System

Abstract

An object of the system according to the embodiment is to effectively generate a new tag line based on a strategy or a branding intention of a company.SOLUTION: A system includes a collection unit, an analysis unit, a reception unit, a generation unit, and a provision unit. The collection part collects data of past tag lines. The analysis unit analyzes the data collected by the collection unit. The reception unit receives a strategy and a branding intention of a company as an input. The generation unit generates a new tag line based on the tendency of the tag line learned by the analysis unit and the strategy and the branding intention of the company received by the reception unit. The provision unit provides the tag line generated by the generation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of making it difficult to effectively generate new taglines based on a company's strategy and branding intentions.

[0005] The system according to the embodiment aims to effectively generate new taglines based on a company's strategy and branding intentions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a reception unit, a generation unit, and a provision unit. The collection unit collects data on past taglines. The analysis unit analyzes the data collected by the collection unit. The reception unit receives as input the company's strategy and branding intentions. The generation unit generates a new tagline based on the tagline trends learned by the analysis unit and the company's strategy and branding intentions received by the reception unit. The provision unit provides the tagline generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively generate new taglines based on a company's strategy and branding intentions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A tagline generation system according to an embodiment of the present invention learns trends in past taglines and generates new taglines based on a company's strategy and branding intentions. The tagline generation system collects data on past taglines and uses a generation AI to learn these trends. Next, the company's strategy and branding intentions are accepted as input, and the generation AI generates a new tagline based on this information. The generated tagline achieves creative and effective branding while maintaining the consistency of the brand message. For example, the tagline generation system collects taglines used in the past by the company and taglines used by competitors. This data is input into the generation AI to learn tagline trends. Next, the company's mission statement, vision, target market, brand values, etc. are accepted as input. This information is input into the generation AI and serves as the basis for generating a new tagline. The generation AI generates a new tagline based on trends in past taglines and the company's strategy and branding intentions. For example, if a company emphasizes "innovation," the generation AI generates a creative tagline based on the theme of "innovation." The generated tagline achieves creative and effective branding while maintaining the consistency of the brand message. This allows the tagline generator system to achieve creative and effective branding while allowing companies to maintain a consistent brand message.This allows the tagline generator system to achieve creative and effective branding while allowing companies to maintain a consistent brand message.

[0029] A tagline generation system according to an embodiment includes a collection unit, an analysis unit, a reception unit, a generation unit, and a provision unit. The collection unit collects data on past taglines. The collection unit can collect, for example, data such as taglines used by a company in the past and taglines used by competitors. The collection unit can collect, for example, taglines from advertising campaigns and product catchphrases. The analysis unit analyzes the data collected by the collection unit. The analysis unit learns tagline trends based on the collected data, for example, using text mining or natural language processing technology. The analysis unit can perform, for example, extraction of frequently occurring keywords and topic modeling. The reception unit receives, as input, a company's strategy and branding intentions. The reception unit can receive, for example, a company's mission statement, vision, target market, brand values, etc. The reception unit can receive, for example, a document describing a company's mission and objectives, or a document describing a company's future goals and direction. The generation unit generates a new tagline based on the tagline trends learned by the analysis unit and the company's strategy and branding intentions received by the reception unit. The generation unit generates the tagline using a generation AI. The generation AI generates the tagline using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if a company emphasizes "innovation," the generation unit can generate a creative tagline with an "innovation" theme. The provision unit provides the tagline generated by the generation unit. For example, the provision unit can provide the generated tagline to the company. For example, the provision unit can provide the tagline by setting the provision format, provision timing, etc. As a result, the tagline generation system according to the embodiment learns the trends of past taglines and generates a new tagline based on the company's strategy and branding intentions, thereby achieving creative and effective branding while maintaining consistency in the brand message.

[0030] The collection unit can collect data on taglines used in the past by a company or taglines of competitors. The collection unit, for example, collects taglines used in the past by a company. For example, the collection unit can collect taglines from advertising campaigns and product slogans. The collection unit can also collect taglines from competitors. For example, the collection unit can collect taglines from advertising campaigns of competitors in the same industry. By collecting data on taglines used in the past by a company or taglines from competitors, tagline trends can be learned based on a wider variety of data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on taglines used in the past by a company into a generation AI to collect data for learning tagline trends.

[0031] The analysis unit can learn tagline trends based on the collected data. The analysis unit, for example, learns tagline trends based on the collected data. For example, the analysis unit can analyze the collected data using text mining or natural language processing technology. The analysis unit can also perform frequently occurring keyword extraction and topic modeling. For example, the analysis unit can extract frequently occurring keywords from the collected data and learn tagline trends. The analysis unit can also learn tagline trends using topic modeling. This enables more accurate tagline generation by learning tagline trends based on the collected data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI to learn tagline trends.

[0032] The reception unit can accept a company's mission statement or vision, a target market, and brand values ​​as input. The reception unit, for example, accepts a company's mission statement as input. For example, the reception unit can accept a document describing the company's mission or objectives. The reception unit can also accept a company's vision as input. For example, the reception unit can accept a document describing the company's future goals and direction. The reception unit can also accept a target market as input. For example, the reception unit can accept customer segments, geographical market characteristics, etc. as input. The reception unit can also accept brand values ​​as input. For example, the reception unit can accept a brand's core values, corporate culture, etc. as input. By accepting a company's mission statement, vision, target market, brand values, etc. as input, tagline generation based on the company's strategy and branding intentions becomes possible. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception department can input a company's mission statement, vision, target market, and brand values ​​into the generation AI and accept them as basic data for tagline generation.

[0033] The generation unit can generate a new tagline based on past tagline trends and the company's strategy and branding intentions. The generation unit generates a new tagline based on, for example, past tagline trends and the company's strategy and branding intentions. The generation unit generates the tagline using a generation AI. The generation AI can generate the tagline using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if a company emphasizes "innovation," the generation unit can generate a creative tagline based on the theme of "innovation." The generation unit can also generate a new tagline based on the company's past tagline. For example, if a company's past tagline was "Create the Future," the generation unit can generate a new tagline based on "Create the Future." By generating a new tagline based on past tagline trends and the company's strategy and branding intentions, creative and effective branding can be achieved while maintaining consistency in the brand message. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation department can input past tagline trends and a company's strategy and branding intentions into the generation AI to generate a new tagline.

[0034] The providing unit can provide the generated tagline to a company. For example, the providing unit provides the generated tagline to a company. The providing unit can provide the tagline by setting, for example, the format and timing of provision. For example, the providing unit can provide the generated tagline to a company through a web application or a mobile application. The providing unit can also send the generated tagline to a company by email. For example, the providing unit can provide the generated tagline to a company in PDF format or text format. This allows a company to quickly use a new tagline by providing the generated tagline to a company. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated tagline into a generation AI to generate data to be provided to a company.

[0035] The collection unit can analyze the success of a company's past marketing campaigns and determine the priority of tagline data to be collected. For example, the collection unit can analyze the success of a company's past marketing campaigns and determine the priority of tagline data to be collected. For example, the collection unit can prioritize collecting taglines that were highly successful in past campaigns. The collection unit can also prioritize collecting taglines from campaigns that were moderately successful. The collection unit can also postpone collecting taglines from campaigns that were less successful. In this way, by analyzing the success of a company's past marketing campaigns, more effective tagline data can be prioritized. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the success of a company's past marketing campaigns into a generation AI to generate data for determining the priority of tagline data to be collected.

[0036] The collection unit can filter tagline data based on the company's industry trends or market trends when collecting the tagline data. For example, the collection unit can filter tagline data taking into account the company's industry trends or market trends when collecting the tagline data. For example, the collection unit can prioritize collecting taglines that match current market trends. The collection unit can also filter and collect highly relevant taglines based on industry trends. The collection unit can also exclude taglines that do not match market trends or industry trends from the collection targets. In this way, by filtering taking into account the company's industry trends or market trends, more relevant tagline data can be collected. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input company's industry trends or market trend data into a generation AI to generate data for filtering.

