system

Abstract

The system according to this embodiment aims to propose a unique concept based on the behavioral data of the target group and derive the optimal strategy. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a proposal unit, a strategy unit, and a creation unit. The collection unit collects behavioral data of the target audience. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a unique concept based on the analysis results obtained by the analysis unit. The strategy unit derives an optimal strategy based on the concept proposed by the proposal unit. The creation unit creates an advertising concept based on the strategy derived by the strategy unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a unique concept has not been sufficiently proposed based on the behavior data of the target layer, and an optimal strategy has not been sufficiently derived, leaving room for improvement.

[0005] The system according to the embodiment aims to propose a unique concept based on the behavior data of the target layer and derive an optimal strategy.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a strategy unit, and a creation unit. The data collection unit collects behavioral data of the target audience. The analysis unit analyzes the data collected by the data collection unit. The proposal unit proposes a unique concept based on the analysis results obtained by the analysis unit. The strategy unit derives an optimal strategy based on the concept proposed by the proposal unit. The creation unit creates an advertising concept based on the strategy derived by the strategy unit. [Effects of the Invention]

[0007] The system according to this embodiment can propose unique concepts and derive optimal strategies based on behavioral data of the target group. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The CreativeSpark AI system, according to an embodiment of the present invention, is an AI-based tool that continuously generates innovative marketing campaign ideas. This CreativeSpark AI system analyzes the behavior and interests of target audiences and proposes unique concepts based on that analysis. The CreativeSpark AI system helps marketers and creative professionals gain new perspectives and ideas to succeed in the competitive landscape. It accurately captures consumer needs and supports the creation of impactful campaigns. The CreativeSpark AI system derives the optimal strategy from a wealth of ideas and creates advertising concepts that resonate strongly with the target audience. By utilizing this CreativeSpark AI system, marketers can efficiently plan and execute compelling campaigns. For example, the CreativeSpark AI system uses AI to analyze the behavior and interests of target audiences. Next, the CreativeSpark AI system proposes unique concepts based on the analysis results. Furthermore, the CreativeSpark AI system uses AI to derive the optimal strategy from a wealth of ideas and creates advertising concepts that resonate strongly with the target audience. For example, the CreativeSpark AI system uses AI to analyze the past behavioral data of a target audience and proposes unique advertising concepts related to a particular product to those who have a high interest in that product. Furthermore, the CreativeSpark AI system helps accurately grasp consumer needs and create impactful campaigns. In this way, the CreativeSpark AI system becomes a powerful tool for marketers and creative professionals to plan and execute efficient and compelling campaigns.

[0029] The CreativeSpark AI system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a strategy unit, and a creation unit. The data collection unit collects behavioral data of the target audience. The data collection unit can collect behavioral data such as website browsing history, purchase history, and social media activity. For example, the data collection unit collects website browsing history to understand the interests of the target audience. The data collection unit can also collect purchase history to analyze the purchasing patterns of the target audience. Furthermore, the data collection unit can collect social media activity to identify areas of interest for the target audience. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the collected data and identify the interests of the target audience. For example, the analysis unit uses AI to analyze the collected data and identify the interests of the target audience. Furthermore, the analysis unit can use AI to analyze the collected data and identify the purchasing patterns of the target audience. Furthermore, the analysis unit can use AI to analyze the collected data and identify areas of interest for the target audience. The proposal unit proposes a unique concept based on the analysis results obtained by the analysis unit. The proposal department, for example, uses AI to propose unique concepts based on analysis results. The proposal department, for example, uses AI to propose unique concepts based on analysis results. The proposal department can also use AI to propose unique advertising concepts based on analysis results. Furthermore, the proposal department can also use AI to propose unique marketing concepts based on analysis results. The strategy department derives the optimal strategy based on the concepts proposed by the proposal department. The strategy department, for example, uses AI to derive the optimal strategy based on the concepts proposed. The strategy department, for example, uses AI to derive the optimal strategy based on the concepts proposed. Furthermore, the strategy department can also use AI to derive the optimal marketing strategy based on the concepts proposed. Furthermore, the strategy department can also use AI to derive the optimal advertising strategy based on the concepts proposed. The creation department creates advertising concepts based on the strategies derived by the strategy department. The creation department, for example, uses AI to create advertising concepts based on strategies derived.The creation unit, for example, creates advertising concepts based on strategies derived by AI. The creation unit can also create marketing concepts based on AI-derived strategies. Furthermore, the creation unit can create advertising campaigns based on AI-derived strategies. As a result, the CreativeSpark AI system according to this embodiment can collect and analyze behavioral data of target audiences, propose unique concepts, derive optimal strategies, and create advertising concepts.

[0030] The data collection unit collects behavioral data from the target audience. For example, it can collect behavioral data such as website browsing history, purchase history, and social media activity. Specifically, when collecting website browsing history, it obtains detailed data such as pages visited, time spent on each page, and links clicked. This allows for an understanding of what kind of content the target audience is interested in. When collecting purchase history, it collects data such as purchased items, purchase frequency, and payment methods to analyze the target audience's purchasing patterns. For example, it can obtain information such as which seasons a particular product is purchased most often, or which price ranges are preferred. When collecting social media activity, it collects data such as content posted by users, the number of likes and shares, and accounts followed. This allows for the identification of topics the target audience is interested in and which communities they belong to. The data collection unit centrally manages this data and updates it in real time to maintain up-to-date information. Furthermore, appropriate privacy protection measures are taken during data collection, and user consent is obtained beforehand. This allows the data collection unit to efficiently and effectively collect behavioral data from the target audience and provide the information necessary for the next analysis step.

[0031] The analytics department analyzes the data collected by the data collection department. For example, the analytics department uses AI to analyze the collected data and identify the interests of the target audience. Specifically, the AI ​​uses machine learning algorithms to extract patterns and trends from the collected data. For example, by analyzing the level of interest in specific keywords and categories from website browsing history, the analytics department can identify what kind of information the target audience is looking for. In the analysis of purchase history, the analytics department analyzes frequently purchased products, the timing of purchases, and the attributes of purchasers to predict the purchasing behavior of the target audience. In the analysis of social media activity, natural language processing technology is used to analyze the content of posts and identify topics and emotions that the target audience is interested in. For example, it can identify topics with many posts expressing positive emotions or topics with many negative reactions. The analytics department integrates this data to understand the overall picture of the target audience. Furthermore, by comparing it with past data, it can track changes in interests and behavior and predict future trends. This allows the analytics department to deeply understand the interests and behavior of the target audience and provide the insights necessary for the next proposal step.

[0032] The proposal department proposes unique concepts based on the analysis results obtained by the analysis department. For example, the proposal department uses AI to propose unique concepts based on the analysis results. Specifically, the AI ​​uses generative AI technology to generate advertising and marketing concepts based on the interests and purchasing patterns of the target audience. For example, if the target audience has a strong interest in a particular brand or product, the proposal department will propose an advertising concept centered on that brand or product. Also, if the target audience values ​​a particular lifestyle or values, the proposal department will propose a marketing concept that resonates with that lifestyle or values. The proposal department generates multiple of these concepts and establishes evaluation criteria to select the most effective one. For example, it predicts the effectiveness of each concept based on past campaign success stories and target audience response data, and selects the optimal concept. Furthermore, the proposal department translates the selected concept into concrete creative ideas and creates detailed plans for application to actual advertising and marketing campaigns. In this way, the proposal department can provide unique concepts for effectively conveying messages to the target audience and build the foundation necessary for the next strategic step.

[0033] The Strategy Department derives the optimal strategy based on the concept proposed by the Proposal Department. For example, the Strategy Department uses AI to derive the optimal strategy based on the proposed concept. Specifically, the AI ​​uses simulation technology to generate multiple strategic scenarios based on the proposed concept and predict the effect of each scenario. For example, it simulates the target audience's response and the success rate of a marketing campaign when a particular advertising concept is used, and selects the most effective strategy. Based on these simulation results, the Strategy Department formulates specific marketing and advertising strategies. For example, it selects media and channels that are of high interest to the target audience and devises a strategy to deliver advertisements at the optimal timing. It also designs campaigns to promote specific products or services based on the target audience's purchasing patterns. Furthermore, the Strategy Department manages resource allocation and schedules to implement the proposed concept and formulates an effective implementation plan. In this way, the Strategy Department can provide the optimal strategy to maximize the use of the proposed concept and deliver an effective message to the target audience.

[0034] The creative department creates advertising concepts based on strategies derived by the strategy department. For example, the creative department creates advertising concepts based on strategies derived using AI. Specifically, the AI ​​uses generative AI technology to automatically generate advertising creatives based on the strategy. For example, it generates visuals and copy based on the interests and purchasing patterns of the target audience and creates advertising banners and video ads. The AI ​​also evaluates the effectiveness of the advertising creatives in real time based on target audience response data and makes adjustments as needed. Based on these generated advertising creatives, the creative department designs and implements specific advertising campaigns. For example, it creates advertising materials for distribution to specific media and channels and sets distribution schedules. The creative department also monitors the progress of advertising campaigns and sets metrics to evaluate their effectiveness. This allows the creative department to create and implement effective advertising concepts for the target audience based on strategies derived by the strategy department. Furthermore, the creative department analyzes the results of advertising campaigns and identifies areas for improvement for the next campaign. This allows the creative department to consistently provide effective advertising concepts and deliver the optimal message to the target audience.

