system

Abstract

The system according to this embodiment aims to perform effective perception changes based on data. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a formulation unit, and an execution unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The formulation unit formulates a strategy based on the analysis results obtained by the analysis unit. The execution unit executes perception change through LY advertisements based on the strategy formulated by the formulation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, an effective perception change based on data has not been sufficiently performed, and there is room for improvement.

[0005] The system according to the embodiment aims to perform an effective perception change based on data.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a strategy formulation unit, and an execution unit. The data collection unit collects data. The analysis unit analyzes the data collected by the data collection unit. The strategy formulation unit formulates a strategy based on the analysis results obtained by the analysis unit. The execution unit executes perception change through LY advertisements based on the strategy formulated by the strategy formulation unit. [Effects of the Invention]

[0007] The system according to this embodiment can perform effective perception changes based on data. [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 numbered 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 applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

[0018] The data processing device 1 is provided with 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 perception change support system according to an embodiment of the present invention is a system that quantitatively expresses perception and supports increased sales for clients by inducing perception change starting with LY advertisements. The perception change support system is a mechanism that quantitatively expresses perception and supports increased sales for clients by inducing perception change starting with LY advertisements. Specifically, it consists of the following steps. First, "measure" the perception of the target category or product / service. Next, formulate a strategy based on the measured perception and "change" or "create" perception. Finally, realize perception change starting with LY advertisements. First, "measure" the perception of the target category or product / service. At this time, LY data and SB Group assets are used to quantitatively express consumer perception. This makes it possible to understand how consumers perceive products and services. Next, formulate a strategy based on the measured perception. Specifically, this involves developing strategies to "change" or "create" perceptions. For example, planning advertising campaigns to positively alter consumer perceptions. It also involves considering measures to provide new value that consumers will newly recognize. This allows for effective control of consumer perceptions. Finally, perception change is achieved starting with LY advertising. Specifically, strategic messages are sent to consumers through LY advertising to promote perception change. For example, the content of advertisements is customized to match consumer perceptions, effectively reaching the target audience. This changes consumer perceptions and helps increase client sales. This mechanism allows companies to accurately understand consumer perceptions and develop effective strategies. Furthermore, by utilizing LY advertising, it becomes possible to achieve perception change and support increased sales for clients. For example, by positively changing consumers' perceptions of a particular product or service, purchasing intent can be increased. Also, by providing new value, consumer interest can be attracted, and sales can be improved.This allows the perception change support system to accurately understand consumer perceptions, develop effective strategies, and help clients increase their sales.

[0029] The perception change support system according to this embodiment comprises a collection unit, an analysis unit, a formulation unit, and an execution unit. The collection unit collects data. The collection unit can collect data in the form of, for example, text data, numerical data, and image data. The collection unit can collect data from websites, social media, and sensor data, for example. The collection unit can collect data from websites using web scraping technology, for example. The collection unit can also collect user posts using social media APIs. Furthermore, the collection unit can collect environmental data in real time using IoT sensors. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis, machine learning algorithms, and natural language processing technology. For example, the analysis unit can analyze collected text data using natural language processing technology to extract consumer sentiment and opinions. The analysis unit can also analyze collected numerical data using statistical analysis methods to identify trends and patterns. Furthermore, the analysis unit can analyze collected image data using image recognition technology to recognize and classify objects. The Strategy Department formulates strategies based on the analysis results obtained by the Analysis Department. For example, the Strategy Department can formulate marketing strategies, sales strategies, and promotional strategies. For example, the Strategy Department plans advertising campaigns targeting specific segments based on consumer sentiment and opinions. Furthermore, the Strategy Department can develop new product development and sales strategies based on trends and patterns. In addition, the Strategy Department can formulate product improvement and quality control strategies based on object recognition and classification results. The Execution Department implements perception change through LY advertising based on the strategies formulated by the Strategy Department. For example, the Execution Department can deliver strategic messages to consumers through online advertising, television advertising, radio advertising, and print advertising. For example, the Execution Department uses online advertising platforms to deliver customized advertisements to target segments. The Execution Department can also convey messages to a broad audience through television advertising.Furthermore, the execution unit can effectively reach local consumers through radio and print advertisements. As a result, the perception change support system according to this embodiment can effectively achieve perception change and support clients in increasing sales by consistently handling everything from data collection and analysis to strategy formulation and execution.

[0030] The data collection unit collects data. The data collection unit can collect data in various formats, such as text data, numerical data, and image data. Specifically, the data collection unit can collect data from websites, social media, and sensor data. When collecting data from websites using web scraping technology, the data collection unit analyzes the HTML structure of specific web pages and extracts the necessary information. For example, it can collect consumer ratings and comments from product review sites. When collecting user posts using social media APIs, the data collection unit filters posts based on specific keywords and hashtags and obtains relevant data. For example, it can collect consumer opinions and sentiments regarding specific brands. Furthermore, when collecting environmental data in real time using IoT sensors, the data collection unit acquires data such as temperature, humidity, and light intensity from the sensors and transmits it to a central database. This allows the data collection unit to collect a wide range of data from diverse data sources and understand the situation in real time. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and planning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis department analyzes the data collected by the data collection department. The analysis department can analyze data using methods such as statistical analysis, machine learning algorithms, and natural language processing techniques. Specifically, it can analyze collected text data using natural language processing techniques to extract consumer sentiment and opinions. Natural language processing techniques include tokenization, morphological analysis, sentiment analysis, and topic modeling. For example, it can classify positive and negative opinions from consumer reviews to understand evaluations of products and services. It can also analyze collected numerical data using statistical analysis methods to identify trends and patterns. For example, it can analyze sales data over time to evaluate seasonal sales fluctuations and the effectiveness of specific campaigns. Furthermore, it can analyze collected image data using image recognition techniques to recognize and classify objects. Image recognition techniques include convolutional neural networks (CNNs) and object detection algorithms. For example, it can detect brand logos from product images to analyze which brands consumers are interested in. This allows the analysis department to analyze collected data from multiple perspectives and gain a deeper understanding of consumer behavior and opinions. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term trend and risk assessments. For example, it can use past sales data to forecast future sales and aid in developing marketing strategies. This allows the analytics department to not only grasp the situation in real time but also to formulate long-term strategies, improving the overall reliability and effectiveness of the system.

