglines. Past taglines include specific periods, campaigns, and product lines. The acquisition team collects taglines from the company's past advertising campaigns and marketing materials. Advertising campaigns include online ads, television ads, and print ads, while marketing materials include presentation materials, marketing reports, and consumer survey results. Step 2: The learning unit learns the trends of past taglines acquired by the acquisition unit. The learning unit uses AI to analyze patterns and success stories of past taglines and learns trends that match the company's strategy and branding requirements. The AI ​​analyzes frequently occurring keywords, success rates, consumer responses, etc. Step 3: The generation unit generates new taglines based on the trends learned by the learning unit. The generation unit uses generative AI to generate new taglines based on the company's strategy and branding intentions. The generative AI uses natural language generation models and machine learning algorithms. The generation unit also uses the generated taglines to propose brand logos and corporate websites. Brand logos include company logos, product logos, and campaign logos, while corporate websites include homepages, product pages, and company information pages.

[0057] (Example of form 2)The tagline generation system according to an embodiment of the present invention is a system that analyzes the trends of a company's past taglines and generates new taglines based on the company's strategy and branding intentions. This tagline generation system collects taglines from a company's past advertising campaigns and marketing materials, uses AI to analyze patterns and success stories of past taglines, and learns trends that match the company's strategy and branding requirements. Next, it uses generation AI to generate new taglines based on the company's strategy and branding intentions. This enables creative and effective branding while maintaining consistency in brand messaging. For example, the tagline generation system collects taglines from a company's past advertising campaigns and marketing materials. For example, advertising campaigns include online advertisements, television advertisements, and print advertisements. Marketing materials include presentation materials, marketing reports, and consumer survey results. Next, the tagline generation system learns the trends of the collected past taglines. For example, the AI ​​analyzes frequently occurring keywords, success rates, and consumer responses, and learns trends that match the company's strategy and branding requirements. Next, the tagline generation system generates new taglines based on the learned trends. For example, the generative AI uses natural language generation models and machine learning algorithms to generate new taglines that reflect a company's vision, mission statement, and brand guidelines. The generated taglines are also used in suggesting brand logos and corporate websites, providing a consistent brand message. For instance, the generated taglines are applied to company logos, product logos, and campaign logos. They are also applied to corporate websites such as homepages, product pages, and company information pages. In this way, the tagline generation system learns the trends of a company's past taglines and generates new taglines, achieving creative and effective branding while maintaining consistency in brand messaging.This allows the tagline generation system to learn from the trends of a company's past taglines and generate new taglines, thereby achieving creative and effective branding while maintaining consistency in brand messaging.

[0058] The tagline generation system according to the embodiment comprises an acquisition unit, a learning unit, and a generation unit. The acquisition unit acquires a company's past taglines. A company's past taglines include, but are not limited to, examples such as a specific period, a specific campaign, or a specific product line. The acquisition unit collects taglines from, for example, a company's past advertising campaigns and marketing materials. Advertising campaigns include, for example, online advertisements, television advertisements, and print advertisements. Marketing materials include, for example, presentation materials, marketing reports, and consumer survey results. The learning unit learns the trends of the past taglines acquired by the acquisition unit. The learning unit analyzes patterns and success stories of past taglines using, for example, AI, and learns trends that match the company's strategy and branding requirements. The AI ​​analyzes, for example, frequently occurring keywords, success rates, and consumer responses. The generation unit generates new taglines based on the trends learned by the learning unit. The generation unit generates new taglines based on the company's strategy and branding intentions using, for example, a generation AI. The generation AI uses, for example, a natural language generation model or a machine learning algorithm. The generation unit also utilizes the generated taglines in proposing brand logos and corporate websites. Brand logos include, for example, company logos, product logos, and campaign logos. Corporate websites include, for example, homepages, product pages, and company information pages. As a result, the tagline generation system according to this embodiment learns the trends of a company's past taglines and generates new taglines, thereby achieving creative and effective branding while maintaining consistency in brand messaging.

