system

Abstract

The system according to the embodiment aims to efficiently conduct an attitude survey and precisely verify the effectiveness of advertising. [Solution] A system according to an embodiment includes a design unit, a collection unit, an analysis unit, and a verification unit. The design unit performs research and design. The collection unit collects data from a user base. The analysis unit analyzes the data collected by the collection unit. The verification unit verifies advertising effectiveness based on the analysis results obtained by the analysis unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it is difficult to verify the cost of conducting opinion surveys and the effectiveness of advertising, and there is room for improvement in formulating efficient marketing strategies.

[0005] The system according to the embodiment aims to efficiently conduct an attitude survey and precisely verify the effectiveness of advertising. [Means for solving the problem]

[0006] The system according to the embodiment includes a design unit, a collection unit, an analysis unit, and a verification unit. The design unit performs research and design. The collection unit collects data from a user base. The analysis unit analyzes the data collected by the collection unit. The verification unit verifies the effectiveness of advertising based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently conduct opinion surveys and precisely verify advertising effectiveness. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A marketing support system according to an embodiment of the present invention combines a generation AI with a large user base and behavioral data to eliminate the cost and difficulty of verifying the effectiveness of opinion surveys that marketers face. In the marketing support system, a generation AI designs the survey to improve efficiency. Next, data is collected from a large user base, and the survey is conducted quickly and at low cost. The collected behavioral data is then analyzed to precisely verify the effectiveness of advertising. For example, in the marketing support system, a generation AI analyzes past data and trends to propose an optimal survey design. For example, the system designs a questionnaire for a specific target demographic and selects effective questions. Next, the marketing support system collects user behavior data from platforms such as e-commerce sites and social media. This enables large-scale surveys to be conducted quickly and at low cost. The collected data is analyzed by a generation AI. The generation AI analyzes the collected data to understand user behavioral patterns and interests. For example, the system analyzes the level of interest in a specific product and the click-through rate of advertisements. Finally, the marketing support system precisely verifies the effectiveness of advertising based on the analysis results. Based on the collected data and analysis results, the generation AI evaluates the effectiveness of advertising and proposes an optimal advertising strategy. This can improve the effectiveness of advertising. As a result, the marketing support system can reduce research costs and time and enable precise verification of advertising effectiveness. For example, it can evaluate the effectiveness of advertising for a specific target demographic in real time and modify advertising strategies as necessary. This improves the efficiency of marketing activities.

[0029] The marketing support system according to the embodiment includes a design unit, a collection unit, an analysis unit, and a verification unit. The design unit designs surveys. For example, the design unit analyzes past data and trends to propose optimal survey designs. For example, the design unit can design questionnaires for specific target demographics and select effective questions. The design unit can also use a generative AI to improve the efficiency of survey design. The collection unit collects data from a user base. The collection unit collects user behavior data from platforms such as e-commerce sites and social networking sites. For example, the collection unit can collect user click data and browsing history. The collection unit can also use AI to improve the efficiency of data collection. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand user behavior patterns and interests. For example, the analysis unit can analyze the level of interest in a specific product and the click rate of an advertisement. The analysis unit can also use a generative AI to improve the efficiency of data analysis. The verification unit verifies the advertising effectiveness based on the analysis results obtained by the analysis unit. For example, the verification unit evaluates the effectiveness of the advertising based on the collected data and the analysis results, and proposes an optimal advertising strategy. For example, the verification unit can evaluate the effectiveness of the advertising for a specific target demographic in real time and modify the advertising strategy as necessary. The verification unit can also use AI to improve the efficiency of the verification of advertising effectiveness. This allows the marketing support system according to the embodiment to efficiently perform survey design, data collection, data analysis, and verification of advertising effectiveness. For example, the marketing support system can use generative AI to improve the efficiency of survey design. The marketing support system can also use AI to improve the efficiency of data collection, data analysis, and verification of advertising effectiveness.

[0030] The collection unit can collect user behavior data from an e-commerce site or a social networking platform. The collection unit collects user behavior data from, for example, an e-commerce site. For example, the collection unit can collect user click data and browsing history. The collection unit can also collect user behavior data from a social networking platform. For example, the collection unit can collect user post content and the number of likes. This allows the collection unit to collect user behavior data from a variety of platforms. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user behavior data acquired from the e-commerce site into a generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data to understand the user's behavioral patterns and interests. The analysis unit can, for example, analyze the collected data to understand the user's behavioral patterns. For example, the analysis unit can analyze the user's click data and browsing history to identify the user's behavioral patterns. The analysis unit can also analyze the collected data to understand the user's interests. For example, the analysis unit can analyze the content of the user's posts and the number of likes to identify the user's interests. This allows the analysis unit to understand the user's behavioral patterns and interests, enabling more precise analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI understand the user's behavioral patterns and interests.