[0032] The Strategy Planning Department formulates strategies based on the analysis results obtained by the Analysis Department. For example, the Strategy Planning Department can formulate marketing strategies, sales strategies, and promotional strategies. Specifically, it plans advertising campaigns targeting specific segments based on consumer sentiment and opinions. For instance, for products with many positive opinions, it develops advertising that emphasizes the product's strengths, while for products with many negative opinions, it develops advertising that highlights areas for improvement. It can also develop new product development and sales strategies based on trends and patterns. For example, for products with increased sales during certain seasons, it develops seasonal promotions to maximize sales. Furthermore, it can formulate product improvement and quality control strategies based on object recognition and classification results. For example, if consumers highly value a particular design or function, it develops a new product with enhanced features. This allows the Strategy Planning Department to formulate concrete strategies based on analysis results and support clients in achieving their goals. Additionally, the Strategy Planning Department can evaluate the effectiveness of strategies and modify them as needed. For example, it can monitor the effectiveness of advertising campaigns and revise the target audience or messaging if the campaign is ineffective. Furthermore, the strategy planning department can develop strategies that reflect the client's needs and requests through communication with the client. This allows the strategy planning department to provide the client with the optimal strategy and maximize the client's business results.

[0033] The execution team implements perception change based on the strategy formulated by the planning team. Specifically, they can deliver strategic messages to consumers through online advertising, television advertising, radio advertising, and print advertising. When using online advertising platforms to deliver customized ads to target audiences, the execution team adjusts the ad creative and message to match the characteristics of the target audience. For example, they might use social media advertising and deliver visually-oriented content for younger demographics, while delivering ads containing specialized information for business professionals. When conveying a message to a broad audience through television advertising, the execution team selects the broadcast time and channel to match the characteristics of the audience. For example, ads for family-oriented products are broadcast during times when families are likely to be together. When effectively reaching local consumers through radio and print advertising, the execution team develops ads tailored to local characteristics and events. For example, they might conduct promotions that coincide with local festivals and events to attract the attention of local residents. This allows the execution team to effectively utilize each media and deliver the optimal message to the target audience. Furthermore, the execution team can monitor the effectiveness of advertising campaigns and modify the ad content and delivery methods as needed. For example, by analyzing the click-through rate and conversion rate of online ads, the execution team can review the ad creative and targeting if the results are poor. Furthermore, through feedback from clients, the execution team can identify areas for improvement in the advertising strategy and incorporate them into the next campaign. This allows the execution team to support clients in achieving their goals and effectively drive perception change.

[0034] The data collection unit can collect data by utilizing LY data and SB Group assets. For example, the data collection unit can use LY data to collect consumer behavior data and purchase history. The data collection unit can also utilize SB Group assets to collect consumer location data and search data. For example, the data collection unit can obtain consumer purchase history from the LY database and analyze consumer purchasing patterns. The data collection unit can also use SB Group's location information services to collect consumer movement history and identify consumer behavior patterns. Furthermore, the data collection unit can use SB Group's search engine to collect consumer search history and understand consumer areas of interest. This enables more detailed and accurate data collection by utilizing LY data and SB Group assets. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from the LY database into a generating AI and have the generating AI perform data analysis.

[0035] The analysis department can analyze collected data using AI to quantitatively represent consumer perceptions. For example, the analysis department can use deep learning technology to analyze collected data and quantify consumer emotions and opinions. The analysis department can also use natural language processing technology to analyze collected text data and score consumer perceptions. For example, the analysis department can use a deep learning model to classify consumer emotions into three categories: positive, negative, and neutral, and calculate an emotion score for each. The analysis department can also use natural language processing technology to analyze consumer opinions and score them based on specific keywords. Furthermore, the analysis department can use machine learning algorithms to analyze collected numerical data and identify consumer behavior patterns. For example, the analysis department can use clustering algorithms to analyze consumer purchase history and identify consumer groups with similar purchase patterns. This allows for an accurate and quantitative representation of consumer perceptions using AI. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the collected data into a generative AI and have the AI ​​perform the quantification of consumer perceptions.

[0036] The Strategy Department can develop strategies to "change" or "create" perceptions based on measured perceptions. For example, the Strategy Department might plan advertising campaigns to positively change consumer perceptions. It can also devise measures to provide new value that consumers will newly recognize. For example, it could create advertisements containing positive messages based on consumer sentiment scores to positively change consumer perceptions. It could also create advertisements that highlight the features and benefits of new products to provide consumers with new value. Furthermore, the Strategy Department could plan promotional activities for target groups based on consumer behavior patterns. For example, it could implement customized promotions for consumer groups with specific purchasing patterns to change consumer perceptions. This allows for the development of effective strategies based on measured perceptions. Some or all of the above processes in the Strategy Department may be performed using AI, for example, or not. For example, the Strategy Department could input measured perception data into a Generative AI and have the Generative AI develop strategies.

[0037] The execution unit can deliver strategic messages to consumers through LY ads and promote perception change. The execution unit can deliver strategic messages to consumers through, for example, online advertising, television advertising, radio advertising, and print advertising. The execution unit can, for example, use online advertising platforms to deliver customized ads to target audiences. The execution unit can also convey messages to a broad audience through television advertising. Furthermore, the execution unit can effectively reach local consumers through radio advertising and print advertising. This allows for effective promotion of perception change through LY ads. Some or all of the above processes in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit can input the content of the LY ad into a generating AI and have the generating AI customize the ad.

[0038] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection time slot from past data collection history and collect data during that time slot. The data collection unit can also adjust the collection frequency from specific data sources based on past data collection history. Furthermore, the data collection unit can analyze past data collection history to identify areas for improvement in the collection method and optimize it. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0039] The data collection unit can filter data based on the user's current areas of interest and behavioral patterns during data collection. For example, the data collection unit can prioritize collecting only data related to the user's current areas of interest. The data collection unit can also analyze the user's behavioral patterns and filter and collect highly relevant data. Furthermore, the data collection unit can collect new data related to areas of interest based on the user's past behavioral history. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest and behavioral pattern data into a generating AI and have the generating AI perform data filtering.