[0059] The acquisition unit retrieves a company's past taglines. These taglines may include, but are not limited to, specific periods, campaigns, or product lines. The acquisition unit collects taglines from, for example, a company's past advertising campaigns and marketing materials. Advertising campaigns include, for example, online ads, television ads, and print ads. Marketing materials include, for example, presentation materials, marketing reports, and consumer survey results. Specifically, the acquisition unit accesses the company's digital archives and marketing databases to extract data from past advertising campaigns. For online advertising, this includes website banner ads, social media posts, and email marketing content. For television advertising, it includes broadcasted commercials, scripts, and viewership data. For print advertising, it collects images and text data from advertisements published in magazines and newspapers. Regarding marketing materials, if internal presentation materials, marketing reports, and consumer survey results are stored in digital format, this data is retrieved. This allows the acquisition unit to build a comprehensive dataset of a company's past taglines, providing the information necessary for the learning unit's analysis in the next step. Furthermore, the data acquisition unit can utilize web scraping technology and APIs to automate the data collection process. This allows for the acquisition of the latest data in real time, improving the overall efficiency of the system.

[0060] The learning unit learns the trends of past taglines acquired by the acquisition unit. For example, the learning unit uses AI to analyze patterns and success stories of past taglines and learns trends that match the company's strategy and branding requirements. The AI ​​analyzes, for example, frequently occurring keywords, success rates, and consumer responses. Specifically, it uses natural language processing (NLP) technology to analyze the text data of taglines and extract frequently occurring keywords and phrases. Furthermore, it uses machine learning algorithms to evaluate the success rate and consumer responses of past taglines and identify what elements were effective. For example, consumer response data such as social media engagement data, survey results, and sales data can be analyzed to quantitatively evaluate the effectiveness of taglines. Based on this data, the learning unit learns the characteristics of taglines that match the company's brand strategy and marketing objectives and uses this to generate new taglines. Furthermore, the learning unit can also predict future trends by considering past tagline trends and market changes. In this way, the learning unit can provide a foundation for generating creative taglines that are relevant to the times while maintaining consistency in the company's brand message.

[0061] The generation unit generates new taglines based on trends learned by the learning unit. For example, the generation unit uses generative AI to generate new taglines based on a company's strategy and branding intentions. The generative AI uses, for example, natural language generation models and machine learning algorithms. Specifically, the generative AI receives data obtained from the learning unit as input and generates new taglines that match the company's brand message and marketing objectives. The generative AI uses, for example, an advanced natural language generation model such as LLM, and generates multiple candidate taglines by taking specific requirements and keywords from the company as prompts. The generated taglines are also used to propose brand logos and corporate websites. Brand logos include, for example, company logos, product logos, and campaign logos. Corporate websites include, for example, homepages, product pages, and company information pages. When applying the generated taglines to brand logos and corporate websites, the generation unit can also adjust the design and layout. This allows the generation unit to achieve creative and effective branding while maintaining consistency in the company's brand message. Furthermore, the generation unit can evaluate the effectiveness of the generated taglines and regenerate or modify them as needed. This allows the generation unit to consistently provide optimal taglines and support a company's branding strategy.

[0062] The generation unit can generate new taglines based on a company's strategy and branding intentions using generative AI. For example, the generation unit generates new taglines based on a company's strategy and branding intentions using generative AI. The generative AI uses, for example, natural language generation models and machine learning algorithms. The generative AI can generate new taglines that reflect a company's vision, mission statement, brand guidelines, etc. This allows for the generation of new taglines based on a company's strategy and branding intentions. For example, the generative AI takes a company's vision and mission statement as input to reflect a company's strategy and branding intentions and generates taglines based on them. The generative AI considers a company's brand guidelines and generates new taglines while maintaining consistency in brand messaging. The generative AI learns the trends of a company's past taglines and generates new taglines based on those trends. For example, the generative AI analyzes patterns and success stories of past taglines and generates new taglines based on them. To reflect a company's strategy and branding intentions, the generative AI takes a company's vision and mission statement as input and generates taglines based on them. Generative AI considers a company's brand guidelines and generates new taglines while maintaining consistency in brand messaging. It learns the trends of a company's past taglines and generates new ones based on those trends. For example, it analyzes past tagline patterns and success stories and generates new taglines based on them. This allows for the generation of new taglines that align with a company's strategy and branding intentions.