[0032] The verification unit can evaluate the effectiveness of an advertisement based on the collected data and analysis results and propose an appropriate advertising strategy. The verification unit, for example, evaluates the effectiveness of an advertisement based on the collected data and analysis results. For example, the verification unit can evaluate the click-through rate and conversion rate of an advertisement. The verification unit can also propose an optimal advertising strategy based on the collected data and analysis results. For example, the verification unit can evaluate the effectiveness of an advertisement for a specific target demographic in real time and modify the advertising strategy as necessary. This allows the verification unit to evaluate the effectiveness of an advertisement and propose an optimal advertising strategy, thereby improving the effectiveness of the advertisement. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the collected data and analysis results into the generation AI and cause the generation AI to evaluate the effectiveness of the advertisement and propose an advertising strategy.

[0033] The design department can analyze past data and trends and propose an appropriate survey design. The design department, for example, analyzes past data and proposes an appropriate survey design. For example, the design department can analyze past sales data and advertising data to create an optimal survey design. The design department can also analyze trends and propose an appropriate survey design. For example, the design department can use time series analysis and trend analysis to create an optimal survey design. This allows the design department to create an optimal survey design based on past data and trends, thereby improving the accuracy of the survey. Some or all of the above-mentioned processing in the design department may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the design department can input past data and trends into a generation AI and have the generation AI execute a survey design proposal.

[0034] The verification unit can evaluate the effectiveness of an advertisement for a specific target demographic in real time and modify the advertising strategy as necessary. The verification unit, for example, evaluates the effectiveness of an advertisement for a specific target demographic in real time. For example, the verification unit can evaluate the click-through rate and conversion rate of the advertisement in real time. The verification unit can also modify the advertising strategy as necessary. For example, the verification unit can quickly modify the advertising strategy based on the evaluation results obtained in real time. This allows the verification unit to evaluate the effectiveness of the advertisement in real time and quickly modify the advertising strategy, thereby maximizing the effectiveness of the advertisement. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can evaluate the effectiveness of the advertisement for a specific target demographic in real time and cause the generation AI to modify the advertising strategy.

[0035] The design department can analyze past survey results and select the optimal questions and survey method. The design department, for example, analyzes past survey results and selects the optimal questions. For example, the design department can select questions that have received a high response rate from past survey results. The design department can also select a survey method that is effective for a specific target demographic based on past survey results. For example, the design department can select a survey method such as an online survey or a telephone survey based on past survey results. This allows the design department to select the optimal questions and survey method based on past survey results, thereby improving the accuracy of the survey. Some or all of the above-mentioned processing in the design department may be performed using, or without, a generation AI. For example, the design department can input past survey results into a generation AI and have the generation AI select the questions and survey method.

[0036] The design department can customize the survey design taking into account the attribute information of the survey subjects. The design department customizes the survey design taking into account, for example, the attribute information of the survey subjects. For example, the design department can adjust the difficulty level of questions depending on the age group of the survey subjects. The design department can also add highly relevant questions depending on the occupation of the survey subjects. The design department can also include region-specific questions depending on the region of the survey subjects. This allows the design department to customize the survey design based on the attribute information of the survey subjects, enabling a more appropriate survey. Some or all of the above-mentioned processing in the design department may be performed using, or without, a generation AI. For example, the design department can input attribute information of the survey subjects into the generation AI and have the generation AI customize the survey design.

[0037] The design department can adjust the design content when designing a survey, taking into account external factors such as seasons and events. For example, the design department can adjust the design content when designing a survey, taking into account external factors such as seasons and events. For example, the design department can add seasonally specific questions depending on the season. The design department can also include questions related to specific event periods. The design department can also predict response trends and adjust the design content depending on the season or event. This allows the design department to design a survey taking into account external factors such as seasons and events, enabling a more appropriate survey. Some or all of the above-mentioned processing in the design department can be performed using, or without, a generation AI. For example, the design department can input information about seasons and events into the generation AI and have the generation AI adjust the survey design.