[0040] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect highly relevant data based on the user's travel history. Furthermore, the data collection unit can collect relevant data in real time based on the user's current location. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0041] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. The data collection unit can also collect data based on the user's social media interests. Furthermore, the data collection unit can analyze the activity of the user's followers and friends on social media and collect relevant data. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to high-importance data to obtain detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a natural language processing algorithm to text data and a statistical analysis algorithm to numerical data. It can also apply an image recognition algorithm to image data and a speech recognition algorithm to audio data. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the data category. This enables highly accurate analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select and apply the most suitable analysis algorithm.

[0044] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also determine the priority of analysis based on the data collection timing. Furthermore, the analysis unit can perform rapid analysis on data where the collection timing is important. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. Furthermore, the analysis unit can perform detailed analysis on highly relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0046] The strategy formulation department can adjust the level of detail of a strategy based on the importance of the data during the strategy formulation process. For example, the department can formulate a detailed strategy based on high-importance data and a simplified strategy based on low-importance data. The department can also prioritize strategies according to the importance of the data. Furthermore, the department can formulate multiple strategies based on high-importance data. This allows for efficient strategy formulation by adjusting the level of detail of strategies based on the importance of the data. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not. For example, the strategy formulation department can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the strategy.

[0047] The strategy formulation department can apply different strategic algorithms depending on the data category when formulating a strategy. For example, it can apply a natural language processing algorithm based on text data and a statistical analysis algorithm based on numerical data. It can also apply an image recognition algorithm based on image data and a speech recognition algorithm based on audio data. Furthermore, the strategy formulation department can select and apply the most appropriate strategic algorithm depending on the data category. This enables highly accurate strategy formulation by applying the most appropriate strategic algorithm according to the data category. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or without AI. For example, the strategy formulation department can input the data category into a generating AI and have the generating AI select and apply the most appropriate strategic algorithm.

[0048] The strategy formulation department can prioritize strategies based on the timing of data collection when formulating strategies. For example, the department can prioritize strategies based on the latest data and postpone those based on older data. The department can also prioritize strategies based on the timing of data collection. Furthermore, the department can quickly formulate strategies based on data where the timing of collection is important. This enables efficient strategy formulation by prioritizing strategies based on the timing of data collection. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not. For example, the strategy formulation department can input the timing of data collection into a generating AI and have the generating AI perform the determination of strategy priorities.

[0049] The strategy formulation department can adjust the order of strategies based on the relevance of data during strategy formulation. For example, the department can prioritize strategies based on highly relevant data and postpone those based on less relevant data. The department can also determine the order of strategies based on the relevance of data. Furthermore, the department can formulate detailed strategies based on highly relevant data. This allows for efficient strategy formulation by adjusting the order of strategies based on the relevance of data. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not. For example, the strategy formulation department can input the relevance of data into a generating AI and have the generating AI adjust the order of strategies.

[0050] The execution unit can analyze the user's past behavior history during execution to select the optimal execution method. For example, the execution unit can select the most effective execution method based on the user's past behavior history. The execution unit can also analyze the user's past behavior patterns and suggest highly relevant execution methods. Furthermore, the execution unit can select the optimal execution method by referring to the user's past success stories. In this way, the optimal execution method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's past behavior history into a generating AI and have the generating AI select the optimal execution method.

[0051] The execution unit can customize the execution method at runtime based on the user's current living situation. For example, if the user is busy, the execution unit can provide a simplified execution method. If the user has more time, the execution unit can also provide a more detailed execution method. Furthermore, the execution unit can customize the optimal execution method according to the user's current living situation. This allows for more appropriate execution by customizing the execution method based on the user's current living situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the execution method.

[0052] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, if the user is in a specific region, the execution unit will select an execution method relevant to that region. The execution unit can also select a highly relevant execution method based on the user's travel history. Furthermore, the execution unit can select the optimal execution method in real time based on the user's current location. This allows for the selection of the optimal execution method by considering the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal execution method.

[0053] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can analyze the content of the user's social media posts and propose relevant means of execution. The execution unit can also propose means of execution based on the user's topics of interest on social media. Furthermore, the execution unit can analyze the activities of the user's followers and friends on social media and propose relevant means of execution. In this way, by analyzing the user's social media activity, it is possible to propose highly relevant means of execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of means of execution.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The data collection unit can analyze past data collection history and select the optimal collection method. For example, it can identify the most effective collection time slot from past data collection history and collect data during that time slot. It can also adjust the collection frequency from specific data sources based on past data collection history. Furthermore, it can analyze past data collection history to identify areas for improvement in the collection method and optimize it. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0056] The data collection unit can filter data based on the user's current areas of interest and behavioral patterns. For example, it can prioritize collecting only data related to the user's current areas of interest. It can also analyze the user's behavioral patterns and filter and collect highly relevant data. Furthermore, it can collect new data related to areas of interest based on the user's past behavioral history. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest and behavioral patterns.

[0057] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. It can also prioritize analyses based on the importance of the data. Furthermore, multiple analytical methods can be applied to high-importance data to obtain detailed results. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0058] The strategy formulation department can apply different strategic algorithms depending on the data category during strategy formulation. For example, natural language processing algorithms can be applied to text data, and statistical analysis algorithms to numerical data. Image recognition algorithms can be applied to image data, and speech recognition algorithms to audio data. Furthermore, the department can select and apply the most appropriate strategic algorithm based on the data category. This allows for highly accurate strategy formulation by applying the optimal strategic algorithm according to the data category.