[0063] The acquisition unit can collect taglines from a company's past advertising campaigns and marketing materials. For example, the acquisition unit collects taglines from a company's past advertising campaigns and marketing materials. Advertising campaigns include, for example, online ads, television ads, and print ads. Marketing materials include, for example, presentation materials, marketing reports, and consumer survey results. By collecting taglines from a company's past advertising campaigns and marketing materials, data for learning past tagline trends can be obtained. Some or all of the above processing in the acquisition unit may be performed using, for example, AI, or not using AI. For example, the acquisition unit can input a company's past advertising campaigns and marketing materials into an AI and have the AI ​​perform the tagline collection. By collecting taglines from a company's past advertising campaigns and marketing materials, data for learning past tagline trends can be obtained.

[0064] The learning unit can analyze past tagline patterns and specific success criteria to learn trends that match the company's strategy and branding requirements. Patterns include, for example, frequently occurring phrases, structural features, and consumer responses. Success criteria include, for example, increased sales, improved brand awareness, and consumer favorability. This allows the learning unit to learn trends that match the company's strategy and branding requirements by analyzing past tagline patterns and success stories. Some or all of the above processing in the learning unit may be performed using, for example, AI, or not. For example, the learning unit can input past tagline patterns and success stories into an AI and have the AI ​​learn the trends. This allows companies to learn trends that align with their strategic and branding requirements by analyzing past tagline patterns and success stories.

[0065] The generation unit can also utilize the generated taglines in proposals for brand logos and corporate websites. For example, the generation unit can utilize the generated taglines in proposals for brand logos and corporate websites. Brand logos include, for example, company logos, product logos, and campaign logos. Corporate websites include, for example, homepages, product pages, and company information pages. By utilizing the generated taglines in proposals for brand logos and corporate websites, a consistent brand message can be provided. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can input the generated taglines into a generation AI and have the generation AI execute proposals for brand logos and corporate websites. The generation unit can input the generated taglines into a generation AI and have the generation AI execute proposals for brand logos and corporate websites. This allows the generated tagline to be used in proposals for brand logos and corporate websites, thereby providing a consistent brand message.

[0066] The acquisition unit can estimate the user's emotions and adjust the timing of tagline acquisition based on the estimated user emotions. For example, if the user is stressed, the acquisition unit can delay the acquisition timing to acquire the tagline when the user is relaxed. For example, if the user is focused, the acquisition unit can acquire the tagline immediately and quickly move on to the next step. For example, if the user is tired, the acquisition unit can adjust the timing to acquire the tagline after a break. In this way, by adjusting the timing of tagline acquisition based on the user's emotions, the tagline can be acquired at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the user's emotion data into the generative AI and have the generative AI perform emotion estimation. Furthermore, the acquisition unit can analyze the user's emotion data in real time and immediately grasp changes in emotions. For example, the acquisition unit monitors the user's facial expressions in real time and instantly detects changes in emotion. The acquisition unit can also accumulate user emotion data and analyze long-term emotional trends. For instance, it can analyze user emotion data over time to identify patterns of emotional change. This allows for a detailed understanding of the user's emotions and enables adjustment of the timing of tagline acquisition.

[0067] The acquisition unit can evaluate the effectiveness of a company's past advertising campaigns and preferentially acquire taglines from campaigns with high specific effectiveness criteria. For example, the acquisition unit can evaluate the click-through rate of past advertising campaigns and acquire taglines from campaigns with high click-through rates. For example, the acquisition unit can evaluate the sales growth rate of past advertising campaigns and acquire taglines from campaigns with high sales growth rates. For example, the acquisition unit can evaluate the number of social media shares of past advertising campaigns and acquire taglines from campaigns with a large number of shares. This allows the company to evaluate the effectiveness of its past advertising campaigns and preferentially acquire taglines from campaigns with high effectiveness. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data from past advertising campaigns into AI and have the AI ​​perform the effectiveness evaluation. The acquisition unit can input data from past advertising campaigns into AI and have the AI ​​perform the effectiveness evaluation. This allows the company to evaluate the effectiveness of its past advertising campaigns and preferentially acquire taglines from campaigns with high effectiveness.

[0068] The acquisition unit can filter taglines based on the company's current market situation and the actions of its competitors when acquiring them. For example, the acquisition unit can consider the company's current market share and prioritize acquiring taglines that are effective in markets where the share is low. For example, the acquisition unit can analyze the latest advertising campaigns of competitors and acquire taglines that differentiate the company from competitors. For example, the acquisition unit can consider current market trends and prioritize acquiring taglines that are in line with those trends. By filtering taglines while considering the company's current market situation and the actions of its competitors, it is possible to acquire more effective taglines. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input data on the company's market situation and competitors into AI and have the AI ​​perform the filtering. The acquisition unit can input data on the company's market situation and competitors into AI and have the AI ​​perform the filtering. This allows companies to filter taglines by considering their current market conditions and the actions of their competitors, thereby obtaining more effective taglines.