[0035] The Needs Assessment Department accurately grasps consumer needs. For example, the Needs Assessment Department uses surveys to understand consumer needs. The Needs Assessment Department can collect consumer needs by conducting surveys. Furthermore, the Needs Assessment Department can also understand consumer needs through interviews. For example, it can collect consumer opinions and requests through interviews. In addition, the Needs Assessment Department can understand consumer needs by analyzing purchase data. For example, it can analyze purchase data to identify consumer purchasing trends. This allows for an accurate understanding of consumer needs.

[0036] The Campaign Execution Department is responsible for creating impactful campaigns. For example, the Campaign Execution Department can implement effective campaigns targeting specific demographics. This includes, for instance, conducting effective advertising campaigns targeting specific demographics, as well as implementing effective promotional campaigns targeting specific demographics, such as promoting specific products or services. Furthermore, the Campaign Execution Department can implement effective marketing campaigns targeting specific demographics, such as delivering specific marketing messages. This enables the creation of impactful campaigns.

[0037] The data collection unit can collect past behavioral data of the target audience. For example, the data collection unit can collect the target audience's past purchase history. For example, the data collection unit can collect past purchase history to understand the target audience's purchasing patterns. The data collection unit can also collect the target audience's past website browsing history. For example, it can collect past website browsing history to identify the target audience's interests. Furthermore, the data collection unit can collect the target audience's past social media activity. For example, it can collect past social media activity to identify the target audience's areas of interest. This allows for the collection of past behavioral data of the target audience.

[0038] The analysis department can analyze the collected data and identify the interests of the target audience. For example, it can analyze the collected data to identify the target audience's interests. It can also analyze the collected data to identify the target audience's purchasing patterns. Furthermore, the analysis department can analyze the collected data to identify the target audience's areas of interest. For example, it can analyze the collected data to understand the target audience's interest in specific products or services. This allows the department to identify the target audience's interests.

[0039] The proposal department can propose unique concepts based on identified interests. For example, the proposal department can propose unique advertising concepts based on identified interests. For example, the proposal department can propose unique marketing concepts based on identified interests. Furthermore, the proposal department can also propose unique promotional concepts based on identified interests. For example, it can propose unique concepts related to specific products or services based on identified interests. This allows for the proposal of unique concepts.

[0040] The Strategy Department can derive the optimal strategy based on the proposed concept. For example, the Strategy Department can derive the optimal marketing strategy based on the proposed concept. For example, the Strategy Department can derive the optimal advertising strategy based on the proposed concept. Furthermore, the Strategy Department can also derive the optimal promotion strategy based on the proposed concept. For example, it can derive the optimal strategy for a specific product or service based on the proposed concept. This allows for the development of the optimal strategy.

[0041] The creation department can create advertising concepts based on the derived strategies. For example, the creation department can create advertising concepts based on the derived strategies. For example, the creation department can create marketing concepts based on the derived strategies. Furthermore, the creation department can also create promotional concepts based on the derived strategies. For example, they can create advertising concepts related to specific products or services based on the derived strategies. This allows for the creation of advertising concepts.

[0042] The data collection unit can analyze the target audience's past purchase history and select the optimal data collection method. For example, based on past purchase history, the data collection unit can prioritize collecting data related to a specific product category for users who have a high interest in that category. The data collection unit can also collect data related to specific seasons or events based on purchase history. For example, based on purchase history, it can collect data related to specific seasons or events. Furthermore, the data collection unit can analyze purchase history and apply different data collection methods to repeat customers and new customers. For example, it can analyze purchase history and apply different data collection methods to repeat customers and new customers. This allows the system to analyze the target audience's past purchase history and select the optimal data collection method.

[0043] The data collection unit can filter behavioral data based on the target audience's current areas of interest. For example, the data collection unit can filter the data to be collected based on keywords related to the current areas of interest. The data collection unit can also analyze social media trends and prioritize the collection of data related to areas of interest. For example, it can analyze social media trends and prioritize the collection of data related to areas of interest. Furthermore, the data collection unit can collect data related to current areas of interest based on the user's search history. For example, it can collect data related to current areas of interest based on the user's search history. This allows the data to be filtered based on the target audience's current areas of interest.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the target group when collecting behavioral data. For example, the data collection unit can prioritize the collection of purchasing behavior in a specific region based on geographical location information. The data collection unit can also prioritize the collection of data related to specific events or seasons based on geographical location information. For example, the data collection unit can prioritize the collection of data related to specific events or seasons based on geographical location information. Furthermore, the data collection unit can also prioritize the collection of data related to areas of interest in each region based on geographical location information. For example, the data collection unit can prioritize the collection of data related to areas of interest in each region based on geographical location information. This allows for the priority collection of highly relevant data by considering the geographical location information of the target group.

[0045] The data collection unit can analyze the social media activities of the target audience and collect relevant data when collecting behavioral data. For example, the data collection unit can analyze the content of social media posts and collect data related to areas of interest. The data collection unit can also prioritize the collection of data from influential users based on the number of social media followers and engagement rates. For example, it can prioritize the collection of data from influential users based on the number of social media followers and engagement rates. Furthermore, the data collection unit can analyze social media trends and collect data related to current areas of interest. For example, it can analyze social media trends and collect data related to current areas of interest. This allows for the analysis of the target audience's social media activities and the collection of relevant data.

[0046] The analysis department can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis department can perform a detailed analysis on high-importance data to gain deeper insights. The analysis department can also perform a simplified analysis on low-importance data to conserve resources. For example, it can perform a simplified analysis on low-importance data to conserve resources. Furthermore, the analysis department can prioritize analyses based on data importance and allocate resources efficiently. For example, it can prioritize analyses based on data importance and allocate resources efficiently. This allows the level of detail of the analysis to be adjusted based on the importance of the collected data.

[0047] The analysis unit can apply different analysis algorithms depending on the data category. For example, it can apply an algorithm to identify purchase patterns to purchase history data. Furthermore, it can apply sentiment analysis algorithms to social media data. Additionally, it can apply algorithms to identify regional trends to geographic location data. This allows for the application of different analysis algorithms depending on the data category.

[0048] The analytics department can prioritize analyses based on when the collected data is submitted. For example, it can prioritize analyzing the most recent data to gain immediate insights. The analytics department can also postpone analyzing older data to conserve resources. Furthermore, the analytics department can adjust the analysis schedule according to the data submission timing to efficiently allocate resources. This allows the analytics department to prioritize analyses based on when the collected data is submitted.

[0049] The analysis unit can adjust the order of analysis based on the relevance of the collected data. For example, the analysis unit can prioritize analyzing highly relevant data to gain deeper insights. The analysis unit can also postpone analyzing less relevant data to conserve resources. Furthermore, the analysis unit can adjust the analysis schedule according to the relevance of the data to efficiently allocate resources. This allows the analysis order to be adjusted based on the relevance of the collected data.

[0050] The proposal team can adjust the level of detail in their proposals based on the importance of the concepts. For example, for high-importance concepts, the proposal team can provide detailed proposals and deeper insights. Furthermore, for low-importance concepts, the proposal team can provide simplified proposals to conserve resources. In addition, the proposal team can prioritize proposals based on the importance of the concepts and allocate resources efficiently. This allows for adjusting the level of detail in proposals based on the importance of the concepts.

[0051] The proposal department can apply different proposal algorithms depending on the category of the concept. For example, for a product concept, it can apply a proposal algorithm that emphasizes product characteristics. For a service concept, it can apply a proposal algorithm that emphasizes the convenience of the service. Furthermore, for a brand concept, it can apply a proposal algorithm that emphasizes the brand image. In this way, different proposal algorithms can be applied depending on the category of the concept.

[0052] The proposal team can prioritize proposals based on the timing of concept submissions. For example, the proposal team can prioritize proposals for the latest concepts to gain immediate insights. The proposal team can also postpone proposals for older concepts to conserve resources. Furthermore, the proposal team can adjust the proposal schedule according to the timing of concept submissions to efficiently allocate resources. This allows the proposal team to prioritize proposals based on the timing of concept submissions.

[0053] The proposal team can adjust the order of proposals based on the relevance of the concepts. For example, the proposal team can prioritize proposals for highly relevant concepts to gain deeper insights. The proposal team can also postpone less relevant concepts to conserve resources. For example, they can postpone less relevant concepts to conserve resources. Furthermore, the proposal team can adjust the proposal schedule according to the relevance of the concepts to allocate resources efficiently. For example, they can adjust the proposal schedule according to the relevance of the concepts to allocate resources efficiently. This allows the order of proposals to be adjusted based on the relevance of the concepts.

[0054] The Strategy Department can select the optimal strategy by referring to past success stories when formulating a strategy. For example, the Strategy Department can analyze past success stories and select strategies applicable to similar situations. The Strategy Department can also identify the factors behind success stories and customize strategies based on them. For example, the Strategy Department can identify the factors behind success stories and customize strategies based on them. Furthermore, the Strategy Department can simulate and select the optimal strategy based on data from success stories. For example, the Strategy Department can simulate and select the optimal strategy based on data from success stories. This allows for the selection of the optimal strategy by referring to past success stories.