[0059] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location. For example, if the user is in a specific region, it will select an execution method relevant to that region. It can also select a highly relevant execution method based on the user's travel history. Furthermore, it can select the optimal execution method in real time based on the user's current location. In this way, the optimal execution method can be selected by considering the user's geographical location.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The collection unit collects data. The collection unit can collect data in various formats, such as text data, numerical data, and image data. The collection unit can collect data from websites, social media, and sensor data, for example. For example, the collection unit can collect data from websites using web scraping technology. The collection unit can also collect user posts using social media APIs. Furthermore, the collection unit can collect environmental data in real time using IoT sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis, machine learning algorithms, and natural language processing techniques. For example, the analysis unit can analyze collected text data using natural language processing techniques to extract consumer sentiment and opinions. The analysis unit can also analyze collected numerical data using statistical analysis methods to identify trends and patterns. Furthermore, the analysis unit can analyze collected image data using image recognition techniques to recognize and classify objects. Step 3: The Strategy Department formulates strategies based on the analysis results obtained by the Analysis Department. The Strategy Department can formulate, for example, marketing strategies, sales strategies, and promotional strategies. For example, the Strategy Department can plan advertising campaigns for target audiences based on consumer sentiment and opinions. The Strategy Department can also develop new product development and sales strategies based on trends and patterns. Furthermore, the Strategy Department can formulate product improvement and quality control strategies based on object recognition and classification results. Step 4: The execution team implements perception change through LY advertising based on the strategy formulated by the planning team. The execution team can deliver strategic messages to consumers through, for example, online advertising, television advertising, radio advertising, and print advertising. For example, the execution team can use online advertising platforms to deliver customized advertisements to target audiences. The execution team can also convey messages to a broad audience through television advertising. Furthermore, the execution team can effectively reach local consumers through radio advertising and print advertising.

[0062] (Example of form 2) The perception change support system according to an embodiment of the present invention is a system that quantitatively expresses perception and supports increased sales for clients by inducing perception change starting with LY advertisements. The perception change support system is a mechanism that quantitatively expresses perception and supports increased sales for clients by inducing perception change starting with LY advertisements. Specifically, it consists of the following steps. First, "measure" the perception of the target category or product / service. Next, formulate a strategy based on the measured perception and "change" or "create" perception. Finally, realize perception change starting with LY advertisements. First, "measure" the perception of the target category or product / service. At this time, LY data and SB Group assets are used to quantitatively express consumer perception. This makes it possible to understand how consumers perceive products and services. Next, formulate a strategy based on the measured perception. Specifically, this involves developing strategies to "change" or "create" perceptions. For example, planning advertising campaigns to positively alter consumer perceptions. It also involves considering measures to provide new value that consumers will newly recognize. This allows for effective control of consumer perceptions. Finally, perception change is achieved starting with LY advertising. Specifically, strategic messages are sent to consumers through LY advertising to promote perception change. For example, the content of advertisements is customized to match consumer perceptions, effectively reaching the target audience. This changes consumer perceptions and helps increase client sales. This mechanism allows companies to accurately understand consumer perceptions and develop effective strategies. Furthermore, by utilizing LY advertising, it becomes possible to achieve perception change and support increased sales for clients. For example, by positively changing consumers' perceptions of a particular product or service, purchasing intent can be increased. Also, by providing new value, consumer interest can be attracted, and sales can be improved.This allows the perception change support system to accurately understand consumer perceptions, develop effective strategies, and help clients increase their sales.

[0063] The perception change support system according to this embodiment comprises a collection unit, an analysis unit, a formulation unit, and an execution unit. The collection unit collects data. The collection unit can collect data in the form of, for example, text data, numerical data, and image data. The collection unit can collect data from websites, social media, and sensor data, for example. The collection unit can collect data from websites using web scraping technology, for example. The collection unit can also collect user posts using social media APIs. Furthermore, the collection unit can collect environmental data in real time using IoT sensors. The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis, machine learning algorithms, and natural language processing technology. For example, the analysis unit can analyze collected text data using natural language processing technology to extract consumer sentiment and opinions. The analysis unit can also analyze collected numerical data using statistical analysis methods to identify trends and patterns. Furthermore, the analysis unit can analyze collected image data using image recognition technology to recognize and classify objects. The Strategy Department formulates strategies based on the analysis results obtained by the Analysis Department. For example, the Strategy Department can formulate marketing strategies, sales strategies, and promotional strategies. For example, the Strategy Department plans advertising campaigns targeting specific segments based on consumer sentiment and opinions. Furthermore, the Strategy Department can develop new product development and sales strategies based on trends and patterns. In addition, the Strategy Department can formulate product improvement and quality control strategies based on object recognition and classification results. The Execution Department implements perception change through LY advertising based on the strategies formulated by the Strategy Department. For example, the Execution Department can deliver strategic messages to consumers through online advertising, television advertising, radio advertising, and print advertising. For example, the Execution Department uses online advertising platforms to deliver customized advertisements to target segments. The Execution Department can also convey messages to a broad audience through television advertising.Furthermore, the execution unit can effectively reach local consumers through radio and print advertisements. As a result, the perception change support system according to this embodiment can effectively achieve perception change and support clients in increasing sales by consistently handling everything from data collection and analysis to strategy formulation and execution.

[0064] The data collection unit collects data. The data collection unit can collect data in various formats, such as text data, numerical data, and image data. Specifically, the data collection unit can collect data from websites, social media, and sensor data. When collecting data from websites using web scraping technology, the data collection unit analyzes the HTML structure of specific web pages and extracts the necessary information. For example, it can collect consumer ratings and comments from product review sites. When collecting user posts using social media APIs, the data collection unit filters posts based on specific keywords and hashtags and obtains relevant data. For example, it can collect consumer opinions and sentiments regarding specific brands. Furthermore, when collecting environmental data in real time using IoT sensors, the data collection unit acquires data such as temperature, humidity, and light intensity from the sensors and transmits it to a central database. This allows the data collection unit to collect a wide range of data from diverse data sources and understand the situation in real time. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data can be stored on a cloud server and made accessible to the analysis and planning departments. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0065] The analysis department analyzes the data collected by the data collection department. The analysis department can analyze data using methods such as statistical analysis, machine learning algorithms, and natural language processing techniques. Specifically, it can analyze collected text data using natural language processing techniques to extract consumer sentiment and opinions. Natural language processing techniques include tokenization, morphological analysis, sentiment analysis, and topic modeling. For example, it can classify positive and negative opinions from consumer reviews to understand evaluations of products and services. It can also analyze collected numerical data using statistical analysis methods to identify trends and patterns. For example, it can analyze sales data over time to evaluate seasonal sales fluctuations and the effectiveness of specific campaigns. Furthermore, it can analyze collected image data using image recognition techniques to recognize and classify objects. Image recognition techniques include convolutional neural networks (CNNs) and object detection algorithms. For example, it can detect brand logos from product images to analyze which brands consumers are interested in. This allows the analysis department to analyze collected data from multiple perspectives and gain a deeper understanding of consumer behavior and opinions. Furthermore, the analytics department can utilize historical data and statistical information to conduct long-term trend and risk assessments. For example, it can use past sales data to forecast future sales and aid in developing marketing strategies. This allows the analytics department to not only grasp the situation in real time but also to formulate long-term strategies, improving the overall reliability and effectiveness of the system.