[0069] The acquisition unit can estimate the user's emotions and specifically determine the priority of taglines to acquire based on the estimated user emotions. For example, if the user has positive emotions, the acquisition unit can prioritize acquiring creative and challenging taglines. For example, if the user has negative emotions, the acquisition unit can prioritize acquiring reassuring taglines. For example, if the user has neutral emotions, the acquisition unit can prioritize acquiring balanced taglines. In this way, by determining the priority of taglines based on the user's emotions, more appropriate taglines can be acquired. 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 acquisition unit may be performed using AI, for example, or not using AI. For example, the acquisition unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. Furthermore, the data acquisition unit can analyze user emotional data in real time and instantly grasp changes in emotion. For example, the unit monitors the user's facial expressions in real time and instantly detects changes in emotion. The data acquisition unit can also accumulate user emotional data and analyze long-term emotional trends. For example, the unit analyzes user emotional data over time and identifies patterns of emotional change. This allows for a detailed understanding of user emotions and enables the prioritization of taglines.

[0070] The acquisition unit can prioritize acquiring taglines that are highly relevant, taking into account the company's geographical market presence when acquiring taglines. For example, the acquisition unit can prioritize acquiring taglines that are tailored to the culture and language of a region in which the company is newly entering. For example, the acquisition unit can acquire effective taglines in similar markets based on successful examples in markets where the company already has a strong presence. For example, the acquisition unit can analyze consumer behavior in the region targeted by the company and acquire highly relevant taglines. This allows for the acquisition of more relevant taglines by taking into account the company's geographical market presence. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input the company's geographical market data into AI and have the AI ​​perform tagline acquisition. The acquisition unit can input the company's geographical market data into AI and have the AI ​​perform tagline acquisition. This allows for the acquisition of more relevant taglines by taking into account the company's geographical market presence.

[0071] The acquisition unit can analyze a company's social media activities and acquire relevant taglines when acquiring taglines. For example, the acquisition unit can analyze popular posts on a company's social media and acquire relevant taglines. For example, the acquisition unit can analyze the reactions of followers on a company's social media and acquire taglines that receive many positive reactions. For example, the acquisition unit can acquire relevant taglines based on successful campaigns on a company's social media. In this way, relevant taglines can be acquired by analyzing a company's social media activities. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can input a company's social media data into AI and have the AI ​​acquire taglines. The acquisition unit can input a company's social media data into AI and have the AI ​​acquire taglines. In this way, relevant taglines can be acquired by analyzing a company's social media activities.

[0072] The learning unit can estimate the user's emotions and specifically select training data based on the estimated user emotions. For example, if the user has positive emotions, the learning unit can select training data that includes many success stories. For example, if the user has negative emotions, the learning unit can select training data that includes many failure stories and learn areas for improvement. For example, if the user has neutral emotions, the learning unit can select balanced training data. This allows for more effective learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. Furthermore, the learning unit can analyze the user's emotion data in real time and immediately grasp changes in emotions. For example, the learning unit monitors the user's facial expressions in real time and instantly detects changes in emotion. The learning unit can also accumulate user emotion data and analyze long-term emotional trends. For instance, it can analyze user emotion data over time to identify patterns of emotional change. This allows for a detailed understanding of the user's emotions and the selection of appropriate training data.

[0073] The learning unit can identify success factors by comparing and analyzing past tagline successes and failures during the learning process. For example, the learning unit can compare and analyze successes and failures based on the click-through rate of past taglines. For example, the learning unit can compare and analyze successes and failures based on the sales growth rate of past taglines. For example, the learning unit can compare and analyze successes and failures based on the number of social media shares of past taglines. By doing so, success factors can be identified by comparing and analyzing past tagline successes and failures. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input past tagline data into AI and have the AI ​​identify success factors. The learning unit can input past tagline data into AI and have the AI ​​identify success factors. By doing so, success factors can be identified by comparing and analyzing past tagline successes and failures.