[0037] The collection unit can monitor the activities of the company's competitors in real time when collecting tagline data and update the collected data. For example, the collection unit can monitor the activities of the company's competitors in real time when collecting tagline data and update the collected data. For example, the collection unit can immediately collect data when a competitor announces a new tagline. Furthermore, if a competitor's campaign is successful, the collection unit can prioritize collecting that tagline. The collection unit can also monitor the activities of competitors in real time and update the collected data as needed. In this way, by monitoring the activities of the company's competitors in real time and updating the collected data, tagline data can be collected based on the latest information. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input trend data of the company's competitors into a generation AI and generate data for updating the collected data.

[0038] When collecting tagline data, the collection unit may prioritize collecting highly relevant data by taking into account the geographical market characteristics of the company. For example, when collecting tagline data, the collection unit may prioritize collecting highly relevant data by taking into account the geographical market characteristics of the company. For example, if the company's main market is North America, the collection unit may prioritize collecting taglines related to the North American market. Furthermore, if the company is expanding into an emerging market, the collection unit may prioritize collecting taglines related to that market. Furthermore, the collection unit may filter and collect the most relevant taglines based on the geographical market characteristics of the company. This allows for more effective collection of tagline data by prioritizing the collection of highly relevant data by taking into account the geographical market characteristics of the company. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the company's geographical market characteristic data into a generation AI to generate data for preferentially collecting highly relevant data.

[0039] The collection unit may analyze the company's social media activities and collect related data when collecting tagline data. For example, the collection unit may analyze the company's social media activities and collect related data when collecting tagline data. For example, the collection unit may collect taglines related to the company's popular posts on social media. The collection unit may also analyze the reactions of the company's followers on social media and collect related taglines. The collection unit may also filter and collect the most effective taglines based on the company's social media activities. In this way, by analyzing the company's social media activities and collecting related data, more effective tagline data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the company's social media activity data into a generation AI to generate data for collecting related data.

[0040] The collection unit can customize the collection method by reflecting the company's past feedback when collecting tagline data. For example, the collection unit can customize the collection method by reflecting the company's past feedback when collecting tagline data. For example, the collection unit can prioritize collecting taglines that the company has received high ratings for in the past. The collection unit can also adjust the collection method based on the company's past feedback and collect optimal taglines. The collection unit can also improve the quality of the collected data by reflecting the company's past feedback. In this way, more effective tagline data can be collected by customizing the collection method by reflecting the company's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's past feedback data into a generation AI to generate data for customizing the collection method.

[0041] The analysis unit can evaluate the success and influence of taglines during analysis and reflect the evaluation in the analysis results. For example, the analysis unit can evaluate the success and influence of taglines during analysis and reflect the evaluation in the analysis results. For example, the analysis unit can focus its analysis on taglines that have been highly successful in past campaigns. The analysis unit can also evaluate the influence of taglines and reflect the evaluation in the analysis results. The analysis unit can also exclude taglines with low success from the analysis results. In this way, more effective analysis results can be obtained by evaluating the success and influence of taglines. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the success and influence of taglines into a generation AI to generate data to be reflected in the analysis results.

[0042] The analysis unit can analyze trends by taking into account the frequency and duration of tagline usage during analysis. For example, the analysis unit can analyze trends by taking into account the frequency and duration of tagline usage during analysis. For example, the analysis unit can focus its analysis on taglines that are used frequently. The analysis unit can also analyze trends of taglines that have been used for a long period of time. The analysis unit can also identify the most effective taglines based on the frequency and duration of usage. This enables more accurate trend analysis by taking into account the frequency and duration of tagline usage. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the frequency and duration of tagline usage into a generation AI to generate data for analyzing trends.

[0043] The analysis unit can adjust the analysis algorithm during analysis, taking into account the linguistic features and cultural background of the tagline. For example, the analysis unit can adjust the analysis algorithm during analysis, taking into account the linguistic features and cultural background of the tagline. For example, the analysis unit can apply an optimal analysis algorithm based on the linguistic features. The analysis unit can also adjust the analysis results by taking into account the cultural background. The analysis unit can also identify the most effective tagline based on the linguistic features and cultural background. In this way, more appropriate analysis results can be obtained by taking into account the linguistic features and cultural background of the tagline. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the linguistic features and cultural background of the tagline into a generation AI to generate data for adjusting the analysis algorithm.

[0044] The analysis unit may perform the analysis while taking into account the geographical usage of the tagline. For example, the analysis unit may perform the analysis while taking into account the geographical usage of the tagline. For example, the analysis unit may focus on taglines that are used over a wide geographical area. The analysis unit may also analyze trends in taglines that are successful in a specific region. The analysis unit may also identify the most effective tagline based on the geographical usage. This allows for more effective analysis results to be obtained by taking into account the geographical usage of the tagline. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input geographical usage data of the tagline into a generation AI to generate data for analysis.

[0045] The analysis unit can improve the accuracy of the analysis by referring to literature and research data related to the tagline during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to literature and research data related to the tagline during analysis. For example, the analysis unit can adjust the analysis algorithm by referring to related literature. The analysis unit can also complement the analysis results based on research data. The analysis unit can also identify the most effective tagline based on related literature and research data. By referring to related literature and research data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input literature and research data related to the tagline into a generation AI to generate data for improving the accuracy of the analysis.

[0046] The analysis unit can perform the analysis taking into account the market value of the tagline. For example, the analysis unit can perform the analysis taking into account the market value of the tagline. For example, the analysis unit can focus the analysis on taglines with high market value. The analysis unit can also evaluate market value and reflect it in the analysis results. The analysis unit can also identify the most effective tagline based on market value. In this way, more effective analysis results can be obtained by taking the market value of the tagline into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market value data of the tagline into a generation AI to generate data for analysis.

[0047] The reception unit can, at the time of reception, appropriate the input content based on the company's past strategies or branding intentions. For example, at the time of reception, the reception unit optimizes the input content by referring to the company's past strategies and branding intentions. The reception unit can, for example, suggest optimal input content based on the company's past strategies. The reception unit can also customize the input content by referring to the company's past branding intentions. The reception unit can also identify the most effective input content based on the company's past strategies and branding intentions. In this way, more effective input content can be provided by referring to the company's past strategies and branding intentions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the company's past strategies and branding intention data into a generation AI to generate data for optimizing the input content.

[0048] The reception unit can customize the input content at the time of reception, taking into account the company's current market situation and the trends of competitors. For example, the reception unit customizes the input content at the time of reception, taking into account the company's current market situation and the trends of competitors. For example, the reception unit can suggest optimal input content based on the company's current market situation. The reception unit can also customize the input content by taking into account the trends of competitors. The reception unit can also identify the most effective input content based on the company's current market situation and the trends of competitors. This makes it possible to provide more effective input content by taking into account the company's current market situation and the trends of competitors. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the company's current market situation and the trends of competitors into a generation AI to generate data for customizing the input content.

[0049] The reception unit can improve the input method by reflecting the company's feedback when receiving the data. The reception unit can improve the input method by reflecting the company's feedback when receiving the data, for example. The reception unit can adjust the input method based on the company's past feedback, for example. The reception unit can also provide the optimal input method by reflecting the company's feedback. The reception unit can also improve the input method and identify the most effective input content based on the company's feedback. In this way, a more effective input method can be provided by reflecting the company's feedback. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the company's feedback data into a generation AI to generate data for improving the input method.