[0038] The design unit can customize the design content by taking into account the user's geographic location information when designing a survey. For example, the design unit can customize the design content by taking into account the user's geographic location information when designing a survey. For example, the design unit can add region-specific questions based on the user's geographic location information. The design unit can also design questions related to regional trends by taking into account the user's geographic location information. The design unit can also design questions tailored to regional culture and customs based on the user's geographic location information. This allows the design unit to customize the survey design based on the user's geographic location information, enabling a more appropriate survey. Some or all of the above-described processing in the design unit may be performed using, or without, a generation AI. For example, the design unit can input the user's geographic location information into the generation AI and have the generation AI customize the survey design.

[0039] The design unit can analyze a user's social media activity and add relevant questions when designing a survey. For example, the design unit can analyze a user's social media activity and add relevant questions when designing a survey. For example, the design unit can analyze a user's social media posts and design relevant questions. The design unit can also customize questions based on the user's social media interests. The design unit can also design relevant questions based on the activities of the user's friends on social media. This allows the design unit to add questions based on the user's social media activity, enabling a more appropriate survey. Some or all of the above-described processing in the design unit can be performed using, or without, a generation AI. For example, the design unit can input the user's social media activity data into the generation AI and have the generation AI add questions.

[0040] The design unit can improve the design content by reflecting the user's past feedback when designing a survey. For example, the design unit can improve the design content by reflecting the user's past feedback when designing a survey. For example, the design unit can improve the content of questions based on the user's past feedback. The design unit can also optimize the survey method by referring to the user's past feedback. The design unit can also design a survey that reduces the burden of responding by reflecting the user's past feedback. This allows the design unit to improve the design content by reflecting the user's past feedback, enabling a more appropriate survey. Some or all of the above-mentioned processing in the design unit may be performed using, or without, a generation AI. For example, the design unit can input the user's past feedback data into the generation AI and have the generation AI improve the design content.

[0041] The collection unit can analyze the user's past behavioral data and select the optimal collection method. The collection unit, for example, analyzes the user's past behavioral data and selects the optimal collection method. For example, the collection unit can select the optimal data collection method based on the user's past behavioral data. The collection unit can also analyze the user's past behavioral data and select an effective timing for data collection. The collection unit can also customize the collection method by referring to the user's past behavioral data. This enables the collection unit to select the optimal collection method based on the user's past behavioral data, thereby enabling more effective data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past behavioral data into the generation AI and have the generation AI select the collection method.

[0042] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can prioritize collecting highly relevant data based on the user's current living situation. The collection unit can also filter the data to be collected based on the user's areas of interest. The collection unit can also determine the priority of the data to be collected taking into account the user's living situation and areas of interest. This allows the collection unit to collect more relevant data by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform data filtering.

[0043] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, when the user uses voice input, the collection unit can prioritize collecting voice data. Furthermore, when the user uses text input, the collection unit can prioritize collecting text data. Furthermore, when the user uses image input, the collection unit can prioritize collecting image data. This allows the collection unit to select the optimal collection means depending on the user's input method, enabling more effective data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's input data to the generation AI and cause the generation AI to select the optimal collection means.

[0044] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit can prioritize collecting region-specific data based on the user's geographical location information. The collection unit can also collect data related to regional trends by taking into account the user's geographical location information. The collection unit can also collect data related to regional culture and customs based on the user's geographical location information. This enables the collection unit to prioritize collecting highly relevant data based on the user's geographical location information, thereby enabling more effective data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0045] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of the user's social media posts and collect related data. The collection unit can also collect data based on the user's social media interests. The collection unit can also collect related data with reference to the activities of the user's friends on social media. This allows the collection unit to collect related data based on the user's social media activities, enabling more appropriate data collection. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0046] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can improve the data collection method based on the user's past feedback. The collection unit can also adjust the type of data to be collected by referring to the user's past feedback. The collection unit can also determine the priority of the data to be collected by reflecting the user's past feedback. In this way, the collection unit can customize the collection method by reflecting the user's past feedback, thereby enabling more appropriate data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0047] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the data, thereby enabling more effective data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0048] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. In this way, the analysis unit can apply different analysis algorithms depending on the category of data, enabling more appropriate data analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input the category of data to the generation AI and have the generation AI select the analysis algorithm to be applied.

[0049] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. The analysis unit can also determine the priority of the analysis by reflecting the user's past analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0050] The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit, for example, determines the priority of analysis based on the time of data submission during analysis. For example, the analysis unit can prioritize analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, the analysis unit can determine the priority of analysis based on the time of data submission, thereby enabling more effective data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission into the generation AI and have the generation AI determine the priority of analysis.

[0051] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data, thereby enabling more effective data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0052] The analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high level of expertise. The analysis unit can also provide analysis results that are concise and easy to understand to a user with low level of expertise. The analysis unit can also adjust the level of detail in the analysis results according to the user's level of expertise. This allows the analysis unit to provide more appropriate information by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the analysis results.