[0074] The learning unit can optimize the learning algorithm based on the history of changes in a company's brand strategy during training. For example, the learning unit can adjust the parameters of the learning algorithm based on the history of changes in a company's brand strategy. For example, the learning unit can weight the training data based on the history of changes in a company's brand strategy. For example, the learning unit can optimize the feedback loop of the learning algorithm based on the history of changes in a company's brand strategy. This allows for more effective learning by optimizing the learning algorithm while considering the history of changes in a company's brand strategy. Some or all of the above processes in the learning unit may be performed using AI, or without AI. For example, the learning unit can input the history of changes in a company's brand strategy into AI and have the AI ​​optimize the learning algorithm. This allows for more effective learning by optimizing the learning algorithm while considering the history of changes in a company's brand strategy.

[0075] The learning unit can estimate the user's emotions and adjust the learning frequency specifically based on the estimated emotions. For example, if the user has positive emotions, the learning unit can increase the learning frequency. For example, if the user has negative emotions, the learning unit can decrease the learning frequency. For example, if the user has neutral emotions, the learning unit can balance the learning frequency. This allows for more effective learning by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, or not using AI. For example, the learning unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. Furthermore, the learning unit can analyze the user's emotion data in real time and immediately grasp changes in emotions. For example, the learning unit monitors the user's facial expressions in real time and instantly detects changes in emotion. The learning unit can also accumulate user emotion data and analyze long-term emotional trends. For instance, it can analyze user emotion data over time to identify patterns of emotional change. This allows for a detailed understanding of the user's emotions and adjustments to the learning frequency.

[0076] The learning unit can weight the training data based on fluctuations in a company's market share during training. For example, if a company's market share is increasing, the learning unit can give a higher weight to success stories. For example, if a company's market share is decreasing, the learning unit can give a higher weight to failure stories. For example, if a company's market share is stable, the learning unit can perform a balanced weighting. This allows for more effective learning by weighting the training data while considering fluctuations in a company's market share. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input company market share data into AI and have the AI ​​perform the weighting. The learning unit can input company market share data into AI and have the AI ​​perform the weighting. This allows for more effective learning by weighting the training data while considering fluctuations in a company's market share.

[0077] The learning unit can adjust its learning algorithm based on the company's industry trends during the learning process. For example, the learning unit can adjust the parameters of the learning algorithm based on industry trends. For example, the learning unit can select learning data based on industry trends. For example, the learning unit can optimize the feedback loop of the learning algorithm based on industry trends. This allows for more effective learning by adjusting the learning algorithm in reference to the company's industry trends. Some or all of the above processes in the learning unit may be performed using AI, or not. For example, the learning unit can input industry trend data into the AI ​​and have the AI ​​perform the algorithm adjustments. This allows for more effective learning by adjusting the learning algorithm in reference to the company's industry trends.

[0078] The generation unit can estimate the user's emotions and specifically adjust the expression of the tagline it generates based on the estimated user emotions. For example, if the user has positive emotions, the generation unit can use a bright and positive expression. For example, if the user has negative emotions, the generation unit can use a reassuring expression. For example, if the user has neutral emotions, the generation unit can use a balanced expression. By adjusting the expression of the tagline based on the user's emotions, it is possible to generate more appropriate taglines. 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 such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation. Furthermore, the generation unit can analyze the user's emotion data in real time and immediately grasp changes in emotions. For example, the generation unit monitors the user's facial expressions in real time and instantly detects changes in emotion. The generation unit can also accumulate user emotion data and analyze long-term emotional trends. For instance, it can analyze user emotion data over time to identify patterns of emotional change. This allows for a detailed understanding of the user's emotions and adjustments to how the tagline is expressed.

[0079] The generation unit can check for consistency with past taglines during generation to maintain consistency in the company's brand message. For example, the generation unit can check for keyword matching between past and new taglines. For example, the generation unit can check for tone and style matching between past and new taglines. For example, the generation unit can check for theme matching between past and new taglines. This ensures consistency in the brand message by checking for consistency with past taglines. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input past tagline data into AI and have the AI ​​perform consistency checks. This ensures consistency in the brand message by checking for consistency with past taglines.