[0050] The reception unit can customize the input content at the time of reception, taking into account the geographical market characteristics of the company. For example, the reception unit customizes the input content at the time of reception, taking into account the geographical market characteristics of the company. For example, if the company's main market is North America, the reception unit can preferentially receive input content related to the North American market. Furthermore, if the company is expanding into an emerging market, the reception unit can customize the input content related to that market. Furthermore, the reception unit can filter and receive the most relevant input content based on the geographical market characteristics of the company. This makes it possible to provide more effective input content by taking into account the geographical market characteristics of the company. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the company's geographical market characteristic data into a generation AI to generate data for customizing the input content.

[0051] The reception unit can analyze the company's social media activity at the time of reception and suggest related input content. For example, the reception unit can analyze the company's social media activity at the time of reception and suggest related input content. For example, the reception unit can suggest input content related to popular posts on the company's social media. The reception unit can also analyze the reactions of the company's followers on social media and suggest related input content. The reception unit can also suggest the most effective input content based on the company's social media activity. In this way, more effective input content can be provided by analyzing the company's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the company's social media activity data into a generation AI to generate data for suggesting related input content.

[0052] The reception unit can customize the input method by reflecting the company's past feedback when receiving the data. For example, the reception unit customizes the input method by reflecting the company's past feedback when receiving the data. For example, the reception unit can preferentially provide input methods that have received high ratings from the company in the past. The reception unit can also adjust the input method based on the company's past feedback and provide optimal input content. The reception unit can also improve the quality of the input data by reflecting the company's past feedback. In this way, a more effective input method can be provided by reflecting the company's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the company's past feedback data into a generation AI to generate data for customizing the input method.

[0053] The generation unit can evaluate the creativity and effectiveness of a tagline at the time of generation and reflect the evaluation in the generation result. For example, the generation unit can evaluate the creativity and effectiveness of a tagline at the time of generation and reflect the evaluation in the generation result. For example, the generation unit can preferentially generate taglines with a high creativity level. The generation unit can also evaluate taglines with high effectiveness and reflect the evaluation in the generation result. The generation unit can also generate the most effective tagline based on the creativity and effectiveness. In this way, by evaluating the creativity and effectiveness of taglines, more effective taglines can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on the creativity and effectiveness of a tagline into a generation AI and generate data to be reflected in the generation result.

[0054] The generation unit can apply different generation algorithms depending on the category or theme of the tagline during generation. For example, the generation unit can apply different generation algorithms depending on the category or theme of the tagline during generation. The generation unit can apply the optimal generation algorithm depending on the category, for example. The generation unit can also apply different generation algorithms based on the theme. The generation unit can also select the most effective generation algorithm depending on the category or theme. In this way, by applying different generation algorithms depending on the category or theme of the tagline, more appropriate taglines can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input tagline category or theme data into a generation AI to generate data for applying different generation algorithms.

[0055] The generation unit can improve the accuracy of tagline generation by referring to the company's past tagline results during generation. For example, the generation unit can improve the accuracy of tagline generation by referring to the company's past tagline results during generation. For example, the generation unit can apply an optimal generation algorithm based on the company's past tagline results. The generation unit can also improve the accuracy of tagline generation by referring to the past tagline results. The generation unit can also generate the most effective tagline based on the company's past tagline results. This improves the accuracy of tagline generation by referring to the company's past tagline results. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the company's past tagline result data into the generation AI to generate data for improving the accuracy of tagline generation.

[0056] The generation unit can determine the generation priority based on the submission time of the tagline at the time of generation. The generation unit, for example, determines the generation priority based on the submission time of the tagline at the time of generation. The generation unit can, for example, prioritize generating taglines that will be submitted soon. The generation unit can also adjust the generation priority based on the submission time. The generation unit can also postpone generating taglines that will be submitted further in the future. In this way, by determining the generation priority based on the submission time of the tagline, taglines can be generated at a more appropriate time. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input tagline submission time data into a generation AI to generate data for determining the generation priority.

[0057] The generation unit can adjust the order of tagline generation based on the relevance of the taglines during generation. The generation unit, for example, can adjust the order of tagline generation based on the relevance of the taglines during generation. The generation unit can, for example, prioritize generating highly relevant taglines. The generation unit can also adjust the order of tagline generation based on the relevance. The generation unit can also postpone generating taglines with low relevance. In this way, by adjusting the order of tagline generation based on the relevance of the taglines, more effective taglines can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input tagline relevance data into a generation AI to generate data for adjusting the order of tagline generation.

[0058] The generation unit may adjust the use of terminology in the tagline to be generated according to the company's level of expertise during generation. For example, the generation unit may adjust the use of terminology in the tagline to be generated according to the company's level of expertise during generation. For example, if the company's level of expertise is high, the generation unit may generate a tagline that uses a lot of terminology. Alternatively, if the company's level of expertise is low, the generation unit may generate a simple, easy-to-understand tagline. The generation unit may also adjust the use of optimal terminology according to the company's level of expertise. In this way, by adjusting the use of terminology according to the company's level of expertise, a more appropriate tagline can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input company's level of expertise data into the generation AI to generate data for adjusting the use of terminology.

[0059] The providing unit can select the optimal delivery method by referring to the company's past feedback at the time of provision. For example, the providing unit can select the optimal delivery method by referring to the company's past feedback at the time of provision. For example, the providing unit can select the optimal delivery method based on the company's past feedback. The providing unit can also adjust the delivery method by reflecting the company's feedback. The providing unit can also select the most effective delivery method based on the company's past feedback. In this way, by referring to the company's past feedback, a more effective delivery method can be selected. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's past feedback data into a generation AI to generate data for selecting the optimal delivery method.

[0060] The provision unit can customize the content to be provided according to the company's current tasks and projects at the time of provision. For example, the provision unit customizes the content to be provided according to the company's current tasks and projects at the time of provision. For example, the provision unit can propose optimal content to be provided based on the company's current tasks. The provision unit can also customize the content to be provided according to the company's current projects. The provision unit can also identify the most effective content to be provided based on the company's current tasks and projects. This allows for more effective information to be provided by customizing the content to be provided according to the company's current tasks and projects. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data on the company's current tasks and projects into a generation AI to generate data for customizing the content to be provided.

[0061] The provision unit can improve the provision method by reflecting the company's feedback at the time of provision. For example, the provision unit can improve the provision method by reflecting the company's feedback at the time of provision. For example, the provision unit can adjust the provision method based on the company's past feedback. The provision unit can also provide the optimal provision method by reflecting the company's feedback. The provision unit can also improve the provision method and identify the most effective provision content based on the company's feedback. In this way, a more effective provision method can be provided by reflecting the company's feedback. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the company's feedback data into a generation AI to generate data for improving the provision method.

[0062] The providing unit can select the optimal delivery method by taking into account the company's device information at the time of provision. For example, the providing unit selects the optimal delivery method by taking into account the company's device information at the time of provision. For example, if the company uses a smartphone, the providing unit can provide a delivery method tailored to the screen size. Furthermore, if the company uses a tablet, the providing unit can provide a delivery method optimized for a large screen. Furthermore, if the company uses a desktop, the providing unit can provide a delivery method including detailed information. This allows a more appropriate delivery method to be selected by taking into account the company's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's device information data into a generation AI to generate data for selecting the optimal delivery method.

[0063] The providing unit can make the provided content multilingual according to the company's language setting at the time of providing. The providing unit, for example, can make the provided content multilingual according to the company's language setting at the time of providing. The providing unit can, for example, automatically translate the provided content based on the company's language setting. The providing unit can also provide a language switching function if the company uses multiple languages. The providing unit can also provide the provided content in the optimal language according to the company's language setting. This makes it possible to provide more appropriate information by making the provided content multilingual according to the company's language setting. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's language setting data into a generation AI to generate data for generating multilingual provided content.