[0055] The Strategy Department can customize strategies by considering the attribute information of the target group when formulating them. For example, the Strategy Department can adjust the content of the strategy according to the age group of the target group. The Strategy Department can also set the focus of the strategy based on the interests of the target group. For example, the focus of the strategy is set based on the interests of the target group. Furthermore, the Strategy Department can select the implementation area of ​​the strategy by considering the geographical distribution of the target group. For example, the implementation area of ​​the strategy is selected by considering the geographical distribution of the target group. This allows for the customization of strategies by considering the attribute information of the target group.

[0056] The Strategy Department can select the optimal strategy when formulating a strategy, taking into account the geographical distribution of the target audience. For example, the Strategy Department can prioritize the formulation of strategies for specific regions based on geographical distribution. The Strategy Department can also formulate strategies tailored to the characteristics of each region, based on geographical distribution. For example, the Strategy Department can formulate strategies tailored to the characteristics of each region, based on geographical distribution. Furthermore, the Strategy Department can formulate strategies considering market trends in each region, based on geographical distribution. For example, the Strategy Department can formulate strategies considering market trends in each region, based on geographical distribution. This allows for the selection of the optimal strategy, taking into account the geographical distribution of the target audience.

[0057] The Strategy Department can improve the accuracy of its strategies by referring to relevant literature during the strategy formulation process. For example, the Strategy Department can formulate strategies that incorporate the latest marketing trends based on relevant literature. The Strategy Department can also formulate strategies that draw on past success stories based on relevant literature. For example, the Strategy Department can formulate strategies that draw on past success stories based on relevant literature. Furthermore, the Strategy Department can formulate strategies that have theoretical backing based on relevant literature. For example, the Strategy Department can formulate strategies that have theoretical backing based on relevant literature. This allows the Strategy Department to improve the accuracy of its strategies by referring to relevant literature.

[0058] The creation department can select the optimal advertising concept by referring to past success stories when creating an advertising concept. For example, the creation department can analyze past success stories and select an advertising concept that can be applied to similar situations. The creation department can also identify the factors behind success stories and customize the advertising concept based on them. For example, they can identify the factors behind success stories and customize the advertising concept based on them. Furthermore, the creation department can simulate and select the optimal advertising concept based on data from success stories. For example, they can simulate and select the optimal advertising concept based on data from success stories. This allows for the selection of the optimal concept by referring to past success stories.

[0059] The creation department can customize advertising concepts by considering the attribute information of the target audience. For example, the creation department can adjust the content of the advertising concept according to the age group of the target audience. The creation department can also set the focus of the advertising concept based on the interests of the target audience. For example, the focus of the advertising concept is set based on the interests of the target audience. Furthermore, the creation department can select the execution area of ​​the advertising concept by considering the geographical distribution of the target audience. For example, the execution area of ​​the advertising concept is selected by considering the geographical distribution of the target audience. This allows for the customization of concepts by considering the attribute information of the target audience.

[0060] The creation department can select the optimal advertising concept by considering the geographical distribution of the target audience. For example, the creation department can prioritize creating advertising concepts for specific regions based on geographical distribution. The creation department can also create advertising concepts tailored to the characteristics of each region based on geographical distribution. For example, the creation department can create advertising concepts tailored to the characteristics of each region based on geographical distribution. Furthermore, the creation department can create advertising concepts by considering market trends in each region based on geographical distribution. For example, the creation department can create advertising concepts by considering market trends in each region based on geographical distribution. This allows for the selection of the optimal concept by considering the geographical distribution of the target audience.

[0061] The creative team can improve the accuracy of advertising concepts by referring to relevant literature during the concept creation process. For example, the creative team can create advertising concepts that incorporate the latest marketing trends based on relevant literature. The creative team can also create advertising concepts that refer to past success stories based on relevant literature. For example, the creative team can create advertising concepts that refer to past success stories based on relevant literature. Furthermore, the creative team can create advertising concepts that have theoretical backing based on relevant literature. For example, the creative team can create advertising concepts that have theoretical backing based on relevant literature. This allows for improvement in the accuracy of concepts by referring to relevant literature.

[0062] The needs assessment unit can analyze past consumer behavior to select the optimal needs assessment method. For example, the needs assessment unit can assess needs for a specific product category based on past consumer behavior. The needs assessment unit can also analyze consumer behavior to assess needs related to specific seasons or events. For example, it can analyze consumer behavior to assess needs related to specific seasons or events. Furthermore, the needs assessment unit can apply different needs assessment methods to repeat customers and new customers based on their consumer behavior. For example, it can apply different needs assessment methods to repeat customers and new customers based on their consumer behavior. This allows the system to analyze past consumer behavior and select the optimal needs assessment method.

[0063] The needs assessment unit can select the optimal needs assessment method by considering the geographical location information of the target group when assessing needs. For example, the needs assessment unit can prioritize the assessment of needs in a specific region based on geographical location information. The needs assessment unit can also select a needs assessment method that is appropriate for the characteristics of each region based on geographical location information. For example, the needs assessment unit can select a needs assessment method that is appropriate for the characteristics of each region based on geographical location information. Furthermore, the needs assessment unit can assess needs by considering market trends in each region based on geographical location information. For example, the needs assessment unit can assess needs by considering market trends in each region based on geographical location information. This allows for the selection of the optimal needs assessment method by considering the geographical location information of the target group.

[0064] The campaign execution unit can select the optimal execution method by referring to past campaign data when executing a campaign. For example, the campaign execution unit can analyze past successful campaign data and select an execution method applicable to similar situations. The campaign execution unit can also identify the factors of successful campaigns and customize the execution method based on them. Furthermore, the campaign execution unit can simulate and select the optimal execution method based on past campaign data. For example, it can simulate and select the optimal execution method based on past campaign data. This allows the optimal execution method to be selected by referring to past campaign data.

[0065] The campaign execution unit can customize the execution method when executing a campaign, taking into account the attribute information of the target audience. For example, the campaign execution unit can adjust the content of the campaign according to the age group of the target audience. The campaign execution unit can also set the focus of the campaign based on the interests of the target audience. For example, it sets the focus of the campaign based on the interests of the target audience. Furthermore, the campaign execution unit can select the execution area of ​​the campaign considering the geographical distribution of the target audience. For example, it selects the execution area of ​​the campaign considering the geographical distribution of the target audience. This allows the execution method to be customized by taking into account the attribute information of the target audience.

[0066] The campaign execution unit can select the optimal execution method when executing a campaign, taking into account the geographical location information of the target audience. For example, the campaign execution unit can prioritize the execution of campaigns in specific regions based on geographical location information. The campaign execution unit can also execute campaigns tailored to the characteristics of each region based on geographical location information. For example, the campaign execution unit can execute campaigns tailored to the characteristics of each region based on geographical location information. Furthermore, the campaign execution unit can execute campaigns while considering market trends in each region based on geographical location information. For example, the campaign execution unit can execute campaigns while considering market trends in each region based on geographical location information. This allows the system to select the optimal execution method, taking into account the geographical location information of the target audience.

[0067] The campaign execution unit can improve the accuracy of its execution methods by referring to relevant literature during campaign execution. For example, the campaign execution unit can execute campaigns that incorporate the latest marketing trends based on relevant literature. The campaign execution unit can also execute campaigns that refer to past success stories based on relevant literature. For example, the campaign execution unit can execute campaigns that refer to past success stories based on relevant literature. Furthermore, the campaign execution unit can execute campaigns that have theoretical backing based on relevant literature. For example, the campaign execution unit can execute campaigns that have theoretical backing based on relevant literature. This allows the accuracy of the execution methods to be improved by referring to relevant literature.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The CreativeSpark AI system can also be equipped with a feedback collection unit. This unit collects the target audience's responses to the implemented campaign. For example, it can collect comments and reviews from social media to understand the target audience's opinions and feelings. It can also conduct surveys to obtain detailed feedback on the campaign's effectiveness. Furthermore, it can analyze post-campaign purchase data to evaluate the campaign's impact. This allows the CreativeSpark AI system to evaluate the effectiveness of the implemented campaign and incorporate the findings into future campaigns.

[0070] The CreativeSpark AI system can also include a competitive analysis department. This department analyzes competitors' campaigns and derives competitive strategies. For example, it collects competitors' advertising content and evaluates its effectiveness. It can also analyze competitors' marketing strategies and identify success factors. Furthermore, it can analyze the timing and frequency of competitors' campaigns and incorporate this into your own campaign plan. This allows the CreativeSpark AI system to provide strategies for gaining a competitive edge.

[0071] The CreativeSpark AI system can also be equipped with a personalized suggestion unit. This unit proposes personalized advertising concepts based on the individual preferences and behaviors of the target audience. For example, it can analyze a specific user's past purchase history and suggest the most suitable advertisement for that user. It can also analyze a user's social media activity and suggest advertisements related to topics of interest. Furthermore, it can suggest advertisements that are likely to interest the user based on their search history. This allows the CreativeSpark AI system to provide advertisements tailored to the individual needs of the target audience.