[0080] The generation unit can customize the tagline during generation, taking into account the characteristics of the company's target market. For example, the generation unit can use language appropriate to the age group of the target market. For example, the generation unit can use expressions that match the culture and values ​​of the target market. For example, the generation unit can include messages that match the purchasing behavior of the target market. This allows for the generation of more effective taglines by customizing them to take into account the characteristics of the company's target market. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input target market data into AI and have the AI ​​perform the tagline customization. The generation unit can input target market data into AI and have the AI ​​perform the tagline customization. This allows for the generation of more effective taglines by customizing them to take into account the characteristics of the company's target market.

[0081] The generation unit can estimate the user's emotions and specifically adjust the length of the tagline it generates based on the estimated emotions. For example, if the user has positive emotions, the generation unit can generate a longer tagline. For example, if the user has negative emotions, the generation unit can generate a shorter tagline. For example, if the user has neutral emotions, the generation unit can generate a tagline of balanced length. By adjusting the tagline length based on the user's emotions, it is possible to generate more appropriate taglines. 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 such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generation AI and have the generation AI perform emotion estimation. Furthermore, the generation unit can analyze the user's emotion data in real time and immediately grasp changes in emotions. For example, the generation unit monitors the user's facial expressions in real time and instantly detects changes in emotion. The generation unit can also accumulate user emotion data and analyze long-term emotional trends. For instance, it can analyze user emotion data over time to identify patterns of emotional change. This allows for a detailed understanding of the user's emotions and enables adjustment of tagline length.

[0082] The generation unit can determine the priority of taglines based on the company's advertising campaign schedule during generation. For example, the generation unit can generate taglines to match the most recent advertising campaign. For example, the generation unit can generate taglines considering the long-term advertising campaign schedule. For example, the generation unit can generate taglines tailored to seasons or events. This allows for the generation of more effective taglines by determining the priority of taglines based on the company's advertising campaign schedule. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input the advertising campaign schedule into AI and have the AI ​​prioritize the taglines. This allows for the generation of more effective taglines by determining the priority of taglines based on the company's advertising campaign schedule.

[0083] The generation unit can incorporate elements to differentiate itself by referencing the taglines of its competitors during the generation process. For example, the generation unit can analyze the keywords of competitors' taglines and incorporate new keywords to differentiate itself. For example, the generation unit can analyze the tone and style of competitors' taglines and incorporate its own tone and style. For example, the generation unit can analyze the themes of competitors' taglines and incorporate new themes to differentiate itself. This allows for the generation of more effective taglines by incorporating elements to differentiate itself by referencing the taglines of its competitors. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on competitors' taglines into AI and have the AI ​​implement the elements of differentiation. The generation unit can input data on competitors' taglines into AI and have the AI ​​implement the elements of differentiation. This allows companies to create more effective taglines by referencing the taglines of their competitors and incorporating elements to differentiate themselves.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The tagline generation system can further estimate the user's emotions and adjust the tone of the tagline based on those emotions. For example, if the user has positive emotions, it can generate a bright and positive-toned tagline. If the user has negative emotions, it can generate a reassuring-toned tagline. If the user has neutral emotions, it can generate a balanced-toned tagline. This allows for the generation of taglines with appropriate tones that match the user's emotions, resulting in more effective branding.

[0086] The tagline generation system can further compare and analyze past successful and unsuccessful taglines of a company to identify success factors. For example, it can compare and analyze successful and unsuccessful taglines based on their click-through rates, sales growth rates, and social media shares. By comparing and analyzing past successful and unsuccessful taglines, the system can identify success factors and generate more effective taglines.

[0087] The tagline generation system can further filter taglines based on a company's current market situation and competitor activities. For example, it can consider a company's current market share and prioritize taglines that are effective in markets where the share is low. It can analyze the latest advertising campaigns of competitors and acquire taglines that differentiate the company from competitors. It can consider current market trends and prioritize taglines that align with those trends. In this way, by filtering taglines while considering a company's current market situation and competitor activities, it is possible to acquire more effective taglines.

[0088] The tagline generation system can further estimate the user's emotions and adjust the timing of tagline acquisition based on those emotions. For example, if the user is stressed, the acquisition timing can be delayed so that the user can acquire the tagline when they are relaxed. If the user is focused, the tagline can be acquired immediately so that they can quickly move on to the next step. If the user is tired, the timing can be adjusted so that the tagline is acquired after a break. In this way, by adjusting the timing of tagline acquisition based on the user's emotions, the tagline can be acquired at a more appropriate time.