[0064] The providing unit can analyze the company's social media activities and provide related information at the time of providing. For example, the providing unit can analyze the company's social media activities and provide related information at the time of providing. For example, the providing unit can provide information related to popular posts on the company's social media. The providing unit can also analyze the reactions of the company's followers on social media and provide related information. The providing unit can also provide the most effective information based on the company's social media activities. In this way, more effective information can be provided by analyzing the company's social media activities. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the company's social media activity data into a generation AI to generate data for providing related information.

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

[0066] The collection unit can analyze the success of a company's past marketing campaigns and determine the priority of tagline data to be collected. For example, taglines that have been highly successful in past campaigns can be collected with priority. Taglines from campaigns with medium success can also be collected with second priority. Taglines from campaigns with low success can also be collected later. In this way, by analyzing the success of a company's past marketing campaigns, more effective tagline data can be collected with priority.

[0067] During analysis, the analysis unit can evaluate the success and influence of taglines and reflect this in the analysis results. For example, it is possible to focus analysis on taglines that have been highly successful in past campaigns. It is also possible to evaluate the influence of taglines and reflect this in the analysis results. Furthermore, it is also possible to exclude taglines with low success from the analysis results. In this way, more effective analysis results can be obtained by evaluating the success and influence of taglines.

[0068] When receiving the input, the reception unit can optimize the input content by referring to the company's past strategies and branding intentions. For example, it can suggest the optimal input content based on the company's past strategies. It can also customize the input content by referring to the company's past branding intentions. Furthermore, it can identify the most effective input content based on the company's past strategies and branding intentions. In this way, it is possible to provide more effective input content by referring to the company's past strategies and branding intentions.

[0069] The generation unit can evaluate the creativity and effectiveness of taglines at the time of generation and reflect the evaluation in the generated results. For example, taglines with high creativity can be generated preferentially. Effective taglines can also be evaluated and reflected in the generated results. Furthermore, the most effective tagline can be generated based on the creativity and effectiveness. In this way, more effective taglines can be generated by evaluating the creativity and effectiveness of taglines.

[0070] At the time of provision, the provision unit can select the optimal provision method by referring to the company's past feedback. For example, the optimal provision method can be selected based on the company's past feedback. The provision method can also be adjusted by reflecting the company's feedback. Furthermore, the most effective provision method can also be selected based on the company's past feedback. In this way, a more effective provision method can be selected by referring to the company's past feedback.

[0071] The processing flow of the first embodiment will be briefly explained below.

[0072] Step 1: The collection unit collects data on past taglines. The collection unit can collect data such as taglines used by the company in the past, taglines of competitors, taglines of advertising campaigns, and catchphrases for products. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses, for example, text mining or natural language processing technology to learn tagline trends based on the collected data. The analysis unit can extract frequently occurring keywords and perform topic modeling. Step 3: The reception department accepts the company's strategy and branding intentions as input. For example, the reception department can accept the company's mission statement, vision, target market, brand values, etc. It can also accept documents describing the company's mission and purpose, as well as documents describing the company's future goals and direction. Step 4: The generation unit generates a new tagline based on the tagline trends learned by the analysis unit and the company's strategy and branding intentions received by the reception unit. The generation unit generates the tagline using generation AI. For example, the generation AI may use text generation AI (e.g., LLM) or multimodal generation AI to generate the tagline. If a company places importance on "innovation," it can generate a creative tagline with the theme of "innovation." Step 5: The providing unit provides the tagline generated by the generating unit. The providing unit can provide the generated tagline to a company, for example. The tagline can be provided by setting the format and timing of provision.

[0073] (Example 2) A tagline generation system according to an embodiment of the present invention learns trends in past taglines and generates new taglines based on a company's strategy and branding intentions. The tagline generation system collects data on past taglines and uses a generation AI to learn these trends. Next, the company's strategy and branding intentions are accepted as input, and the generation AI generates a new tagline based on this information. The generated tagline achieves creative and effective branding while maintaining the consistency of the brand message. For example, the tagline generation system collects taglines used in the past by the company and taglines used by competitors. This data is input into the generation AI to learn tagline trends. Next, the company's mission statement, vision, target market, brand values, etc. are accepted as input. This information is input into the generation AI and serves as the basis for generating a new tagline. The generation AI generates a new tagline based on trends in past taglines and the company's strategy and branding intentions. For example, if a company emphasizes "innovation," the generation AI generates a creative tagline based on the theme of "innovation." The generated tagline achieves creative and effective branding while maintaining the consistency of the brand message. This allows the tagline generator system to achieve creative and effective branding while allowing companies to maintain a consistent brand message.This allows the tagline generator system to achieve creative and effective branding while allowing companies to maintain a consistent brand message.

[0074] A tagline generation system according to an embodiment includes a collection unit, an analysis unit, a reception unit, a generation unit, and a provision unit. The collection unit collects data on past taglines. The collection unit can collect, for example, data such as taglines used by a company in the past and taglines used by competitors. The collection unit can collect, for example, taglines from advertising campaigns and product catchphrases. The analysis unit analyzes the data collected by the collection unit. The analysis unit learns tagline trends based on the collected data, for example, using text mining or natural language processing technology. The analysis unit can perform, for example, extraction of frequently occurring keywords and topic modeling. The reception unit receives, as input, a company's strategy and branding intentions. The reception unit can receive, for example, a company's mission statement, vision, target market, brand values, etc. The reception unit can receive, for example, a document describing a company's mission and objectives, or a document describing a company's future goals and direction. The generation unit generates a new tagline based on the tagline trends learned by the analysis unit and the company's strategy and branding intentions received by the reception unit. The generation unit generates the tagline using a generation AI. The generation AI generates the tagline using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if a company emphasizes "innovation," the generation unit can generate a creative tagline with an "innovation" theme. The provision unit provides the tagline generated by the generation unit. For example, the provision unit can provide the generated tagline to the company. For example, the provision unit can provide the tagline by setting the provision format, provision timing, etc. As a result, the tagline generation system according to the embodiment learns the trends of past taglines and generates a new tagline based on the company's strategy and branding intentions, thereby achieving creative and effective branding while maintaining consistency in the brand message.

[0075] The collection unit can collect data on taglines used in the past by a company or taglines of competitors. The collection unit, for example, collects taglines used in the past by a company. For example, the collection unit can collect taglines from advertising campaigns and product slogans. The collection unit can also collect taglines from competitors. For example, the collection unit can collect taglines from advertising campaigns of competitors in the same industry. By collecting data on taglines used in the past by a company or taglines from competitors, tagline trends can be learned based on a wider variety of data. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input data on taglines used in the past by a company into a generation AI to collect data for learning tagline trends.

[0076] The analysis unit can learn tagline trends based on the collected data. The analysis unit, for example, learns tagline trends based on the collected data. For example, the analysis unit can analyze the collected data using text mining or natural language processing technology. The analysis unit can also perform frequently occurring keyword extraction and topic modeling. For example, the analysis unit can extract frequently occurring keywords from the collected data and learn tagline trends. The analysis unit can also learn tagline trends using topic modeling. This enables more accurate tagline generation by learning tagline trends based on the collected data. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into a generation AI to learn tagline trends.

[0077] The reception unit can accept a company's mission statement or vision, target market, and brand values ​​as input. The reception unit, for example, accepts a company's mission statement as input. For example, the reception unit can accept a document describing the company's mission or objectives. The reception unit can also accept a company's vision as input. For example, the reception unit can accept a document describing the company's future goals and direction. The reception unit can also accept a target market as input. For example, the reception unit can accept customer segments, geographical market characteristics, etc. as input. The reception unit can also accept brand values ​​as input. For example, the reception unit can accept a brand's core values, corporate culture, etc. as input. By accepting a company's mission statement, vision, target market, brand values, etc. as input, tagline generation based on the company's strategy and branding intentions becomes possible. Some or all of the above-described processing in the reception unit may be performed, for example, using AI or without AI. For example, the reception department can input a company's mission statement, vision, target market, and brand values ​​into the generation AI and accept them as basic data for tagline generation.