[0066] The Strategy Planning Department formulates strategies based on the analysis results obtained by the Analysis Department. For example, the Strategy Planning Department can formulate marketing strategies, sales strategies, and promotional strategies. Specifically, it plans advertising campaigns targeting specific segments based on consumer sentiment and opinions. For instance, for products with many positive opinions, it develops advertising that emphasizes the product's strengths, while for products with many negative opinions, it develops advertising that highlights areas for improvement. It can also develop new product development and sales strategies based on trends and patterns. For example, for products with increased sales during certain seasons, it develops seasonal promotions to maximize sales. Furthermore, it can formulate product improvement and quality control strategies based on object recognition and classification results. For example, if consumers highly value a particular design or function, it develops a new product with enhanced features. This allows the Strategy Planning Department to formulate concrete strategies based on analysis results and support clients in achieving their goals. Additionally, the Strategy Planning Department can evaluate the effectiveness of strategies and modify them as needed. For example, it can monitor the effectiveness of advertising campaigns and revise the target audience or messaging if the campaign is ineffective. Furthermore, the strategy planning department can develop strategies that reflect the client's needs and requests through communication with the client. This allows the strategy planning department to provide the client with the optimal strategy and maximize the client's business results.

[0067] The execution team implements perception change based on the strategy formulated by the planning team. Specifically, they can deliver strategic messages to consumers through online advertising, television advertising, radio advertising, and print advertising. When using online advertising platforms to deliver customized ads to target audiences, the execution team adjusts the ad creative and message to match the characteristics of the target audience. For example, they might use social media advertising and deliver visually-oriented content for younger demographics, while delivering ads containing specialized information for business professionals. When conveying a message to a broad audience through television advertising, the execution team selects the broadcast time and channel to match the characteristics of the audience. For example, ads for family-oriented products are broadcast during times when families are likely to be together. When effectively reaching local consumers through radio and print advertising, the execution team develops ads tailored to local characteristics and events. For example, they might conduct promotions that coincide with local festivals and events to attract the attention of local residents. This allows the execution team to effectively utilize each media and deliver the optimal message to the target audience. Furthermore, the execution team can monitor the effectiveness of advertising campaigns and modify the ad content and delivery methods as needed. For example, by analyzing the click-through rate and conversion rate of online ads, the execution team can review the ad creative and targeting if the results are poor. Furthermore, through feedback from clients, the execution team can identify areas for improvement in the advertising strategy and incorporate them into the next campaign. This allows the execution team to support clients in achieving their goals and effectively drive perception change.

[0068] The data collection unit can collect data by utilizing LY data and SB Group assets. For example, the data collection unit can use LY data to collect consumer behavior data and purchase history. The data collection unit can also utilize SB Group assets to collect consumer location data and search data. For example, the data collection unit can obtain consumer purchase history from the LY database and analyze consumer purchasing patterns. The data collection unit can also use SB Group's location information services to collect consumer movement history and identify consumer behavior patterns. Furthermore, the data collection unit can use SB Group's search engine to collect consumer search history and understand consumer areas of interest. This enables more detailed and accurate data collection by utilizing LY data and SB Group assets. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data obtained from the LY database into a generating AI and have the generating AI perform data analysis.

[0069] The analysis department can analyze collected data using AI to quantitatively represent consumer perceptions. For example, the analysis department can use deep learning technology to analyze collected data and quantify consumer emotions and opinions. The analysis department can also use natural language processing technology to analyze collected text data and score consumer perceptions. For example, the analysis department can use a deep learning model to classify consumer emotions into three categories: positive, negative, and neutral, and calculate an emotion score for each. The analysis department can also use natural language processing technology to analyze consumer opinions and score them based on specific keywords. Furthermore, the analysis department can use machine learning algorithms to analyze collected numerical data and identify consumer behavior patterns. For example, the analysis department can use clustering algorithms to analyze consumer purchase history and identify consumer groups with similar purchase patterns. This allows for an accurate and quantitative representation of consumer perceptions using AI. Some or all of the above processes in the analysis department may be performed using AI, or not. For example, the analysis department can input the collected data into a generative AI and have the AI ​​perform the quantification of consumer perceptions.

[0070] The Strategy Department can develop strategies to "change" or "create" perceptions based on measured perceptions. For example, the Strategy Department might plan advertising campaigns to positively change consumer perceptions. It can also devise measures to provide new value that consumers will newly recognize. For example, it could create advertisements containing positive messages based on consumer sentiment scores to positively change consumer perceptions. It could also create advertisements that highlight the features and benefits of new products to provide consumers with new value. Furthermore, the Strategy Department could plan promotional activities for target groups based on consumer behavior patterns. For example, it could implement customized promotions for consumer groups with specific purchasing patterns to change consumer perceptions. This allows for the development of effective strategies based on measured perceptions. Some or all of the above processes in the Strategy Department may be performed using AI, for example, or not. For example, the Strategy Department could input measured perception data into a Generative AI and have the Generative AI develop strategies.

[0071] The execution unit can deliver strategic messages to consumers through LY ads and promote perception change. The execution unit can deliver strategic messages to consumers through, for example, online advertising, television advertising, radio advertising, and print advertising. The execution unit can, for example, use online advertising platforms to deliver customized ads to target audiences. The execution unit can also convey messages to a broad audience through television advertising. Furthermore, the execution unit can effectively reach local consumers through radio advertising and print advertising. This allows for effective promotion of perception change through LY ads. Some or all of the above processes in the execution unit may be performed using, for example, AI, or not using AI. For example, the execution unit can input the content of the LY ad into a generating AI and have the generating AI customize the ad.