[0089] The tagline generation system can also prioritize acquiring highly relevant taglines by considering a company's geographical market presence. For example, it can prioritize acquiring taglines tailored to the culture and language of a newly entered region. It can acquire effective taglines for similar markets based on a company's success in existing markets. It can analyze consumer behavior in target regions and acquire highly relevant taglines. By considering a company's geographical market presence when acquiring taglines, it can acquire more relevant taglines.

[0090] The tagline generation system can further estimate the user's emotions and adjust the way the generated tagline is expressed based on those estimated emotions. For example, if the user has positive emotions, a bright and positive expression can be used. If the user has negative emotions, a reassuring expression can be used. If the user has neutral emotions, a balanced expression can be used. By adjusting the tagline expression based on the user's emotions, it is possible to generate more appropriate taglines.

[0091] The tagline generation system can further analyze a company's social media activities and retrieve relevant taglines. For example, it can analyze a company's popular social media posts and retrieve relevant taglines. It can analyze the reactions of a company's social media followers and retrieve taglines that receive many positive responses. It can retrieve relevant taglines based on successful examples of a company's social media campaigns. In short, by analyzing a company's social media activities, it can retrieve relevant taglines.

[0092] The tagline generation system can further estimate the user's emotions and adjust the length of the generated tagline based on those emotions. For example, if the user has positive emotions, a longer tagline can be generated. If the user has negative emotions, a shorter tagline can be generated. If the user has neutral emotions, a tagline of balanced length can be generated. This allows for the generation of more appropriate taglines by adjusting the tagline length based on the user's emotions.

[0093] The tagline generation system can further optimize its learning algorithm based on the history of changes in a company's brand strategy. For example, the parameters of the learning algorithm can be adjusted based on the history of changes in the company's brand strategy. The weighting of the training data can be adjusted based on the history of changes in the company's brand strategy. The feedback loop of the learning algorithm can be optimized based on the history of changes in the company's brand strategy. As a result, by optimizing the learning algorithm while considering the history of changes in the company's brand strategy, more effective learning can be achieved.