[0072] The CreativeSpark AI system can also be equipped with a real-time adjustment unit. This unit collects data in real time during a campaign and adjusts the campaign content as needed. For example, it monitors social media reactions and changes the ad content if there are many negative reactions. It can also analyze website traffic data and change ad placement if it is not performing well. Furthermore, it can analyze purchase data in real time and strengthen promotions if sales are not growing. This allows the CreativeSpark AI system to make real-time adjustments to maximize the effectiveness of the campaign.

[0073] The following briefly describes the processing flow for example form 1.

[0074] Step 1: The data collection unit collects behavioral data of the target audience. For example, it can collect behavioral data such as website browsing history, purchase history, and social media activity. The data collection unit collects website browsing history to understand the target audience's interests. It can also collect purchase history to analyze the target audience's purchasing patterns. Furthermore, it can collect social media activity to identify the target audience's areas of interest. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the collected data and identify the interests of the target audience. The AI ​​can analyze the collected data to identify the target audience's interests, purchasing patterns, and areas of interest. Step 3: The proposal department proposes unique concepts based on the analysis results obtained by the analysis department. For example, AI can be used to propose unique concepts based on the analysis results. AI can propose unique advertising and marketing concepts based on the analysis results. Step 4: The Strategy Department derives the optimal strategy based on the concept proposed by the Proposal Department. For example, they can use AI to derive the optimal strategy based on the proposed concept. AI can derive the optimal marketing and advertising strategies based on the proposed concept. Step 5: The creation team creates advertising concepts based on the strategies derived by the strategy team. For example, they create advertising concepts based on strategies derived using AI. AI can create advertising concepts, marketing concepts, and advertising campaigns based on strategies derived by AI.

[0075] (Example of form 2) The CreativeSpark AI system, according to an embodiment of the present invention, is an AI-based tool that continuously generates innovative marketing campaign ideas. This CreativeSpark AI system analyzes the behavior and interests of target audiences and proposes unique concepts based on that analysis. The CreativeSpark AI system helps marketers and creative professionals gain new perspectives and ideas to succeed in the competitive landscape. It accurately captures consumer needs and supports the creation of impactful campaigns. The CreativeSpark AI system derives the optimal strategy from a wealth of ideas and creates advertising concepts that resonate strongly with the target audience. By utilizing this CreativeSpark AI system, marketers can efficiently plan and execute compelling campaigns. For example, the CreativeSpark AI system uses AI to analyze the behavior and interests of target audiences. Next, the CreativeSpark AI system proposes unique concepts based on the analysis results. Furthermore, the CreativeSpark AI system uses AI to derive the optimal strategy from a wealth of ideas and creates advertising concepts that resonate strongly with the target audience. For example, the CreativeSpark AI system uses AI to analyze the past behavioral data of a target audience and proposes unique advertising concepts related to a particular product to those who have a high interest in that product. Furthermore, the CreativeSpark AI system helps accurately grasp consumer needs and create impactful campaigns. In this way, the CreativeSpark AI system becomes a powerful tool for marketers and creative professionals to plan and execute efficient and compelling campaigns.

[0076] The CreativeSpark AI system according to this embodiment comprises a data collection unit, an analysis unit, a proposal unit, a strategy unit, and a creation unit. The data collection unit collects behavioral data of the target audience. The data collection unit can collect behavioral data such as website browsing history, purchase history, and social media activity. For example, the data collection unit collects website browsing history to understand the interests of the target audience. The data collection unit can also collect purchase history to analyze the purchasing patterns of the target audience. Furthermore, the data collection unit can collect social media activity to identify areas of interest for the target audience. The analysis unit analyzes the data collected by the data collection unit. For example, the analysis unit uses AI to analyze the collected data and identify the interests of the target audience. For example, the analysis unit uses AI to analyze the collected data and identify the interests of the target audience. Furthermore, the analysis unit can use AI to analyze the collected data and identify the purchasing patterns of the target audience. Furthermore, the analysis unit can use AI to analyze the collected data and identify areas of interest for the target audience. The proposal unit proposes a unique concept based on the analysis results obtained by the analysis unit. The proposal department, for example, uses AI to propose unique concepts based on analysis results. The proposal department, for example, uses AI to propose unique concepts based on analysis results. The proposal department can also use AI to propose unique advertising concepts based on analysis results. Furthermore, the proposal department can also use AI to propose unique marketing concepts based on analysis results. The strategy department derives the optimal strategy based on the concepts proposed by the proposal department. The strategy department, for example, uses AI to derive the optimal strategy based on the concepts proposed. The strategy department, for example, uses AI to derive the optimal strategy based on the concepts proposed. Furthermore, the strategy department can also use AI to derive the optimal marketing strategy based on the concepts proposed. Furthermore, the strategy department can also use AI to derive the optimal advertising strategy based on the concepts proposed. The creation department creates advertising concepts based on the strategies derived by the strategy department. The creation department, for example, uses AI to create advertising concepts based on strategies derived.The creation unit, for example, creates advertising concepts based on strategies derived by AI. The creation unit can also create marketing concepts based on AI-derived strategies. Furthermore, the creation unit can create advertising campaigns based on AI-derived strategies. As a result, the CreativeSpark AI system according to this embodiment can collect and analyze behavioral data of target audiences, propose unique concepts, derive optimal strategies, and create advertising concepts.

[0077] The data collection unit collects behavioral data from the target audience. For example, it can collect behavioral data such as website browsing history, purchase history, and social media activity. Specifically, when collecting website browsing history, it obtains detailed data such as pages visited, time spent on each page, and links clicked. This allows for an understanding of what kind of content the target audience is interested in. When collecting purchase history, it collects data such as purchased items, purchase frequency, and payment methods to analyze the target audience's purchasing patterns. For example, it can obtain information such as which seasons a particular product is purchased most often, or which price ranges are preferred. When collecting social media activity, it collects data such as content posted by users, the number of likes and shares, and accounts followed. This allows for the identification of topics the target audience is interested in and which communities they belong to. The data collection unit centrally manages this data and updates it in real time to maintain up-to-date information. Furthermore, appropriate privacy protection measures are taken during data collection, and user consent is obtained beforehand. This allows the data collection unit to efficiently and effectively collect behavioral data from the target audience and provide the information necessary for the next analysis step.

[0078] The analytics department analyzes the data collected by the data collection department. For example, the analytics department uses AI to analyze the collected data and identify the interests of the target audience. Specifically, the AI ​​uses machine learning algorithms to extract patterns and trends from the collected data. For example, by analyzing the level of interest in specific keywords and categories from website browsing history, the analytics department can identify what kind of information the target audience is looking for. In the analysis of purchase history, the analytics department analyzes frequently purchased products, the timing of purchases, and the attributes of purchasers to predict the purchasing behavior of the target audience. In the analysis of social media activity, natural language processing technology is used to analyze the content of posts and identify topics and emotions that the target audience is interested in. For example, it can identify topics with many posts expressing positive emotions or topics with many negative reactions. The analytics department integrates this data to understand the overall picture of the target audience. Furthermore, by comparing it with past data, it can track changes in interests and behavior and predict future trends. This allows the analytics department to deeply understand the interests and behavior of the target audience and provide the insights necessary for the next proposal step.

[0079] The proposal department proposes unique concepts based on the analysis results obtained by the analysis department. For example, the proposal department uses AI to propose unique concepts based on the analysis results. Specifically, the AI ​​uses generative AI technology to generate advertising and marketing concepts based on the interests and purchasing patterns of the target audience. For example, if the target audience has a strong interest in a particular brand or product, the proposal department will propose an advertising concept centered on that brand or product. Also, if the target audience values ​​a particular lifestyle or values, the proposal department will propose a marketing concept that resonates with that lifestyle or values. The proposal department generates multiple of these concepts and establishes evaluation criteria to select the most effective one. For example, it predicts the effectiveness of each concept based on past campaign success stories and target audience response data, and selects the optimal concept. Furthermore, the proposal department translates the selected concept into concrete creative ideas and creates detailed plans for application to actual advertising and marketing campaigns. In this way, the proposal department can provide unique concepts for effectively conveying messages to the target audience and build the foundation necessary for the next strategic step.

[0080] The Strategy Department derives the optimal strategy based on the concept proposed by the Proposal Department. For example, the Strategy Department uses AI to derive the optimal strategy based on the proposed concept. Specifically, the AI ​​uses simulation technology to generate multiple strategic scenarios based on the proposed concept and predict the effect of each scenario. For example, it simulates the target audience's response and the success rate of a marketing campaign when a particular advertising concept is used, and selects the most effective strategy. Based on these simulation results, the Strategy Department formulates specific marketing and advertising strategies. For example, it selects media and channels that are of high interest to the target audience and devises a strategy to deliver advertisements at the optimal timing. It also designs campaigns to promote specific products or services based on the target audience's purchasing patterns. Furthermore, the Strategy Department manages resource allocation and schedules to implement the proposed concept and formulates an effective implementation plan. In this way, the Strategy Department can provide the optimal strategy to maximize the use of the proposed concept and deliver an effective message to the target audience.