[0072] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit may refrain from collecting data and attempt to collect it again when the user is relaxed. Furthermore, if the user is excited, the data collection unit can collect data in real time to obtain information at the peak of their emotions. Additionally, if the user is relaxed, the data collection unit can perform detailed data collection to obtain highly accurate information. This allows for more appropriate data collection by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0073] The data collection unit can analyze past data collection history and select the optimal collection method. For example, the data collection unit can identify the most effective collection time slot from past data collection history and collect data during that time slot. The data collection unit can also adjust the collection frequency from specific data sources based on past data collection history. Furthermore, the data collection unit can analyze past data collection history to identify areas for improvement in the collection method and optimize it. In this way, the optimal collection method can be selected by analyzing past data collection history. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data collection history into a generating AI and have the generating AI select the optimal collection method.

[0074] The data collection unit can filter data based on the user's current areas of interest and behavioral patterns during data collection. For example, the data collection unit can prioritize collecting only data related to the user's current areas of interest. The data collection unit can also analyze the user's behavioral patterns and filter and collect highly relevant data. Furthermore, the data collection unit can collect new data related to areas of interest based on the user's past behavioral history. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest and behavioral patterns. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's areas of interest and behavioral pattern data into a generating AI and have the generating AI perform data filtering.

[0075] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting data related to the peak of the emotion. The data collection unit can also prioritize collecting detailed data if the user is relaxed. Furthermore, if the user is stressed, the data collection unit can prioritize collecting data related to stress reduction. This allows for the collection of more important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the data priority.

[0076] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of data related to that region. The data collection unit can also collect highly relevant data based on the user's travel history. Furthermore, the data collection unit can collect relevant data in real time based on the user's current location. This allows for the priority collection of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant data.

[0077] The data collection unit can analyze the user's social media activity and collect relevant data during data collection. For example, the data collection unit can analyze the content of the user's social media posts and collect relevant data. The data collection unit can also collect data based on the user's social media interests. Furthermore, the data collection unit can analyze the activity of the user's followers and friends on social media and collect relevant data. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0078] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the analysis unit can provide analysis results using visually stimulating graphs or charts. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0079] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. The analysis unit can also determine the priority of the analysis according to the importance of the data. Furthermore, the analysis unit can apply multiple analysis methods to high-importance data to obtain detailed results. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0080] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, it can apply a natural language processing algorithm to text data and a statistical analysis algorithm to numerical data. It can also apply an image recognition algorithm to image data and a speech recognition algorithm to audio data. Furthermore, the analysis unit can select and apply the most suitable analysis algorithm depending on the data category. This enables highly accurate analysis by applying the most suitable analysis algorithm according to the data category. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data category into a generating AI and have the generating AI select and apply the most suitable analysis algorithm.

[0081] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can also provide a longer analysis with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide an analysis with visually stimulating effects. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0082] The analysis unit can determine the priority of analysis based on the data collection timing during the analysis process. For example, the analysis unit can prioritize the analysis of the most recent data and postpone the analysis of older data. The analysis unit can also determine the priority of analysis based on the data collection timing. Furthermore, the analysis unit can perform rapid analysis on data where the collection timing is important. This enables efficient analysis by determining the priority of analysis based on the data collection timing. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the data collection timing into a generating AI and have the generating AI determine the priority of analysis.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant data and postpone the analysis of less relevant data. The analysis unit can also determine the order of analysis based on the relevance of the data. Furthermore, the analysis unit can perform detailed analysis on highly relevant data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the relevance of the data into a generating AI and have the generating AI adjust the order of analysis.

[0084] The strategy planning unit can estimate the user's emotions and adjust the way the strategy is presented based on those emotions. For example, if the user is relaxed, the strategy planning unit can provide a detailed strategy. If the user is in a hurry, it can provide a concise strategy that gets straight to the point. Furthermore, if the user is excited, the strategy planning unit can present the strategy using visually stimulating graphs and charts. This allows for the provision of more appropriate strategies by adjusting the way the strategy is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy planning unit may be performed using AI or not. For example, the strategy planning unit can input user emotion data into a generative AI and have the generative AI adjust the way the strategy is presented.

[0085] The strategy formulation department can adjust the level of detail of a strategy based on the importance of the data during the strategy formulation process. For example, the department can formulate a detailed strategy based on high-importance data and a simplified strategy based on low-importance data. The department can also prioritize strategies according to the importance of the data. Furthermore, the department can formulate multiple strategies based on high-importance data. This allows for efficient strategy formulation by adjusting the level of detail of strategies based on the importance of the data. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not. For example, the strategy formulation department can input the importance of the data into a generating AI and have the generating AI adjust the level of detail of the strategy.

[0086] The strategy formulation department can apply different strategic algorithms depending on the data category when formulating a strategy. For example, it can apply a natural language processing algorithm based on text data and a statistical analysis algorithm based on numerical data. It can also apply an image recognition algorithm based on image data and a speech recognition algorithm based on audio data. Furthermore, the strategy formulation department can select and apply the most appropriate strategic algorithm depending on the data category. This enables highly accurate strategy formulation by applying the most appropriate strategic algorithm according to the data category. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or without AI. For example, the strategy formulation department can input the data category into a generating AI and have the generating AI select and apply the most appropriate strategic algorithm.

[0087] The strategy planning unit can estimate the user's emotions and adjust the length of the strategy based on the estimated emotions. For example, if the user is in a hurry, the strategy planning unit can provide a short, concise strategy. If the user is relaxed, the strategy planning unit can provide a longer strategy with detailed explanations. Furthermore, if the user is excited, the strategy planning unit can provide a strategy with visually stimulating effects. By adjusting the length of the strategy based on the user's emotions, a more appropriate strategy can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the strategy planning unit may be performed using AI or not. For example, the strategy planning unit can input user emotion data into a generative AI and have the generative AI adjust the length of the strategy.

[0088] The strategy formulation department can prioritize strategies based on the timing of data collection when formulating strategies. For example, the department can prioritize strategies based on the latest data and postpone those based on older data. The department can also prioritize strategies based on the timing of data collection. Furthermore, the department can quickly formulate strategies based on data where the timing of collection is important. This enables efficient strategy formulation by prioritizing strategies based on the timing of data collection. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not. For example, the strategy formulation department can input the timing of data collection into a generating AI and have the generating AI perform the determination of strategy priorities.