[0053] The verification unit can optimize the verification algorithm by referring to past advertising effectiveness data during verification. The verification unit can optimize the verification algorithm by referring to past advertising effectiveness data during verification, for example. For example, the verification unit can optimize the verification algorithm based on past advertising effectiveness data. The verification unit can also adjust the level of verification detail by referring to past advertising effectiveness data. The verification unit can also determine the priority of verification by reflecting past advertising effectiveness data. In this way, the verification unit can optimize the verification algorithm based on past advertising effectiveness data, thereby enabling more accurate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit may be performed using, or without, the generation AI, for example. For example, the verification unit can input past advertising effectiveness data into the generation AI and cause the generation AI to optimize the verification algorithm.

[0054] The verification unit can improve the verification method by reflecting user feedback during verification. The verification unit can improve the verification method by reflecting user feedback during verification, for example. For example, the verification unit can improve the verification method based on user feedback. The verification unit can also adjust the level of verification detail by referring to user feedback. The verification unit can also determine the priority of verification by reflecting user feedback. In this way, the verification unit can improve the verification method by reflecting user feedback, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit can be performed using, or without, the generation AI, for example. For example, the verification unit can input user feedback data into the generation AI and cause the generation AI to improve the verification method.

[0055] The verification unit can adjust the verification content during verification, taking into account the timing and frequency of advertisement display. For example, the verification unit can adjust the verification content during verification, taking into account the timing and frequency of advertisement display. For example, the verification unit can adjust the verification content based on the timing of advertisement display. The verification unit can also adjust the verification content based on the frequency of advertisement display. The verification unit can also determine the priority of verification, taking into account the timing and frequency of advertisement display. This allows the verification unit to adjust the verification content taking into account the timing and frequency of advertisement display, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-described processing in the verification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the verification unit can input data on the timing and frequency of advertisement display into the generation AI, and cause the generation AI to adjust the verification content.

[0056] The verification unit can perform verification taking into account the geographic distribution of the advertisement during verification. For example, the verification unit can perform verification taking into account the geographic distribution of the advertisement during verification. For example, the verification unit can adjust the verification content based on the geographic distribution of the advertisement. The verification unit can also provide a region-specific verification method taking into account the geographic distribution of the advertisement. The verification unit can also determine the priority of verification based on the geographic distribution of the advertisement. In this way, the verification unit can perform verification taking into account the geographic distribution of the advertisement, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-described processing in the verification unit may be performed using, or without, the generation AI. For example, the verification unit can input geographic distribution data of the advertisement into the generation AI and cause the generation AI to adjust the verification content.

[0057] The verification unit can improve the accuracy of the verification by referring to literature related to the advertisement during verification. The verification unit can improve the accuracy of the verification by referring to literature related to the advertisement during verification, for example. For example, the verification unit can optimize the verification algorithm based on literature related to the advertisement. The verification unit can also adjust the level of verification detail by referring to literature related to the advertisement. The verification unit can also determine the priority of the verification by reflecting literature related to the advertisement. In this way, the verification unit can optimize the verification algorithm based on literature related to the advertisement, thereby enabling more accurate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit can be performed using, or without, the generation AI, for example. For example, the verification unit can input literature data related to the advertisement into the generation AI and cause the generation AI to optimize the verification algorithm.

[0058] The verification unit can perform verification taking into account the market value of the advertisement during verification. For example, the verification unit can perform verification taking into account the market value of the advertisement during verification. For example, the verification unit can adjust the verification content based on the market value of the advertisement. The verification unit can also adjust the level of verification detail taking into account the market value of the advertisement. The verification unit can also determine the priority of verification based on the market value of the advertisement. This allows the verification unit to perform verification taking into account the market value of the advertisement, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit may be performed using, or without, the generation AI, for example. For example, the verification unit can input market value data of the advertisement into the generation AI and cause the generation AI to adjust the verification content.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The design department can analyze past survey results and select the optimal questions and survey method. For example, the design department can select questions that have received a high response rate from past survey results. The design department can also select a survey method that is effective for a specific target demographic based on past survey results. For example, the design department can select a survey method such as an online survey or a telephone survey based on past survey results. In this way, the design department can improve the accuracy of the survey by selecting the optimal questions and survey method based on past survey results. Some or all of the above-mentioned processing in the design department may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the design department can input past survey results into a generation AI and have the generation AI select the questions and survey method.