[0078] The generation unit can generate a new tagline based on past tagline trends and the company's strategy and branding intentions. The generation unit generates a new tagline based on, for example, past tagline trends and the company's strategy and branding intentions. The generation unit generates the tagline using a generation AI. The generation AI can generate the tagline using, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, if a company emphasizes "innovation," the generation unit can generate a creative tagline based on the theme of "innovation." The generation unit can also generate a new tagline based on the company's past tagline. For example, if a company's past tagline was "Create the Future," the generation unit can generate a new tagline based on "Create the Future." By generating a new tagline based on past tagline trends and the company's strategy and branding intentions, creative and effective branding can be achieved while maintaining consistency in the brand message. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation department can input past tagline trends and a company's strategy and branding intentions into the generation AI to generate a new tagline.

[0079] The providing unit can provide the generated tagline to a company. For example, the providing unit provides the generated tagline to a company. The providing unit can provide the tagline by setting, for example, the format and timing of provision. For example, the providing unit can provide the generated tagline to a company through a web application or a mobile application. The providing unit can also send the generated tagline to a company by email. For example, the providing unit can provide the generated tagline to a company in PDF format or text format. This allows a company to quickly use a new tagline by providing the generated tagline to a company. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the generated tagline into a generation AI to generate data to be provided to a company.

[0080] The collection unit can estimate the user's emotions and adjust the timing of tagline data collection based on the emotion data. For example, the collection unit estimates the user's emotions and adjusts the timing of tagline data collection based on the emotion data. For example, if the user is feeling stressed, the collection unit can delay the collection timing and collect data when the user is relaxed. If the user is excited, the collection unit can immediately start collecting data and collect data while the user's emotions are heightened. If the user is tired, the collection unit can adjust the collection timing and collect data after the user has rested. This allows data to be collected at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and generate data for adjusting the collection timing.

[0081] The collection unit can analyze the success of a company's past marketing campaigns and determine the priority of tagline data to be collected. For example, the collection unit can analyze the success of a company's past marketing campaigns and determine the priority of tagline data to be collected. For example, the collection unit can prioritize collecting taglines that were highly successful in past campaigns. The collection unit can also prioritize collecting taglines from campaigns that were moderately successful. The collection unit can also postpone collecting taglines from campaigns that were less successful. In this way, by analyzing the success of a company's past marketing campaigns, more effective tagline data can be prioritized. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input data on the success of a company's past marketing campaigns into a generation AI to generate data for determining the priority of tagline data to be collected.

[0082] The collection unit can filter tagline data based on the company's industry trends or market trends when collecting the tagline data. For example, the collection unit can filter tagline data taking into account the company's industry trends or market trends when collecting the tagline data. For example, the collection unit can prioritize collecting taglines that match current market trends. The collection unit can also filter and collect highly relevant taglines based on industry trends. The collection unit can also exclude taglines that do not match market trends or industry trends from the collection targets. In this way, by filtering taking into account the company's industry trends or market trends, more relevant tagline data can be collected. Some or all of the above-described processing by the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input company's industry trends or market trend data into a generation AI to generate data for filtering.

[0083] The collection unit can monitor the activities of the company's competitors in real time when collecting tagline data and update the collected data. For example, the collection unit can monitor the activities of the company's competitors in real time when collecting tagline data and update the collected data. For example, the collection unit can immediately collect data when a competitor announces a new tagline. Furthermore, if a competitor's campaign is successful, the collection unit can prioritize collecting that tagline. The collection unit can also monitor the activities of competitors in real time and update the collected data as needed. In this way, by monitoring the activities of the company's competitors in real time and updating the collected data, tagline data can be collected based on the latest information. Some or all of the above-mentioned processing in the collection unit can be performed, for example, using AI or without AI. For example, the collection unit can input trend data of the company's competitors into a generation AI and generate data for updating the collected data.

[0084] The collection unit can estimate the user's emotions and determine the priority of tagline data to be collected based on the emotion data. For example, the collection unit can estimate the user's emotions and determine the priority of tagline data to be collected based on the emotion data. For example, if the user is relaxed, the collection unit can prioritize collecting creative taglines. Furthermore, if the user is feeling stressed, the collection unit can prioritize collecting simple and intuitive taglines. Furthermore, if the user is excited, the collection unit can prioritize collecting taglines that enhance the user's emotions. This allows for prioritized collection of more appropriate data by determining the priority of tagline data to be collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into a generation AI to generate data for determining the priority of tagline data to be collected.

[0085] When collecting tagline data, the collection unit may prioritize collecting highly relevant data by taking into account the geographical market characteristics of the company. For example, when collecting tagline data, the collection unit may prioritize collecting highly relevant data by taking into account the geographical market characteristics of the company. For example, if the company's main market is North America, the collection unit may prioritize collecting taglines related to the North American market. Furthermore, if the company is expanding into an emerging market, the collection unit may prioritize collecting taglines related to that market. Furthermore, the collection unit may filter and collect the most relevant taglines based on the geographical market characteristics of the company. This allows for more effective collection of tagline data by prioritizing the collection of highly relevant data by taking into account the geographical market characteristics of the company. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the company's geographical market characteristic data into a generation AI to generate data for preferentially collecting highly relevant data.

[0086] The collection unit may analyze the company's social media activities and collect related data when collecting tagline data. For example, the collection unit may analyze the company's social media activities and collect related data when collecting tagline data. For example, the collection unit may collect taglines related to the company's popular posts on social media. The collection unit may also analyze the reactions of the company's followers on social media and collect related taglines. The collection unit may also filter and collect the most effective taglines based on the company's social media activities. In this way, by analyzing the company's social media activities and collecting related data, more effective tagline data can be collected. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the company's social media activity data into a generation AI to generate data for collecting related data.

[0087] The collection unit can customize the collection method by reflecting the company's past feedback when collecting tagline data. For example, the collection unit can customize the collection method by reflecting the company's past feedback when collecting tagline data. For example, the collection unit can prioritize collecting taglines that the company has received high ratings for in the past. The collection unit can also adjust the collection method based on the company's past feedback and collect optimal taglines. The collection unit can also improve the quality of the collected data by reflecting the company's past feedback. In this way, more effective tagline data can be collected by customizing the collection method by reflecting the company's past feedback. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the company's past feedback data into a generation AI to generate data for customizing the collection method.

[0088] The analysis unit can estimate the user's emotions and adjust the tagline trend analysis method based on the emotion data. For example, the analysis unit can estimate the user's emotions and adjust the tagline trend analysis method based on the emotion data. For example, the analysis unit can perform a detailed trend analysis when the user is relaxed. For example, the analysis unit can perform a concise trend analysis when the user is in a hurry. For example, the analysis unit can perform a trend analysis that accentuates the user's emotions when the user is excited. This allows for more appropriate analysis results to be obtained by adjusting the trend analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or without an AI. For example, the analysis unit can input the user's emotion data into the generation AI and generate data for adjusting the trend analysis method.

[0089] The analysis unit can evaluate the success and influence of taglines during analysis and reflect the evaluation in the analysis results. For example, the analysis unit can evaluate the success and influence of taglines during analysis and reflect the evaluation in the analysis results. For example, the analysis unit can focus its analysis on taglines that have been highly successful in past campaigns. The analysis unit can also evaluate the influence of taglines and reflect the evaluation in the analysis results. The analysis unit can also exclude taglines with low success from the analysis results. In this way, more effective analysis results can be obtained by evaluating the success and influence of taglines. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the success and influence of taglines into a generation AI to generate data to be reflected in the analysis results.