[0081] The creative department creates advertising concepts based on strategies derived by the strategy department. For example, the creative department creates advertising concepts based on strategies derived using AI. Specifically, the AI ​​uses generative AI technology to automatically generate advertising creatives based on the strategy. For example, it generates visuals and copy based on the interests and purchasing patterns of the target audience and creates advertising banners and video ads. The AI ​​also evaluates the effectiveness of the advertising creatives in real time based on target audience response data and makes adjustments as needed. Based on these generated advertising creatives, the creative department designs and implements specific advertising campaigns. For example, it creates advertising materials for distribution to specific media and channels and sets distribution schedules. The creative department also monitors the progress of advertising campaigns and sets metrics to evaluate their effectiveness. This allows the creative department to create and implement effective advertising concepts for the target audience based on strategies derived by the strategy department. Furthermore, the creative department analyzes the results of advertising campaigns and identifies areas for improvement for the next campaign. This allows the creative department to consistently provide effective advertising concepts and deliver the optimal message to the target audience.

[0082] The Needs Assessment Department accurately grasps consumer needs. For example, the Needs Assessment Department uses surveys to understand consumer needs. The Needs Assessment Department can collect consumer needs by conducting surveys. Furthermore, the Needs Assessment Department can also understand consumer needs through interviews. For example, it can collect consumer opinions and requests through interviews. In addition, the Needs Assessment Department can understand consumer needs by analyzing purchase data. For example, it can analyze purchase data to identify consumer purchasing trends. This allows for an accurate understanding of consumer needs.

[0083] The Campaign Execution Department is responsible for creating impactful campaigns. For example, the Campaign Execution Department can implement effective campaigns targeting specific demographics. This includes, for instance, conducting effective advertising campaigns targeting specific demographics, as well as implementing effective promotional campaigns targeting specific demographics, such as promoting specific products or services. Furthermore, the Campaign Execution Department can implement effective marketing campaigns targeting specific demographics, such as delivering specific marketing messages. This enables the creation of impactful campaigns.

[0084] The data collection unit can collect past behavioral data of the target audience. For example, the data collection unit can collect the target audience's past purchase history. For example, the data collection unit can collect past purchase history to understand the target audience's purchasing patterns. The data collection unit can also collect the target audience's past website browsing history. For example, it can collect past website browsing history to identify the target audience's interests. Furthermore, the data collection unit can collect the target audience's past social media activity. For example, it can collect past social media activity to identify the target audience's areas of interest. This allows for the collection of past behavioral data of the target audience.

[0085] The analysis department can analyze the collected data and identify the interests of the target audience. For example, it can analyze the collected data to identify the target audience's interests. It can also analyze the collected data to identify the target audience's purchasing patterns. Furthermore, the analysis department can analyze the collected data to identify the target audience's areas of interest. For example, it can analyze the collected data to understand the target audience's interest in specific products or services. This allows the department to identify the target audience's interests.

[0086] The proposal department can propose unique concepts based on identified interests. For example, the proposal department can propose unique advertising concepts based on identified interests. For example, the proposal department can propose unique marketing concepts based on identified interests. Furthermore, the proposal department can also propose unique promotional concepts based on identified interests. For example, it can propose unique concepts related to specific products or services based on identified interests. This allows for the proposal of unique concepts.

[0087] The Strategy Department can derive the optimal strategy based on the proposed concept. For example, the Strategy Department can derive the optimal marketing strategy based on the proposed concept. For example, the Strategy Department can derive the optimal advertising strategy based on the proposed concept. Furthermore, the Strategy Department can also derive the optimal promotion strategy based on the proposed concept. For example, it can derive the optimal strategy for a specific product or service based on the proposed concept. This allows for the development of the optimal strategy.

[0088] The creation department can create advertising concepts based on the derived strategies. For example, the creation department can create advertising concepts based on the derived strategies. For example, the creation department can create marketing concepts based on the derived strategies. Furthermore, the creation department can also create promotional concepts based on the derived strategies. For example, they can create advertising concepts related to specific products or services based on the derived strategies. This allows for the creation of advertising concepts.

[0089] The data collection unit can estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated emotions. For example, if the user is excited, the data collection unit can collect behavioral data in real time to capture immediate reactions. The data collection unit can also collect behavioral data at regular intervals when the user is relaxed to grasp long-term trends. For example, if the user is relaxed, it can collect behavioral data at regular intervals. Furthermore, if the user is stressed, the data collection unit can reduce the collection frequency to alleviate the user's burden. For example, if the user is stressed, it can reduce the collection frequency. This allows the timing of behavioral data collection to be adjusted based on the user's emotions.

[0090] The data collection unit can analyze the target audience's past purchase history and select the optimal data collection method. For example, based on past purchase history, the data collection unit can prioritize collecting data related to a specific product category for users who have a high interest in that category. The data collection unit can also collect data related to specific seasons or events based on purchase history. For example, based on purchase history, it can collect data related to specific seasons or events. Furthermore, the data collection unit can analyze purchase history and apply different data collection methods to repeat customers and new customers. For example, it can analyze purchase history and apply different data collection methods to repeat customers and new customers. This allows the system to analyze the target audience's past purchase history and select the optimal data collection method.

[0091] The data collection unit can filter behavioral data based on the target audience's current areas of interest. For example, the data collection unit can filter the data to be collected based on keywords related to the current areas of interest. The data collection unit can also analyze social media trends and prioritize the collection of data related to areas of interest. For example, it can analyze social media trends and prioritize the collection of data related to areas of interest. Furthermore, the data collection unit can collect data related to current areas of interest based on the user's search history. For example, it can collect data related to current areas of interest based on the user's search history. This allows the data to be filtered based on the target audience's current areas of interest.

[0092] The data collection unit can estimate the user's emotions and prioritize the data to collect based on those emotions. For example, if the user is excited, the data collection unit will prioritize collecting real-time data to capture immediate reactions. The data collection unit can also prioritize collecting periodic data to understand long-term trends if the user is relaxed. Furthermore, if the user is stressed, the data collection unit can reduce the frequency of collection to alleviate the user's burden. This allows the data collection unit to prioritize the data to collect based on the user's emotions.

[0093] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of the target group when collecting behavioral data. For example, the data collection unit can prioritize the collection of purchasing behavior in a specific region based on geographical location information. The data collection unit can also prioritize the collection of data related to specific events or seasons based on geographical location information. For example, the data collection unit can prioritize the collection of data related to specific events or seasons based on geographical location information. Furthermore, the data collection unit can also prioritize the collection of data related to areas of interest in each region based on geographical location information. For example, the data collection unit can prioritize the collection of data related to areas of interest in each region based on geographical location information. This allows for the priority collection of highly relevant data by considering the geographical location information of the target group.

[0094] The data collection unit can analyze the social media activities of the target audience and collect relevant data when collecting behavioral data. For example, the data collection unit can analyze the content of social media posts and collect data related to areas of interest. The data collection unit can also prioritize the collection of data from influential users based on the number of social media followers and engagement rates. For example, it can prioritize the collection of data from influential users based on the number of social media followers and engagement rates. Furthermore, the data collection unit can analyze social media trends and collect data related to current areas of interest. For example, it can analyze social media trends and collect data related to current areas of interest. This allows for the analysis of the target audience's social media activities and the collection of relevant data.

[0095] The analytics unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is excited, the analytics unit can analyze the data in real time to capture immediate reactions. The analytics unit can also analyze data periodically to understand long-term trends if the user is relaxed. For example, if the user is relaxed, the analytics unit can analyze data periodically to understand long-term trends. Furthermore, if the user is stressed, the analytics unit can reduce the frequency of analysis to alleviate the user's burden. For example, if the user is stressed, the analysis frequency can be reduced. This allows the data analysis method to be adjusted based on the user's emotions.

[0096] The analysis department can adjust the level of detail of the analysis based on the importance of the collected data. For example, the analysis department can perform a detailed analysis on high-importance data to gain deeper insights. The analysis department can also perform a simplified analysis on low-importance data to conserve resources. For example, it can perform a simplified analysis on low-importance data to conserve resources. Furthermore, the analysis department can prioritize analyses based on data importance and allocate resources efficiently. For example, it can prioritize analyses based on data importance and allocate resources efficiently. This allows the level of detail of the analysis to be adjusted based on the importance of the collected data.

[0097] The analysis unit can apply different analysis algorithms depending on the data category. For example, it can apply an algorithm to identify purchase patterns to purchase history data. Furthermore, it can apply sentiment analysis algorithms to social media data. Additionally, it can apply algorithms to identify regional trends to geographic location data. This allows for the application of different analysis algorithms depending on the data category.

[0098] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is excited, the analysis unit can provide a visually stimulating display method. The analysis unit can also provide a calming display method if the user is relaxed. Furthermore, if the user is stressed, the analysis unit can provide a simple and easy-to-read display method. This allows the display method of the analysis results to be adjusted based on the user's emotions.

[0099] The analytics department can prioritize analyses based on when the collected data is submitted. For example, it can prioritize analyzing the most recent data to gain immediate insights. The analytics department can also postpone analyzing older data to conserve resources. Furthermore, the analytics department can adjust the analysis schedule according to the data submission timing to efficiently allocate resources. This allows the analytics department to prioritize analyses based on when the collected data is submitted.

[0100] The analysis unit can adjust the order of analysis based on the relevance of the collected data. For example, the analysis unit can prioritize analyzing highly relevant data to gain deeper insights. The analysis unit can also postpone analyzing less relevant data to conserve resources. Furthermore, the analysis unit can adjust the analysis schedule according to the relevance of the data to efficiently allocate resources. This allows the analysis order to be adjusted based on the relevance of the collected data.