[0089] The strategy formulation department can adjust the order of strategies based on the relevance of data during strategy formulation. For example, the department can prioritize strategies based on highly relevant data and postpone those based on less relevant data. The department can also determine the order of strategies based on the relevance of data. Furthermore, the department can formulate detailed strategies based on highly relevant data. This allows for efficient strategy formulation by adjusting the order of strategies based on the relevance of data. Some or all of the above processes in the strategy formulation department may be performed using AI, for example, or not. For example, the strategy formulation department can input the relevance of data into a generating AI and have the generating AI adjust the order of strategies.

[0090] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated emotions. For example, if the user is relaxed, the execution unit can provide an execution method that includes detailed explanations. If the user is in a hurry, the execution unit can also provide a concise execution method that gets straight to the point. Furthermore, if the user is excited, the execution unit can provide an execution method that includes visually stimulating effects. This allows for more appropriate execution by adjusting the execution method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, for example, or not using AI. For example, the execution unit can input user emotion data into a generative AI and have the generative AI adjust the execution method.

[0091] The execution unit can analyze the user's past behavior history during execution to select the optimal execution method. For example, the execution unit can select the most effective execution method based on the user's past behavior history. The execution unit can also analyze the user's past behavior patterns and suggest highly relevant execution methods. Furthermore, the execution unit can select the optimal execution method by referring to the user's past success stories. In this way, the optimal execution method can be selected by analyzing the user's past behavior history. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's past behavior history into a generating AI and have the generating AI select the optimal execution method.

[0092] The execution unit can customize the execution method at runtime based on the user's current living situation. For example, if the user is busy, the execution unit can provide a simplified execution method. If the user has more time, the execution unit can also provide a more detailed execution method. Furthermore, the execution unit can customize the optimal execution method according to the user's current living situation. This allows for more appropriate execution by customizing the execution method based on the user's current living situation. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's current living situation data into a generating AI and have the generating AI perform the customization of the execution method.

[0093] The execution unit can estimate the user's emotions and determine the priority of actions based on the estimated emotions. For example, if the user is excited, the execution unit may prioritize actions related to the peak of the emotion. It may also prioritize detailed actions if the user is relaxed. Furthermore, if the user is stressed, the execution unit may prioritize actions related to stress reduction. This allows for more appropriate actions by prioritizing actions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the execution unit may be performed using AI, or not. For example, the execution unit can input user emotion data into a generative AI and have the generative AI determine the priority of actions.

[0094] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location information. For example, if the user is in a specific region, the execution unit will select an execution method relevant to that region. The execution unit can also select a highly relevant execution method based on the user's travel history. Furthermore, the execution unit can select the optimal execution method in real time based on the user's current location. This allows for the selection of the optimal execution method by considering the user's geographical location information. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's geographical location data into a generating AI and have the generating AI select the optimal execution method.

[0095] The execution unit can analyze the user's social media activity at runtime and propose means of execution. For example, the execution unit can analyze the content of the user's social media posts and propose relevant means of execution. The execution unit can also propose means of execution based on the user's topics of interest on social media. Furthermore, the execution unit can analyze the activities of the user's followers and friends on social media and propose relevant means of execution. In this way, by analyzing the user's social media activity, it is possible to propose highly relevant means of execution. Some or all of the above processing in the execution unit may be performed using AI, for example, or without AI. For example, the execution unit can input the user's social media activity data into a generating AI and have the generating AI execute the proposal of means of execution.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, data collection can be withheld and attempted again when the user is relaxed. If the user is excited, data can be collected in real time to obtain information at the peak of their emotions. Furthermore, if the user is relaxed, detailed data collection can be performed to obtain highly accurate information. In this way, by adjusting the timing of data collection according to the user's emotions, more appropriate data collection becomes possible.

[0098] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results. If the user is in a hurry, it can provide concise analysis results that get straight to the point. Furthermore, if the user is excited, it can present the analysis results using visually stimulating graphs and charts. In this way, by adjusting the presentation of the analysis based on the user's emotions, it can provide more appropriate analysis results.

[0099] The strategy development team can estimate the user's emotions and adjust the way the strategy is presented based on those emotions. For example, if the user is relaxed, a detailed strategy can be provided. If the user is in a hurry, a concise strategy that gets straight to the point can be provided. Furthermore, if the user is excited, a strategy can be presented using visually stimulating graphs and charts. By adjusting the way the strategy is presented based on the user's emotions, a more appropriate strategy can be provided.

[0100] The execution unit can estimate the user's emotions and adjust the execution method based on those emotions. For example, if the user is relaxed, it can provide an execution method that includes detailed explanations. If the user is in a hurry, it can provide a concise execution method that gets straight to the point. Furthermore, if the user is excited, it can provide an execution method that includes visually stimulating effects. By adjusting the execution method based on the user's emotions, more appropriate execution becomes possible.

[0101] The execution unit can estimate the user's emotions and determine the priority of actions based on those emotions. For example, if the user is excited, it can prioritize actions related to the peak of their emotions. If the user is relaxed, it can also prioritize detailed actions. Furthermore, if the user is stressed, it can prioritize actions related to stress reduction. By prioritizing actions based on the user's emotions, more appropriate actions can be performed.

[0102] The data collection unit can analyze past data collection history and select the optimal collection method. For example, it can identify the most effective collection time slot from past data collection history and collect data during that time slot. It can also adjust the collection frequency from specific data sources based on past data collection history. Furthermore, it can analyze past data collection history to identify areas for improvement in the collection method and optimize it. In this way, the optimal collection method can be selected by analyzing past data collection history.

[0103] The data collection unit can filter data based on the user's current areas of interest and behavioral patterns. For example, it can prioritize collecting only data related to the user's current areas of interest. It can also analyze the user's behavioral patterns and filter and collect highly relevant data. Furthermore, it can collect new data related to areas of interest based on the user's past behavioral history. This allows for the collection of highly relevant data by filtering data based on the user's areas of interest and behavioral patterns.

[0104] The analysis department can adjust the level of detail of the analysis based on the importance of the data. For example, it can perform a detailed analysis on high-importance data and a simplified analysis on low-importance data. It can also prioritize analyses based on the importance of the data. Furthermore, multiple analytical methods can be applied to high-importance data to obtain detailed results. This allows for efficient analysis by adjusting the level of detail based on the importance of the data.