[0090] The analysis unit can analyze trends by taking into account the frequency and duration of tagline usage during analysis. For example, the analysis unit can analyze trends by taking into account the frequency and duration of tagline usage during analysis. For example, the analysis unit can focus its analysis on taglines that are used frequently. The analysis unit can also analyze trends of taglines that have been used for a long period of time. The analysis unit can also identify the most effective taglines based on the frequency and duration of usage. This enables more accurate trend analysis by taking into account the frequency and duration of tagline usage. Some or all of the above-described processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the frequency and duration of tagline usage into a generation AI to generate data for analyzing trends.

[0091] The analysis unit can adjust the analysis algorithm during analysis, taking into account the linguistic features and cultural background of the tagline. For example, the analysis unit can adjust the analysis algorithm during analysis, taking into account the linguistic features and cultural background of the tagline. For example, the analysis unit can apply an optimal analysis algorithm based on the linguistic features. The analysis unit can also adjust the analysis results by taking into account the cultural background. The analysis unit can also identify the most effective tagline based on the linguistic features and cultural background. In this way, more appropriate analysis results can be obtained by taking into account the linguistic features and cultural background of the tagline. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input data on the linguistic features and cultural background of the tagline into a generation AI to generate data for adjusting the analysis algorithm.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotion data. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the emotion data. For example, the analysis unit can display detailed analysis results when the user is relaxed. For example, the analysis unit can display concise analysis results when the user is in a hurry. For example, the analysis unit can display visually stimulating analysis results when the user is excited. This allows for more appropriate display by adjusting the display method of the analysis results based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and generate data for adjusting the display method of the analysis results.

[0093] The analysis unit may perform the analysis while taking into account the geographical usage of the tagline. For example, the analysis unit may perform the analysis while taking into account the geographical usage of the tagline. For example, the analysis unit may focus on taglines that are used over a wide geographical area. The analysis unit may also analyze trends in taglines that are successful in a specific region. The analysis unit may also identify the most effective tagline based on the geographical usage. This allows for more effective analysis results to be obtained by taking into account the geographical usage of the tagline. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input geographical usage data of the tagline into a generation AI to generate data for analysis.

[0094] The analysis unit can improve the accuracy of the analysis by referring to literature and research data related to the tagline during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to literature and research data related to the tagline during analysis. For example, the analysis unit can adjust the analysis algorithm by referring to related literature. The analysis unit can also complement the analysis results based on research data. The analysis unit can also identify the most effective tagline based on related literature and research data. By referring to related literature and research data, the accuracy of the analysis is improved. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input literature and research data related to the tagline into a generation AI to generate data for improving the accuracy of the analysis.

[0095] The analysis unit can perform the analysis taking into account the market value of the tagline. For example, the analysis unit can perform the analysis taking into account the market value of the tagline. For example, the analysis unit can focus the analysis on taglines with high market value. The analysis unit can also evaluate market value and reflect it in the analysis results. The analysis unit can also identify the most effective tagline based on market value. In this way, more effective analysis results can be obtained by taking the market value of the tagline into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input market value data of the tagline into a generation AI to generate data for analysis.

[0096] The reception unit can estimate the user's emotions and adjust the input method for the company's strategy and branding intentions based on the emotion data. For example, the reception unit can estimate the user's emotions and adjust the input method for the company's strategy and branding intentions based on the emotion data. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to enable the company's strategy and branding intentions to be input quickly. This allows for more appropriate input by adjusting the input method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input user emotional data into the generation AI and generate data to adjust the input method for a company's strategy and branding intentions.

[0097] The reception unit can, at the time of reception, appropriate the input content based on the company's past strategies or branding intentions. For example, at the time of reception, the reception unit optimizes the input content by referring to the company's past strategies and branding intentions. The reception unit can, for example, suggest optimal input content based on the company's past strategies. The reception unit can also customize the input content by referring to the company's past branding intentions. The reception unit can also identify the most effective input content based on the company's past strategies and branding intentions. In this way, more effective input content can be provided by referring to the company's past strategies and branding intentions. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the company's past strategies and branding intention data into a generation AI to generate data for optimizing the input content.

[0098] The reception unit can customize the input content at the time of reception, taking into account the company's current market situation and the trends of competitors. For example, the reception unit customizes the input content at the time of reception, taking into account the company's current market situation and the trends of competitors. For example, the reception unit can suggest optimal input content based on the company's current market situation. The reception unit can also customize the input content by taking into account the trends of competitors. The reception unit can also identify the most effective input content based on the company's current market situation and the trends of competitors. This makes it possible to provide more effective input content by taking into account the company's current market situation and the trends of competitors. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input data on the company's current market situation and the trends of competitors into a generation AI to generate data for customizing the input content.

[0099] The reception unit can improve the input method by reflecting the company's feedback when receiving the data. The reception unit can improve the input method by reflecting the company's feedback when receiving the data, for example. The reception unit can adjust the input method based on the company's past feedback, for example. The reception unit can also provide the optimal input method by reflecting the company's feedback. The reception unit can also improve the input method and identify the most effective input content based on the company's feedback. In this way, a more effective input method can be provided by reflecting the company's feedback. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the company's feedback data into a generation AI to generate data for improving the input method.

[0100] The reception unit can estimate the user's emotions and prioritize the input contents based on the emotion data. For example, the reception unit can estimate the user's emotions and prioritize the input contents based on the emotion data. For example, when the user is relaxed, the reception unit can prioritize detailed input contents. Furthermore, when the user is stressed, the reception unit can prioritize simple but important input contents. Furthermore, when the user is in a hurry, the reception unit can prioritize contents that can be entered quickly. This enables more appropriate input by prioritizing the input contents based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's emotion data into a generation AI to generate data for prioritizing the input contents.

[0101] The reception unit can customize the input content at the time of reception, taking into account the geographical market characteristics of the company. For example, the reception unit customizes the input content at the time of reception, taking into account the geographical market characteristics of the company. For example, if the company's main market is North America, the reception unit can preferentially receive input content related to the North American market. Furthermore, if the company is expanding into an emerging market, the reception unit can customize the input content related to that market. Furthermore, the reception unit can filter and receive the most relevant input content based on the geographical market characteristics of the company. This makes it possible to provide more effective input content by taking into account the geographical market characteristics of the company. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the company's geographical market characteristic data into a generation AI to generate data for customizing the input content.

[0102] The reception unit can analyze the company's social media activity at the time of reception and suggest related input content. For example, the reception unit can analyze the company's social media activity at the time of reception and suggest related input content. For example, the reception unit can suggest input content related to popular posts on the company's social media. The reception unit can also analyze the reactions of the company's followers on social media and suggest related input content. The reception unit can also suggest the most effective input content based on the company's social media activity. In this way, more effective input content can be provided by analyzing the company's social media activity. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the company's social media activity data into a generation AI to generate data for suggesting related input content.

[0103] The reception unit can customize the input method by reflecting the company's past feedback when receiving the data. For example, the reception unit customizes the input method by reflecting the company's past feedback when receiving the data. For example, the reception unit can preferentially provide input methods that have received high ratings from the company in the past. The reception unit can also adjust the input method based on the company's past feedback and provide optimal input content. The reception unit can also improve the quality of the input data by reflecting the company's past feedback. In this way, a more effective input method can be provided by reflecting the company's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the company's past feedback data into a generation AI to generate data for customizing the input method.

[0104] The generation unit can estimate the user's emotions and adjust the expression style of the tagline to be generated based on the emotion data. For example, the generation unit can estimate the user's emotions and adjust the expression style of the tagline to be generated based on the emotion data. For example, if the user is relaxed, the generation unit can generate a tagline with a soft expression. If the user is excited, the generation unit can generate a tagline with a powerful expression. If the user is stressed, the generation unit can generate a tagline with a simple and intuitive expression. This allows for the generation of a more appropriate tagline by adjusting the expression style of the tagline based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the user's emotion data into the generation AI and generate data for adjusting the expression style of the tagline to be generated.