[0101] The suggestion function can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is excited, the suggestion function can present visually stimulating suggestions. It can also present calming suggestions if the user is relaxed. Furthermore, if the user is stressed, the suggestion function can present simple and easily visible suggestions. This allows the suggestion function to adjust its presentation based on the user's emotions.

[0102] The proposal team can adjust the level of detail in their proposals based on the importance of the concepts. For example, for high-importance concepts, the proposal team can provide detailed proposals and deeper insights. Furthermore, for low-importance concepts, the proposal team can provide simplified proposals to conserve resources. In addition, the proposal team can prioritize proposals based on the importance of the concepts and allocate resources efficiently. This allows for adjusting the level of detail in proposals based on the importance of the concepts.

[0103] The proposal department can apply different proposal algorithms depending on the category of the concept. For example, for a product concept, it can apply a proposal algorithm that emphasizes product characteristics. For a service concept, it can apply a proposal algorithm that emphasizes the convenience of the service. Furthermore, for a brand concept, it can apply a proposal algorithm that emphasizes the brand image. In this way, different proposal algorithms can be applied depending on the category of the concept.

[0104] The suggestion function can estimate the user's emotions and adjust the length of the suggestion based on those emotions. For example, if the user is excited, the suggestion function can make a short, to-the-point suggestion. It can also make a longer suggestion with more detailed explanations if the user is relaxed. Furthermore, if the user is stressed, the suggestion function can make a simple, visually appealing suggestion. This allows the suggestion function to adjust the length of the suggestion based on the user's emotions.

[0105] The proposal team can prioritize proposals based on the timing of concept submissions. For example, the proposal team can prioritize proposals for the latest concepts to gain immediate insights. The proposal team can also postpone proposals for older concepts to conserve resources. Furthermore, the proposal team can adjust the proposal schedule according to the timing of concept submissions to efficiently allocate resources. This allows the proposal team to prioritize proposals based on the timing of concept submissions.

[0106] The proposal team can adjust the order of proposals based on the relevance of the concepts. For example, the proposal team can prioritize proposals for highly relevant concepts to gain deeper insights. The proposal team can also postpone less relevant concepts to conserve resources. For example, they can postpone less relevant concepts to conserve resources. Furthermore, the proposal team can adjust the proposal schedule according to the relevance of the concepts to allocate resources efficiently. For example, they can adjust the proposal schedule according to the relevance of the concepts to allocate resources efficiently. This allows the order of proposals to be adjusted based on the relevance of the concepts.

[0107] The Strategy Department can estimate user emotions and adjust its strategy formulation process based on those emotions. For example, if a user is excited, the Strategy Department can formulate a quickly actionable strategy. Alternatively, if a user is relaxed, the Strategy Department can formulate a strategy that includes detailed planning. Furthermore, if a user is stressed, the Strategy Department can formulate a simple and easy-to-implement strategy. This allows the Strategy Department to adjust its strategy formulation process based on user emotions.

[0108] The Strategy Department can select the optimal strategy by referring to past success stories when formulating a strategy. For example, the Strategy Department can analyze past success stories and select strategies applicable to similar situations. The Strategy Department can also identify the factors behind success stories and customize strategies based on them. For example, the Strategy Department can identify the factors behind success stories and customize strategies based on them. Furthermore, the Strategy Department can simulate and select the optimal strategy based on data from success stories. For example, the Strategy Department can simulate and select the optimal strategy based on data from success stories. This allows for the selection of the optimal strategy by referring to past success stories.

[0109] The Strategy Department can customize strategies by considering the attribute information of the target group when formulating them. For example, the Strategy Department can adjust the content of the strategy according to the age group of the target group. The Strategy Department can also set the focus of the strategy based on the interests of the target group. For example, the focus of the strategy is set based on the interests of the target group. Furthermore, the Strategy Department can select the implementation area of ​​the strategy by considering the geographical distribution of the target group. For example, the implementation area of ​​the strategy is selected by considering the geographical distribution of the target group. This allows for the customization of strategies by considering the attribute information of the target group.

[0110] The strategy department can estimate the user's emotions and prioritize strategies based on those emotions. For example, if the user is excited, the strategy department will prioritize strategies that can be implemented immediately. The strategy department can also prioritize long-term strategies if the user is relaxed. Furthermore, if the user is stressed, the strategy department can prioritize simple and easy-to-implement strategies. This allows for the prioritization of strategies based on the user's emotions.

[0111] The Strategy Department can select the optimal strategy when formulating a strategy, taking into account the geographical distribution of the target audience. For example, the Strategy Department can prioritize the formulation of strategies for specific regions based on geographical distribution. The Strategy Department can also formulate strategies tailored to the characteristics of each region, based on geographical distribution. For example, the Strategy Department can formulate strategies tailored to the characteristics of each region, based on geographical distribution. Furthermore, the Strategy Department can formulate strategies considering market trends in each region, based on geographical distribution. For example, the Strategy Department can formulate strategies considering market trends in each region, based on geographical distribution. This allows for the selection of the optimal strategy, taking into account the geographical distribution of the target audience.

[0112] The Strategy Department can improve the accuracy of its strategies by referring to relevant literature during the strategy formulation process. For example, the Strategy Department can formulate strategies that incorporate the latest marketing trends based on relevant literature. The Strategy Department can also formulate strategies that draw on past success stories based on relevant literature. For example, the Strategy Department can formulate strategies that draw on past success stories based on relevant literature. Furthermore, the Strategy Department can formulate strategies that have theoretical backing based on relevant literature. For example, the Strategy Department can formulate strategies that have theoretical backing based on relevant literature. This allows the Strategy Department to improve the accuracy of its strategies by referring to relevant literature.

[0113] The creation unit can estimate the user's emotions and adjust how the advertising concept is created based on those estimated emotions. For example, if the user is excited, the creation unit can create a visually stimulating advertising concept. Similarly, if the user is relaxed, the creation unit can create a calming advertising concept. Furthermore, if the user is stressed, the creation unit can create a simple and highly visible advertising concept. This allows the creation of advertising concepts to be adjusted based on the user's emotions.

[0114] The creation department can select the optimal advertising concept by referring to past success stories when creating an advertising concept. For example, the creation department can analyze past success stories and select an advertising concept that can be applied to similar situations. The creation department can also identify the factors behind success stories and customize the advertising concept based on them. For example, they can identify the factors behind success stories and customize the advertising concept based on them. Furthermore, the creation department can simulate and select the optimal advertising concept based on data from success stories. For example, they can simulate and select the optimal advertising concept based on data from success stories. This allows for the selection of the optimal concept by referring to past success stories.

[0115] The creation department can customize advertising concepts by considering the attribute information of the target audience. For example, the creation department can adjust the content of the advertising concept according to the age group of the target audience. The creation department can also set the focus of the advertising concept based on the interests of the target audience. For example, the focus of the advertising concept is set based on the interests of the target audience. Furthermore, the creation department can select the execution area of ​​the advertising concept by considering the geographical distribution of the target audience. For example, the execution area of ​​the advertising concept is selected by considering the geographical distribution of the target audience. This allows for the customization of concepts by considering the attribute information of the target audience.

[0116] The development team can estimate the user's emotions and prioritize advertising concepts based on those emotions. For example, if the user is excited, the development team will prioritize advertising concepts that can be implemented immediately. The development team can also prioritize long-term advertising concepts if the user is relaxed. Furthermore, if the user is stressed, the development team can prioritize simple and easy-to-implement advertising concepts. This allows for the prioritization of advertising concepts based on the user's emotions.

[0117] The creation department can select the optimal advertising concept by considering the geographical distribution of the target audience. For example, the creation department can prioritize creating advertising concepts for specific regions based on geographical distribution. The creation department can also create advertising concepts tailored to the characteristics of each region based on geographical distribution. For example, the creation department can create advertising concepts tailored to the characteristics of each region based on geographical distribution. Furthermore, the creation department can create advertising concepts by considering market trends in each region based on geographical distribution. For example, the creation department can create advertising concepts by considering market trends in each region based on geographical distribution. This allows for the selection of the optimal concept by considering the geographical distribution of the target audience.

[0118] The creative team can improve the accuracy of advertising concepts by referring to relevant literature during the concept creation process. For example, the creative team can create advertising concepts that incorporate the latest marketing trends based on relevant literature. The creative team can also create advertising concepts that refer to past success stories based on relevant literature. For example, the creative team can create advertising concepts that refer to past success stories based on relevant literature. Furthermore, the creative team can create advertising concepts that have theoretical backing based on relevant literature. For example, the creative team can create advertising concepts that have theoretical backing based on relevant literature. This allows for improvement in the accuracy of concepts by referring to relevant literature.

[0119] The needs assessment unit can estimate the user's emotions and adjust the needs assessment method based on the estimated user emotions. For example, if the user is excited, the needs assessment unit can assess their needs in real time and capture their immediate reactions. The needs assessment unit can also assess regular needs to understand long-term trends when the user is relaxed. For example, if the user is relaxed, it can assess regular needs to understand long-term trends. Furthermore, if the user is stressed, the needs assessment unit can reduce the frequency of needs assessment to alleviate the user's burden. For example, if the user is stressed, it can reduce the frequency of needs assessment. This allows the needs assessment method to be adjusted based on the user's emotions.