[0105] The strategy formulation department can apply different strategic algorithms depending on the data category during strategy formulation. For example, natural language processing algorithms can be applied to text data, and statistical analysis algorithms to numerical data. Image recognition algorithms can be applied to image data, and speech recognition algorithms to audio data. Furthermore, the department can select and apply the most appropriate strategic algorithm based on the data category. This allows for highly accurate strategy formulation by applying the optimal strategic algorithm according to the data category.

[0106] The execution unit can select the optimal execution method at runtime, taking into account the user's geographical location. For example, if the user is in a specific region, it will select an execution method relevant to that region. It can also select a highly relevant execution method based on the user's travel history. Furthermore, it can select the optimal execution method in real time based on the user's current location. In this way, the optimal execution method can be selected by considering the user's geographical location.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The collection unit collects data. The collection unit can collect data in various formats, such as text data, numerical data, and image data. The collection unit can collect data from websites, social media, and sensor data, for example. For example, the collection unit can collect data from websites using web scraping technology. The collection unit can also collect user posts using social media APIs. Furthermore, the collection unit can collect environmental data in real time using IoT sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit can analyze the data using, for example, statistical analysis, machine learning algorithms, and natural language processing techniques. For example, the analysis unit can analyze collected text data using natural language processing techniques to extract consumer sentiment and opinions. The analysis unit can also analyze collected numerical data using statistical analysis methods to identify trends and patterns. Furthermore, the analysis unit can analyze collected image data using image recognition techniques to recognize and classify objects. Step 3: The Strategy Department formulates strategies based on the analysis results obtained by the Analysis Department. The Strategy Department can formulate, for example, marketing strategies, sales strategies, and promotional strategies. For example, the Strategy Department can plan advertising campaigns for target audiences based on consumer sentiment and opinions. The Strategy Department can also develop new product development and sales strategies based on trends and patterns. Furthermore, the Strategy Department can formulate product improvement and quality control strategies based on object recognition and classification results. Step 4: The execution team implements perception change through LY advertising based on the strategy formulated by the planning team. The execution team can deliver strategic messages to consumers through, for example, online advertising, television advertising, radio advertising, and print advertising. For example, the execution team can use online advertising platforms to deliver customized advertisements to target audiences. The execution team can also convey messages to a broad audience through television advertising. Furthermore, the execution team can effectively reach local consumers through radio advertising and print advertising.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and execution unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 38B of the smart device 14 and processes the data with the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and formulates a strategy based on the analysis results. The execution unit is implemented by the control unit 46A of the smart device 14 and executes perception changes through LY advertisements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and 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 data using the camera 42 and microphone 238 of the smart glasses 214 and processes the data by the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and formulates a strategy based on the analysis results. The execution unit is implemented by the control unit 46A of the smart glasses 214 and executes perception changes through LY advertisements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the data collection unit, analysis unit, planning unit, and execution unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit collects data using the camera 42 and microphone 238 of the headset terminal 314 and processes the data with the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and formulates a strategy based on the analysis results. The execution unit is implemented by the control unit 46A of the headset terminal 314 and executes perception changes through LY advertisements. 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.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[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 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.

[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 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.

[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 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.

[0161] Each of the multiple elements described above, including the collection unit, analysis unit, planning unit, and execution unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data using the camera 42 and microphone 238 of the robot 414 and processes the data with the control unit 46A. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and formulates a strategy based on the analysis results. The execution unit is implemented by, for example, the control unit 46A of the robot 414 and executes perception changes through LY advertisements. The correspondence between each unit and the devices and control units is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) A data collection unit that collects data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis department, the strategy formulation department formulates a strategy, The system comprises: an execution unit that executes perception changes through LY advertisements based on the strategy formulated by the aforementioned planning unit; A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data by utilizing LY data and SB Group assets. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The collected data is analyzed using AI to quantitatively represent consumer perceptions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning department, Based on measured perceptions, we develop strategies to "change" or "create" perceptions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The execution unit is, Through LY advertising, we deliver strategic messages to consumers and drive perception change. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and behavioral patterns. The system described in Appendix 1, characterized by the features described herein. (Note 9) 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 10) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit is It estimates the user's emotions and adjusts the way the analysis is presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit is During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is During analysis, different analytical algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is It estimates the user's emotions and adjusts the length of the analysis 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 During analysis, prioritize the analysis based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned planning department, It estimates user sentiment and adjusts the way strategies are presented based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned planning department, When formulating a strategy, adjust the level of detail of the strategy based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned planning department, When formulating a strategy, apply different strategic algorithms depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned planning department, The system estimates the user's emotions and adjusts the length of the strategy based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned planning department, When formulating a strategy, prioritize strategies based on when data is collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned planning department, When formulating a strategy, adjust the order of strategies based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The execution unit is, It estimates the user's emotions and adjusts the execution method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The execution unit is, During execution, the system analyzes the user's past behavior history to select the optimal execution method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The execution unit is, At runtime, the execution method is customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The execution unit is, It estimates the user's emotions and determines the priority of actions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The execution unit is, During execution, the system selects the optimal execution method, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The execution unit is, During execution, the system analyzes the user's social media activity and suggests implementation methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0181] 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 data, An analysis unit analyzes the data collected by the aforementioned collection unit, Based on the analysis results obtained by the aforementioned analysis department, the strategy formulation department formulates a strategy, The system comprises: an execution unit that executes perception changes through LY advertisements based on the strategy formulated by the aforementioned planning unit; A system characterized by the following features.

2. The aforementioned collection unit is We collect data by utilizing LY data and SB Group assets. The system according to feature 1.

3. The aforementioned analysis unit is The collected data is analyzed using AI to quantitatively express consumer perceptions. The system according to feature 1.

4. The aforementioned planning department, Based on measured perceptions, we formulate perception strategies. The system according to feature 1.

5. The execution unit is, Through LY advertising, we deliver strategic messages to consumers and drive perception change. The system according to feature 1.

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

7. The aforementioned collection unit is Analyze past data collection history and select the optimal collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting data, filtering is performed based on the user's current areas of interest and behavioral patterns. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

10. The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data, taking into account the user's geographical location. The system according to feature 1.

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