[0105] The generation unit can evaluate the creativity and effectiveness of a tagline at the time of generation and reflect the evaluation in the generation result. For example, the generation unit can evaluate the creativity and effectiveness of a tagline at the time of generation and reflect the evaluation in the generation result. For example, the generation unit can preferentially generate taglines with a high creativity level. The generation unit can also evaluate taglines with high effectiveness and reflect the evaluation in the generation result. The generation unit can also generate the most effective tagline based on the creativity and effectiveness. In this way, by evaluating the creativity and effectiveness of taglines, more effective taglines can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input data on the creativity and effectiveness of a tagline into a generation AI and generate data to be reflected in the generation result.

[0106] The generation unit can apply different generation algorithms depending on the category or theme of the tagline during generation. For example, the generation unit can apply different generation algorithms depending on the category or theme of the tagline during generation. The generation unit can apply the optimal generation algorithm depending on the category, for example. The generation unit can also apply different generation algorithms based on the theme. The generation unit can also select the most effective generation algorithm depending on the category or theme. In this way, by applying different generation algorithms depending on the category or theme of the tagline, more appropriate taglines can be generated. Some or all of the above-described processing in the generation unit can be performed using, for example, AI, or can be performed without using AI. For example, the generation unit can input tagline category or theme data into a generation AI to generate data for applying different generation algorithms.

[0107] The generation unit can improve the accuracy of tagline generation by referring to the company's past tagline results during generation. For example, the generation unit can improve the accuracy of tagline generation by referring to the company's past tagline results during generation. For example, the generation unit can apply an optimal generation algorithm based on the company's past tagline results. The generation unit can also improve the accuracy of tagline generation by referring to the past tagline results. The generation unit can also generate the most effective tagline based on the company's past tagline results. This improves the accuracy of tagline generation by referring to the company's past tagline results. Some or all of the above-described processing in the generation unit can be performed using AI, for example, or without AI. For example, the generation unit can input the company's past tagline result data into the generation AI to generate data for improving the accuracy of tagline generation.

[0108] The generation unit can estimate the user's emotions and adjust the length of the tagline to be generated based on the emotion data. For example, the generation unit can estimate the user's emotions and adjust the length of the tagline to be generated based on the emotion data. For example, the generation unit can generate a longer tagline when the user is relaxed. Furthermore, the generation unit can generate a short and to-the-point tagline when the user is in a hurry. Furthermore, the generation unit can generate a visually stimulating tagline when the user is excited. By adjusting the length of the tagline based on the user's emotions, a more appropriate tagline can be generated. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be 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-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and generate data for adjusting the length of the tagline to be generated.

[0109] The generation unit can determine the generation priority based on the submission time of the tagline at the time of generation. The generation unit, for example, determines the generation priority based on the submission time of the tagline at the time of generation. The generation unit can, for example, prioritize generating taglines that will be submitted soon. The generation unit can also adjust the generation priority based on the submission time. The generation unit can also postpone generating taglines that will be submitted further in the future. In this way, by determining the generation priority based on the submission time of the tagline, taglines can be generated at a more appropriate time. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input tagline submission time data into a generation AI to generate data for determining the generation priority.

[0110] The generation unit can adjust the order of tagline generation based on the relevance of the taglines during generation. The generation unit, for example, can adjust the order of tagline generation based on the relevance of the taglines during generation. The generation unit can, for example, prioritize generating highly relevant taglines. The generation unit can also adjust the order of tagline generation based on the relevance. The generation unit can also postpone generating taglines with low relevance. In this way, by adjusting the order of tagline generation based on the relevance of the taglines, more effective taglines can be generated. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI. For example, the generation unit can input tagline relevance data into a generation AI to generate data for adjusting the order of tagline generation.

[0111] The generation unit may adjust the use of terminology in the tagline to be generated according to the company's level of expertise during generation. For example, the generation unit may adjust the use of terminology in the tagline to be generated according to the company's level of expertise during generation. For example, if the company's level of expertise is high, the generation unit may generate a tagline that uses a lot of terminology. Alternatively, if the company's level of expertise is low, the generation unit may generate a simple, easy-to-understand tagline. The generation unit may also adjust the use of optimal terminology according to the company's level of expertise. In this way, by adjusting the use of terminology according to the company's level of expertise, a more appropriate tagline can be generated. Some or all of the above-described processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit may input company's level of expertise data into the generation AI to generate data for adjusting the use of terminology.

[0112] The providing unit can estimate the user's emotions and adjust the display method of the tagline to be provided based on the emotion data. For example, the providing unit can estimate the user's emotions and adjust the display method of the tagline to be provided based on the emotion data. For example, the providing unit can provide a detailed display method when the user is relaxed. Furthermore, the providing unit can provide a concise display method when the user is in a hurry. Furthermore, the providing unit can provide a visually stimulating display method when the user is excited. This enables a more appropriate display by adjusting the display method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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-mentioned processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and generate data for adjusting the display method of the tagline to be provided.

[0113] The providing unit can select the optimal delivery method by referring to the company's past feedback at the time of provision. For example, the providing unit can select the optimal delivery method by referring to the company's past feedback at the time of provision. For example, the providing unit can select the optimal delivery method based on the company's past feedback. The providing unit can also adjust the delivery method by reflecting the company's feedback. The providing unit can also select the most effective delivery method based on the company's past feedback. In this way, by referring to the company's past feedback, a more effective delivery method can be selected. Some or all of the above-mentioned processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's past feedback data into a generation AI to generate data for selecting the optimal delivery method.

[0114] The provision unit can customize the content to be provided according to the company's current tasks and projects at the time of provision. For example, the provision unit customizes the content to be provided according to the company's current tasks and projects at the time of provision. For example, the provision unit can propose optimal content to be provided based on the company's current tasks. The provision unit can also customize the content to be provided according to the company's current projects. The provision unit can also identify the most effective content to be provided based on the company's current tasks and projects. This allows for more effective information to be provided by customizing the content to be provided according to the company's current tasks and projects. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input data on the company's current tasks and projects into a generation AI to generate data for customizing the content to be provided.

[0115] The provision unit can improve the provision method by reflecting the company's feedback at the time of provision. For example, the provision unit can improve the provision method by reflecting the company's feedback at the time of provision. For example, the provision unit can adjust the provision method based on the company's past feedback. The provision unit can also provide the optimal provision method by reflecting the company's feedback. The provision unit can also improve the provision method and identify the most effective provision content based on the company's feedback. In this way, a more effective provision method can be provided by reflecting the company's feedback. Some or all of the above-mentioned processing in the provision unit may be performed using AI, for example, or may be performed without using AI. For example, the provision unit can input the company's feedback data into a generation AI to generate data for improving the provision method.

[0116] The providing unit can estimate the user's emotions and determine the priority of taglines to be provided based on the emotion data. For example, the providing unit can estimate the user's emotions and determine the priority of taglines to be provided based on the emotion data. For example, if the user is relaxed, the providing unit can prioritize providing detailed taglines. If the user is in a hurry, the providing unit can prioritize providing concise taglines. If the user is excited, the providing unit can prioritize providing visually stimulating taglines. This allows for more appropriate taglines to be provided by determining the priority of taglines to be provided based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input the user's emotion data into the generation AI and generate data for determining the priority of taglines to be provided.

[0117] The providing unit can select the optimal delivery method by taking into account the company's device information at the time of provision. For example, the providing unit selects the optimal delivery method by taking into account the company's device information at the time of provision. For example, if the company uses a smartphone, the providing unit can provide a delivery method tailored to the screen size. Furthermore, if the company uses a tablet, the providing unit can provide a delivery method optimized for a large screen. Furthermore, if the company uses a desktop, the providing unit can provide a delivery method including detailed information. This allows a more appropriate delivery method to be selected by taking into account the company's device information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's device information data into a generation AI to generate data for selecting the optimal delivery method.