[0120] The needs assessment unit can analyze past consumer behavior to select the optimal needs assessment method. For example, the needs assessment unit can assess needs for a specific product category based on past consumer behavior. The needs assessment unit can also analyze consumer behavior to assess needs related to specific seasons or events. For example, it can analyze consumer behavior to assess needs related to specific seasons or events. Furthermore, the needs assessment unit can apply different needs assessment methods to repeat customers and new customers based on their consumer behavior. For example, it can apply different needs assessment methods to repeat customers and new customers based on their consumer behavior. This allows the system to analyze past consumer behavior and select the optimal needs assessment method.

[0121] The needs assessment unit can estimate the user's emotions and prioritize needs based on those emotions. For example, if the user is excited, the needs assessment unit will prioritize needs that can be addressed immediately. It can also prioritize long-term needs if the user is relaxed. Furthermore, if the user is stressed, the needs assessment unit will prioritize simple and easy-to-implement needs. This allows for the prioritization of needs based on the user's emotions.

[0122] The needs assessment unit can select the optimal needs assessment method by considering the geographical location information of the target group when assessing needs. For example, the needs assessment unit can prioritize the assessment of needs in a specific region based on geographical location information. The needs assessment unit can also select a needs assessment method that is appropriate for the characteristics of each region based on geographical location information. For example, the needs assessment unit can select a needs assessment method that is appropriate for the characteristics of each region based on geographical location information. Furthermore, the needs assessment unit can assess needs by considering market trends in each region based on geographical location information. For example, the needs assessment unit can assess needs by considering market trends in each region based on geographical location information. This allows for the selection of the optimal needs assessment method by considering the geographical location information of the target group.

[0123] The campaign execution unit can estimate the user's emotions and adjust the campaign execution method based on the estimated user emotions. For example, if the user is excited, the campaign execution unit can execute an immediately available campaign. The campaign execution unit can also execute a long-term campaign if the user is relaxed. Furthermore, if the user is stressed, the campaign execution unit can execute a simple and easy-to-execute campaign. In this way, the campaign execution method can be adjusted based on the user's emotions.

[0124] The campaign execution unit can select the optimal execution method by referring to past campaign data when executing a campaign. For example, the campaign execution unit can analyze past successful campaign data and select an execution method applicable to similar situations. The campaign execution unit can also identify the factors of successful campaigns and customize the execution method based on them. Furthermore, the campaign execution unit can simulate and select the optimal execution method based on past campaign data. For example, it can simulate and select the optimal execution method based on past campaign data. This allows the optimal execution method to be selected by referring to past campaign data.

[0125] The campaign execution unit can customize the execution method when executing a campaign, taking into account the attribute information of the target audience. For example, the campaign execution unit can adjust the content of the campaign according to the age group of the target audience. The campaign execution unit can also set the focus of the campaign based on the interests of the target audience. For example, it sets the focus of the campaign based on the interests of the target audience. Furthermore, the campaign execution unit can select the execution area of ​​the campaign considering the geographical distribution of the target audience. For example, it selects the execution area of ​​the campaign considering the geographical distribution of the target audience. This allows the execution method to be customized by taking into account the attribute information of the target audience.

[0126] The campaign execution unit can estimate the user's emotions and prioritize campaigns based on those emotions. For example, if the user is excited, the campaign execution unit will prioritize campaigns that can be executed immediately. It can also prioritize long-term campaigns if the user is relaxed. Furthermore, if the user is stressed, the campaign execution unit can prioritize simple and easy-to-execute campaigns. This allows campaign prioritization to be determined based on the user's emotions.

[0127] The campaign execution unit can select the optimal execution method when executing a campaign, taking into account the geographical location information of the target audience. For example, the campaign execution unit can prioritize the execution of campaigns in specific regions based on geographical location information. The campaign execution unit can also execute campaigns tailored to the characteristics of each region based on geographical location information. For example, the campaign execution unit can execute campaigns tailored to the characteristics of each region based on geographical location information. Furthermore, the campaign execution unit can execute campaigns while considering market trends in each region based on geographical location information. For example, the campaign execution unit can execute campaigns while considering market trends in each region based on geographical location information. This allows the system to select the optimal execution method, taking into account the geographical location information of the target audience.

[0128] The campaign execution unit can improve the accuracy of its execution methods by referring to relevant literature during campaign execution. For example, the campaign execution unit can execute campaigns that incorporate the latest marketing trends based on relevant literature. The campaign execution unit can also execute campaigns that refer to past success stories based on relevant literature. For example, the campaign execution unit can execute campaigns that refer to past success stories based on relevant literature. Furthermore, the campaign execution unit can execute campaigns that have theoretical backing based on relevant literature. For example, the campaign execution unit can execute campaigns that have theoretical backing based on relevant literature. This allows the accuracy of the execution methods to be improved by referring to relevant literature.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The CreativeSpark AI system can also be equipped with a feedback collection unit. This unit collects the target audience's responses to the implemented campaign. For example, it can collect comments and reviews from social media to understand the target audience's opinions and feelings. It can also conduct surveys to obtain detailed feedback on the campaign's effectiveness. Furthermore, it can analyze post-campaign purchase data to evaluate the campaign's impact. This allows the CreativeSpark AI system to evaluate the effectiveness of the implemented campaign and incorporate the findings into future campaigns.

[0131] The CreativeSpark AI system can also be equipped with an emotion estimation unit. This unit estimates the emotions of the target audience and adjusts the campaign content based on the estimated emotions. For example, if the target audience is excited, the emotion estimation unit will suggest visually stimulating advertisements. It can also suggest advertisements with a calm tone if the target audience is relaxed. Furthermore, if the target audience is stressed, the emotion estimation unit can suggest simple and highly visible advertisements. This allows the CreativeSpark AI system to suggest the most suitable advertisements based on the emotions of the target audience.

[0132] The CreativeSpark AI system can also include a competitive analysis department. This department analyzes competitors' campaigns and derives competitive strategies. For example, it collects competitors' advertising content and evaluates its effectiveness. It can also analyze competitors' marketing strategies and identify success factors. Furthermore, it can analyze the timing and frequency of competitors' campaigns and incorporate this into your own campaign plan. This allows the CreativeSpark AI system to provide strategies for gaining a competitive edge.

[0133] The CreativeSpark AI system can also be equipped with a personalized suggestion unit. This unit proposes personalized advertising concepts based on the individual preferences and behaviors of the target audience. For example, it can analyze a specific user's past purchase history and suggest the most suitable advertisement for that user. It can also analyze a user's social media activity and suggest advertisements related to topics of interest. Furthermore, it can suggest advertisements that are likely to interest the user based on their search history. This allows the CreativeSpark AI system to provide advertisements tailored to the individual needs of the target audience.

[0134] The CreativeSpark AI system can also be equipped with a real-time adjustment unit. This unit collects data in real time during a campaign and adjusts the campaign content as needed. For example, it monitors social media reactions and changes the ad content if there are many negative reactions. It can also analyze website traffic data and change ad placement if it is not performing well. Furthermore, it can analyze purchase data in real time and strengthen promotions if sales are not growing. This allows the CreativeSpark AI system to make real-time adjustments to maximize the effectiveness of the campaign.

[0135] The CreativeSpark AI system can further utilize its emotion estimation capabilities to create interactive advertisements based on the emotions of the target audience. For example, if a user is excited, it can create an advertisement incorporating game elements. If the user is relaxed, it can create an advertisement using relaxing music and visuals. Furthermore, if the user is stressed, it can create a simple and highly visible advertisement. In this way, the CreativeSpark AI system can provide interactive advertisements that are tailored to the emotions of the target audience.

[0136] The CreativeSpark AI system can further adjust ad delivery timing based on the emotions of the target audience using its emotion estimation function. For example, if a user is excited, the system can deliver ads immediately. If the user is relaxed, ads can be delivered at regular intervals. Furthermore, if the user is stressed, the ad delivery frequency can be reduced to lessen the user's burden. In this way, the CreativeSpark AI system can provide optimal ad delivery timing tailored to the emotions of the target audience.

[0137] The CreativeSpark AI system can also dynamically change ad content based on the target audience's emotions using its emotion estimation function. For example, if a user is excited, it can change to a visually stimulating ad. If the user is relaxed, it can change to an ad with a calming tone. Furthermore, if the user is stressed, it can change to a simple and highly visible ad. In this way, the CreativeSpark AI system can provide optimal ad content tailored to the emotions of the target audience.

[0138] The CreativeSpark AI system can further utilize its emotion estimation capabilities to select ad formats based on the target audience's emotions. For example, using emotion estimation, it can select video ads if the user is excited, still image ads if the user is relaxed, and text ads if the user is stressed. This allows the CreativeSpark AI system to provide the optimal ad format tailored to the target audience's emotions.