[0094] The tagline generation system can further estimate the user's emotions and select training data based on those emotions. For example, if the user has positive emotions, it can select training data that includes many success stories. If the user has negative emotions, it can select training data that includes many failure stories to learn areas for improvement. If the user has neutral emotions, it can select balanced training data. By selecting training data based on the user's emotions, the system can achieve more effective learning.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The acquisition team retrieves the company's past taglines. Past taglines include specific periods, campaigns, and product lines. The acquisition team collects taglines from the company's past advertising campaigns and marketing materials. Advertising campaigns include online ads, television ads, and print ads, while marketing materials include presentation materials, marketing reports, and consumer survey results. Step 2: The learning unit learns the trends of past taglines acquired by the acquisition unit. The learning unit uses AI to analyze patterns and success stories of past taglines and learns trends that match the company's strategy and branding requirements. The AI ​​analyzes frequently occurring keywords, success rates, consumer responses, etc. Step 3: The generation unit generates new taglines based on the trends learned by the learning unit. The generation unit uses generative AI to generate new taglines based on the company's strategy and branding intentions. The generative AI uses natural language generation models and machine learning algorithms. The generation unit also uses the generated taglines to propose brand logos and corporate websites. Brand logos include company logos, product logos, and campaign logos, while corporate websites include homepages, product pages, and company information pages.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] For example, the acquisition unit can collect taglines from a company's past advertising campaigns and marketing materials using the camera 42 and microphone 38B of the smart device 14. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to analyze patterns and success stories of past taglines and learn trends that match the company's strategy and branding requirements. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses generation AI to generate new taglines based on the company's strategy and branding intentions. The generated taglines can be used, for example, through the output device 40 of the smart device 14 to propose brand logos and corporate websites. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be modified in various ways.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] For example, the acquisition unit can collect taglines from a company's past advertising campaigns and marketing materials using the camera 42 and microphone 238 of the smart glasses 214. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to analyze patterns and success stories of past taglines and learn trends that match the company's strategy and branding requirements. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses generation AI to generate new taglines based on the company's strategy and branding intentions. The generated taglines can also be used, for example, through the speaker 240 of the smart glasses 214 to suggest brand logos and corporate websites. The correspondence between each unit and the device and control unit is not limited to the example described above, and various changes are possible.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] For example, the acquisition unit can collect taglines from a company's past advertising campaigns and marketing materials using the camera 42 and microphone 238 of the headset terminal 314. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to analyze patterns and success stories of past taglines and learn trends that match the company's strategy and branding requirements. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses generation AI to generate new taglines based on the company's strategy and branding intentions. The generated taglines can be used, for example, through the display 343 of the headset terminal 314 to suggest brand logos and corporate websites. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] For example, the acquisition unit can collect taglines from a company's past advertising campaigns and marketing materials using the camera 42 and microphone 238 of the robot 414. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses AI to analyze patterns and success stories of past taglines and learn trends that match the company's strategy and branding requirements. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and uses generation AI to generate new taglines based on the company's strategy and branding intentions. The generated taglines can also be used, for example, through the speaker 240 and display device of the robot 414 to propose brand logos and corporate websites. The correspondence between each unit and the device and control unit is not limited to the example described above and can be modified in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) The acquisition unit retrieves past taglines of companies, A learning unit that learns the trends of past taglines acquired by the acquisition unit, The system comprises a generation unit that generates new taglines based on trends learned by the learning unit. A system characterized by the following features. (Note 2) The generating unit is Generating new taglines based on a company's strategy and branding intentions using generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) The acquisition unit is, Collect taglines from a company's past advertising campaigns and marketing materials. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned learning unit, By analyzing past tagline patterns and specific success criteria, we learn trends that align with the company's strategy and branding requirements. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is The generated tagline will also be used in proposals for brand logos and corporate websites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The acquisition unit is, It estimates the user's emotions and adjusts the timing of tagline acquisition based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The acquisition unit is, We evaluate the effectiveness of a company's past advertising campaigns and prioritize acquiring taglines from campaigns with high performance based on specific criteria. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, When acquiring taglines, filtering is performed based on the company's current market situation and the activities of its competitors. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, It estimates user sentiment and specifically determines the priority of taglines to acquire based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When acquiring taglines, we prioritize selecting highly relevant taglines that take into account the company's geographical market presence. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, When acquiring taglines, we analyze a company's social media activities and retrieve relevant taglines. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned learning unit, The system estimates the user's emotions and then specifically selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned learning unit, During the learning process, past successful and unsuccessful examples of taglines are compared and analyzed to identify the factors for success. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned learning unit, During training, the learning algorithm is optimized based on the history of changes in the company's brand strategy. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency specifically based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned learning unit, During training, the training data is weighted based on fluctuations in the market share of companies. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned learning unit, During training, the learning algorithm is adjusted based on the company's industry trends. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is This involves estimating user sentiment and specifically adjusting the way taglines are expressed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is During generation, the system checks for consistency with past taglines to maintain the company's brand message. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the tagline is customized to take into account the characteristics of the company's target market. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is It estimates the user's emotions and specifically adjusts the length of the tagline generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is During generation, taglines are prioritized based on the company's advertising campaign schedule. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, incorporate elements to differentiate the company by referencing the taglines of its competitors. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0169] 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 acquisition unit retrieves past taglines of companies, A learning unit that learns the trends of past taglines acquired by the acquisition unit, The system comprises a generation unit that generates new taglines based on trends learned by the learning unit. A system characterized by the following features.

2. The generating unit is Generating new taglines based on a company's strategy and branding intentions using generative AI. The system according to feature 1.

3. The acquisition unit is, Collect taglines from a company's past advertising campaigns and marketing materials. The system according to feature 1.

4. The aforementioned learning unit, By analyzing past tagline patterns and specific success criteria, we learn trends that align with the company's strategy and branding requirements. The system according to feature 1.

5. The generating unit is The generated tagline will also be used in proposals for brand logos and corporate websites. The system according to feature 1.

6. The acquisition unit is, It estimates the user's emotions and adjusts the timing of tagline acquisition based on the estimated user emotions. The system according to feature 1.

7. The acquisition unit is, We evaluate the effectiveness of a company's past advertising campaigns and prioritize acquiring taglines from campaigns with high performance based on specific criteria. The system according to feature 1.

8. The acquisition unit is, When acquiring taglines, filtering is performed based on the company's current market situation and the activities of its competitors. The system according to feature 1.

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