[0118] The providing unit can make the provided content multilingual according to the company's language setting at the time of providing. The providing unit, for example, can make the provided content multilingual according to the company's language setting at the time of providing. The providing unit can, for example, automatically translate the provided content based on the company's language setting. The providing unit can also provide a language switching function if the company uses multiple languages. The providing unit can also provide the provided content in the optimal language according to the company's language setting. This makes it possible to provide more appropriate information by making the provided content multilingual according to the company's language setting. Some or all of the above-mentioned processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the company's language setting data into a generation AI to generate data for generating multilingual provided content.

[0119] The providing unit can analyze the company's social media activities and provide related information at the time of providing. For example, the providing unit can analyze the company's social media activities and provide related information at the time of providing. For example, the providing unit can provide information related to popular posts on the company's social media. The providing unit can also analyze the reactions of the company's followers on social media and provide related information. The providing unit can also provide the most effective information based on the company's social media activities. In this way, more effective information can be provided by analyzing the company's social media activities. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the company's social media activity data into a generation AI to generate data for providing related information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reception unit, generation unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reception unit, generation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned collection unit, analysis unit, reception unit, generation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is realized by the specific processing unit 290 of the data processing device 12. For example, the provision unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12.

[0120] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0121] The collection unit can analyze the success of a company's past marketing campaigns and determine the priority of tagline data to be collected. For example, taglines that have been highly successful in past campaigns can be collected with priority. Taglines from campaigns with medium success can also be collected with second priority. Taglines from campaigns with low success can also be collected later. In this way, by analyzing the success of a company's past marketing campaigns, more effective tagline data can be collected with priority.

[0122] During analysis, the analysis unit can evaluate the success and influence of taglines and reflect this in the analysis results. For example, it is possible to focus analysis on taglines that have been highly successful in past campaigns. It is also possible to evaluate the influence of taglines and reflect this in the analysis results. Furthermore, it is also possible to exclude taglines with low success from the analysis results. In this way, more effective analysis results can be obtained by evaluating the success and influence of taglines.

[0123] When receiving the input, the reception unit can optimize the input content by referring to the company's past strategies and branding intentions. For example, it can suggest the optimal input content based on the company's past strategies. It can also customize the input content by referring to the company's past branding intentions. Furthermore, it can identify the most effective input content based on the company's past strategies and branding intentions. In this way, it is possible to provide more effective input content by referring to the company's past strategies and branding intentions.

[0124] The generation unit can evaluate the creativity and effectiveness of taglines at the time of generation and reflect the evaluation in the generated results. For example, taglines with high creativity can be generated preferentially. Effective taglines can also be evaluated and reflected in the generated results. Furthermore, the most effective tagline can be generated based on the creativity and effectiveness. In this way, more effective taglines can be generated by evaluating the creativity and effectiveness of taglines.

[0125] At the time of provision, the provision unit can select the optimal provision method by referring to the company's past feedback. For example, the optimal provision method can be selected based on the company's past feedback. The provision method can also be adjusted by reflecting the company's feedback. Furthermore, the most effective provision method can also be selected based on the company's past feedback. In this way, a more effective provision method can be selected by referring to the company's past feedback.

[0126] The collection unit can estimate the user's emotions and adjust the timing of tagline data collection based on the emotion data. For example, if the user is feeling stressed, the collection timing can be delayed to collect data when the user is relaxed. Also, if the user is excited, collection can be started immediately to collect data while the user's emotions are heightened. Furthermore, if the user is tired, the collection timing can be adjusted to collect data after the user has rested. In this way, by adjusting the collection timing based on the user's emotions, data can be collected at more appropriate times.

[0127] The analysis unit can estimate the user's emotions and adjust the method of tagline trend analysis based on the emotion data. For example, if the user is relaxed, a detailed trend analysis can be performed. If the user is in a hurry, a concise trend analysis can be performed. Furthermore, if the user is excited, a trend analysis that highlights the user's emotions can be performed. In this way, by adjusting the trend analysis method based on the user's emotions, more appropriate analysis results can be obtained.

[0128] The reception unit can estimate the user's emotions and adjust the input method for the company's strategy and branding intentions based on the emotion data. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to enable the company's strategy and branding intentions to be input quickly. This allows for more appropriate input by adjusting the input method based on the user's emotions.

[0129] The generation unit can estimate the user's emotions and adjust the expression style of the tagline to be generated based on the emotion data. For example, if the user is relaxed, a tagline with a soft expression can be generated. If the user is excited, a tagline with a powerful expression can be generated. Furthermore, if the user is stressed, a tagline with a simple and intuitive expression can be generated. In this way, by adjusting the expression style of the tagline based on the user's emotions, more appropriate taglines can be generated.

[0130] The providing unit can estimate the user's emotions and adjust the display method of the tagline to be provided based on the emotion data. For example, if the user is relaxed, a detailed display method can be provided. If the user is in a hurry, a concise display method can be provided. Furthermore, if the user is excited, a visually stimulating display method can be provided. In this way, by adjusting the display method based on the user's emotions, more appropriate display is possible.

[0131] The processing flow of the second embodiment will be briefly explained below.

[0132] Step 1: The collection unit collects data on past taglines. The collection unit can collect data such as taglines used by the company in the past, taglines of competitors, taglines of advertising campaigns, and catchphrases for products. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses, for example, text mining or natural language processing technology to learn tagline trends based on the collected data. The analysis unit can extract frequently occurring keywords and perform topic modeling. Step 3: The reception department accepts the company's strategy and branding intentions as input. For example, the reception department can accept the company's mission statement, vision, target market, brand values, etc. It can also accept documents describing the company's mission and purpose, as well as documents describing the company's future goals and direction. Step 4: The generation unit generates a new tagline based on the tagline trends learned by the analysis unit and the company's strategy and branding intentions received by the reception unit. The generation unit generates the tagline using generation AI. For example, the generation AI may use text generation AI (e.g., LLM) or multimodal generation AI to generate the tagline. If a company places importance on "innovation," it can generate a creative tagline with the theme of "innovation." Step 5: The providing unit provides the tagline generated by the generating unit. The providing unit can provide the generated tagline to a company, for example. The tagline can be provided by setting the format and timing of provision.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0138] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0154] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0161] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0162] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0163] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0164] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0165] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0166] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0167] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0168] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0169] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0170] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0171] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0172] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0173] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0174] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0175] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0176] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0177] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0187] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0188] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0189] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0190] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0191] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0192] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0193] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0194] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0195] 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.

[0196] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0197] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0198] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0199] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0200] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0201] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0202] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0203] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0204] [Explanation of symbols]

[0205] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection department that collects data on past taglines; an analysis unit that analyzes the data collected by the collection unit; a reception unit that receives corporate strategies and branding intentions as input; a generation unit that generates a new tagline based on the tagline trends learned by the analysis unit and the company's strategy and branding intentions received by the reception unit; a providing unit that provides the tagline generated by the generating unit. A system characterized by:

2. The collecting unit Collect data on taglines used by the company in the past or competitors' taglines 2. The system of claim 1.

3. The analysis unit Learn tagline trends based on collected data 2. The system of claim 1.

4. The reception unit Accepts as input the company's mission statement or vision, target market, and brand values 2. The system of claim 1.

5. The generation unit Generate new taglines based on past tagline trends and the company's strategy and branding intentions 2. The system of claim 1.

6. The providing unit Provide generated taglines to businesses 2. The system of claim 1.

7. The collecting unit Estimate user sentiment and adjust the timing of tagline data collection based on the estimated user sentiment.

2. The system of claim 1.

8. The collecting unit Analyze the success of a company's past marketing campaigns to prioritize tagline data collection 2. The system of claim 1.

Images