[0139] The CreativeSpark AI system can further evaluate the effectiveness of ads based on the emotions of the target audience by using its emotion estimation function. For example, it can use emotion estimation to evaluate the click-through rate of ads when users are excited, and to evaluate the viewing time of ads when users are relaxed. Furthermore, it can evaluate the engagement rate of ads when users are stressed. This allows the CreativeSpark AI system to evaluate the effectiveness of ads based on the emotions of the target audience and reflect that in future campaigns.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The data collection unit collects behavioral data of the target audience. For example, it can collect behavioral data such as website browsing history, purchase history, and social media activity. The data collection unit collects website browsing history to understand the target audience's interests. It can also collect purchase history to analyze the target audience's purchasing patterns. Furthermore, it can collect social media activity to identify the target audience's areas of interest. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, it uses AI to analyze the collected data and identify the interests of the target audience. The AI ​​can analyze the collected data to identify the target audience's interests, purchasing patterns, and areas of interest. Step 3: The proposal department proposes unique concepts based on the analysis results obtained by the analysis department. For example, AI can be used to propose unique concepts based on the analysis results. AI can propose unique advertising and marketing concepts based on the analysis results. Step 4: The Strategy Department derives the optimal strategy based on the concept proposed by the Proposal Department. For example, they can use AI to derive the optimal strategy based on the proposed concept. AI can derive the optimal marketing and advertising strategies based on the proposed concept. Step 5: The creation team creates advertising concepts based on the strategies derived by the strategy team. For example, they create advertising concepts based on strategies derived using AI. AI can create advertising concepts, marketing concepts, and advertising campaigns based on strategies derived by AI.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0144] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, strategy unit, creation unit, needs assessment unit, and campaign execution unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects behavioral data of the target audience using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit analyzes the collected data with, for example, the specific processing unit 290 of the data processing unit 12 to identify the interests of the target audience. The proposal unit proposes a unique concept based on the analysis results with, for example, the specific processing unit 290 of the data processing unit 12. The strategy unit derives an optimal strategy based on the proposed concept with, for example, the specific processing unit 290 of the data processing unit 12. The creation unit creates an advertising concept based on the strategy derived with, for example, the control unit 46A of the smart device 14. The needs assessment unit accurately grasps consumer needs with, for example, the specific processing unit 290 of the data processing unit 12. The campaign execution unit implements an influential campaign with, for example, the control unit 46A of the smart device 14. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, strategy unit, creation unit, needs assessment unit, and campaign execution unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit collects behavioral data of the target audience using the camera 42 and microphone 238 of the smart glasses 214 and processes the data with the control unit 46A. The analysis unit analyzes the collected data with the specific processing unit 290 of the data processing unit 12 and identifies the interests of the target audience. The proposal unit proposes a unique concept based on the analysis results with the specific processing unit 290 of the data processing unit 12. The strategy unit derives an optimal strategy based on the proposed concept with the specific processing unit 290 of the data processing unit 12. The creation unit creates an advertising concept based on the strategy derived with the control unit 46A of the smart glasses 214. The needs assessment unit accurately grasps consumer needs with the specific processing unit 290 of the data processing unit 12. The campaign execution unit implements an influential campaign with the control unit 46A of the smart glasses 214. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, strategy unit, creation unit, needs assessment unit, and campaign execution unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects behavioral data of the target audience using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit analyzes the collected data with, for example, the specific processing unit 290 of the data processing unit 12 to identify the interests of the target audience. The proposal unit proposes a unique concept based on the analysis results with, for example, the specific processing unit 290 of the data processing unit 12. The strategy unit derives an optimal strategy based on the proposed concept with, for example, the specific processing unit 290 of the data processing unit 12. The creation unit creates an advertising concept based on the strategy derived with, for example, the control unit 46A of the headset terminal 314. The needs assessment unit accurately grasps consumer needs with, for example, the specific processing unit 290 of the data processing unit 12. The campaign execution unit, for example, implements an influential campaign using the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0180] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0181] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0182] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0184] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0185] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0186] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0187] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0188] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0189] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0190] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0191] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0192] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0193] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0194] Each of the multiple elements described above, including the data collection unit, analysis unit, proposal unit, strategy unit, creation unit, needs assessment unit, and campaign execution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit collects behavioral data of the target group using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit analyzes the collected data with, for example, the specific processing unit 290 of the data processing unit 12 to identify the interests of the target group. The proposal unit proposes a unique concept based on the analysis results with, for example, the specific processing unit 290 of the data processing unit 12. The strategy unit derives an optimal strategy based on the proposed concept with, for example, the specific processing unit 290 of the data processing unit 12. The creation unit creates an advertising concept based on the strategy derived with, for example, the control unit 46A of the robot 414. The needs assessment unit accurately grasps consumer needs with, for example, the specific processing unit 290 of the data processing unit 12. The campaign execution unit implements an influential campaign with, for example, the control unit 46A of the robot 414. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0195] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0196] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0197] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0198] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0199] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0200] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0201] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0202] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0203] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0204] 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.

[0205] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0206] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0207] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0208] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0209] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0210] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0211] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0212] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0213] (Note 1) A data collection unit that collects behavioral data of the target group, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a unique concept. The Strategy Department derives the optimal strategy based on the concept proposed by the aforementioned Proposal Department, The system comprises a creation unit that creates advertising concepts based on the strategies derived by the aforementioned strategy unit. A system characterized by the following features. (Note 2) Equipped with a needs assessment unit to accurately capture consumer needs. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes a campaign execution unit to deliver influential campaigns. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned collection unit is Collect past behavioral data of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is Analyze the collected data to identify the interests of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, Propose a unique concept based on identified interests. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned Strategy Department, Develop the optimal strategy based on the proposed concept. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned creation unit, Create an advertising concept based on the derived strategy. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Analyze the target audience's past purchase history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting behavioral data, filter it based on the target audience's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is When collecting behavioral data, prioritize the collection of highly relevant data by considering the geographical location information of the target group. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting behavioral data, analyze the social media activity of the target audience and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is We estimate user emotions and adjust the data analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is Adjust the level of detail of the analysis based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is Apply different analytical algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is Prioritize analysis based on when the collected data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is Adjust the order of analysis based on the relevance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the concept. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the concept. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When submitting a proposal, prioritize the proposals based on when the concept was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the concepts. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Strategy Department, We estimate user sentiment and adjust our strategy development methods based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Strategy Department, When formulating a strategy, refer to past success stories to select the optimal strategy. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Strategy Department, When formulating a strategy, customize it by taking into account the attribute information of the target group. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Strategy Department, We estimate user sentiment and prioritize strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Strategy Department, When formulating a strategy, the optimal strategy should be selected by considering the geographical distribution of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Strategy Department, When formulating a strategy, refer to relevant literature to improve the accuracy of the strategy. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned creation unit, We estimate user emotions and adjust how ad concepts are created based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned creation unit, When creating an advertising concept, refer to past successful examples to select the most suitable concept. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned creation unit, When creating an advertising concept, customize the concept by taking into account the attributes of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned creation unit, We estimate user emotions and prioritize advertising concepts based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned creation unit, When creating an advertising concept, select the most suitable concept by considering the geographical distribution of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned creation unit, When creating an advertising concept, refer to relevant literature to improve the accuracy of the concept. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned needs assessment unit, We estimate user emotions and adjust how we understand their needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned needs assessment unit, When identifying consumer needs, we analyze past consumer behavior to select the most suitable method for understanding those needs. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned needs assessment unit, It estimates the user's emotions and prioritizes their needs based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned needs assessment unit, When identifying needs, the most suitable method of needs assessment is selected by considering the geographical location information of the target group. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned campaign execution unit, It estimates user sentiment and adjusts how campaigns are executed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned campaign execution unit, When running a campaign, the optimal execution method is selected by referring to past campaign data. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned campaign execution unit, When running a campaign, customize the execution method by taking into account the attribute information of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 46) The aforementioned campaign execution unit, The system estimates user sentiment and prioritizes campaigns based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 47) The aforementioned campaign execution unit, When executing a campaign, the optimal execution method is selected considering the geographical location information of the target audience. The system described in Appendix 1, characterized by the features described herein. (Note 48) The aforementioned campaign execution unit, When executing a campaign, refer to relevant literature to improve the accuracy of the execution method. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects behavioral data of the target group, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis unit, the proposal unit proposes a unique concept. The Strategy Department derives the optimal strategy based on the concept proposed by the aforementioned Proposal Department, The system comprises a creation unit that creates advertising concepts based on the strategies derived by the aforementioned strategy unit. A system characterized by the following features.

2. Equipped with a needs assessment unit to accurately capture consumer needs. The system according to feature 1.

3. It includes a campaign execution unit to deliver influential campaigns. The system according to feature 1.

4. The aforementioned collection unit is Collect past behavioral data of the target audience. The system according to feature 1.

5. The aforementioned analysis unit is Analyze the collected data to identify the interests of the target audience. The system according to feature 1.

6. The aforementioned proposal section is, Propose a unique concept based on identified interests. The system according to feature 1.

7. The aforementioned Strategy Department, Develop the optimal strategy based on the proposed concept. The system according to feature 1.

8. The aforementioned creation unit, Create an advertising concept based on the derived strategy. The system according to feature 1.

9. The aforementioned collection unit is We estimate the user's emotions and adjust the timing of behavioral data collection based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is Analyze the target audience's past purchase history and select the optimal data collection method. The system according to feature 1.

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