[0061] The collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the collection unit can select the optimal data collection method based on the user's past behavioral data. The collection unit can also analyze the user's past behavioral data and select an effective timing for data collection. The collection unit can also customize the collection method by referring to the user's past behavioral data. This enables the collection unit to select the optimal collection method based on the user's past behavioral data, thereby enabling more effective data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past behavioral data into the generation AI and have the generation AI select the collection method.

[0062] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling more effective data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0063] During verification, the verification unit can adjust the verification content taking into account the timing and frequency of advertisement display. For example, the verification unit can adjust the verification content based on the timing of advertisement display. The verification unit can also adjust the verification content based on the frequency of advertisement display. The verification unit can also determine the priority of verification taking into account the timing and frequency of advertisement display. This allows the verification unit to adjust the verification content taking into account the timing and frequency of advertisement display, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-described processing in the verification unit may be performed using, or without, the generation AI, for example. For example, the verification unit can input data on the timing and frequency of advertisement display into the generation AI and cause the generation AI to adjust the verification content.

[0064] The verification unit can perform verification taking into account the market value of the advertisement during verification. For example, the verification unit can adjust the verification content based on the market value of the advertisement. The verification unit can also adjust the level of verification detail taking into account the market value of the advertisement. The verification unit can also determine the priority of verification based on the market value of the advertisement. This allows the verification unit to perform verification taking into account the market value of the advertisement, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit may be performed using, or without, the generation AI, for example. For example, the verification unit can input market value data of the advertisement into the generation AI and cause the generation AI to adjust the verification content.

[0065] The processing flow of the first embodiment will be briefly explained below.

[0066] Step 1: The design department designs the survey. For example, the design department analyzes past data and trends and proposes the optimal survey design. For example, the design department can design a questionnaire for a specific target demographic and select effective questions. The design department can also use generative AI to improve the efficiency of survey design. Step 2: The collection unit collects data from the user base. The collection unit collects user behavior data from platforms such as e-commerce sites and social media. For example, the collection unit can collect user click data and browsing history. The collection unit can also use AI to improve the efficiency of data collection. Step 3: The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes the collected data to understand user behavior patterns and interests. For example, the analysis unit can analyze the level of interest in a particular product or the click rate of an advertisement. The analysis unit can also use generative AI to improve the efficiency of data analysis. Step 4: The verification department verifies the effectiveness of the advertisement based on the analysis results obtained by the analysis department. For example, the verification department evaluates the effectiveness of the advertisement based on the collected data and analysis results and proposes the optimal advertising strategy. For example, the verification department can evaluate the effectiveness of the advertisement for a specific target demographic in real time and modify the advertising strategy as necessary. The verification department can also use AI to improve the efficiency of verifying the effectiveness of the advertisement.

[0067] (Example 2) A marketing support system according to an embodiment of the present invention combines a generation AI with a large user base and behavioral data to eliminate the cost and difficulty of verifying the effectiveness of opinion surveys that marketers face. In the marketing support system, a generation AI designs the survey to improve efficiency. Next, data is collected from a large user base, and the survey is conducted quickly and at low cost. The collected behavioral data is then analyzed to precisely verify the effectiveness of advertising. For example, in the marketing support system, a generation AI analyzes past data and trends to propose an optimal survey design. For example, the system designs a questionnaire for a specific target demographic and selects effective questions. Next, the marketing support system collects user behavior data from platforms such as e-commerce sites and social media. This enables large-scale surveys to be conducted quickly and at low cost. The collected data is analyzed by a generation AI. The generation AI analyzes the collected data to understand user behavioral patterns and interests. For example, the system analyzes the level of interest in a specific product and the click-through rate of advertisements. Finally, the marketing support system precisely verifies the effectiveness of advertising based on the analysis results. Based on the collected data and analysis results, the generation AI evaluates the effectiveness of advertising and proposes an optimal advertising strategy. This can improve the effectiveness of advertising. As a result, the marketing support system can reduce research costs and time and enable precise verification of advertising effectiveness. For example, it can evaluate the effectiveness of advertising for a specific target demographic in real time and modify advertising strategies as necessary. This improves the efficiency of marketing activities.

[0068] The marketing support system according to the embodiment includes a design unit, a collection unit, an analysis unit, and a verification unit. The design unit designs surveys. For example, the design unit analyzes past data and trends to propose optimal survey designs. For example, the design unit can design questionnaires for specific target demographics and select effective questions. The design unit can also use a generative AI to improve the efficiency of survey design. The collection unit collects data from a user base. The collection unit collects user behavior data from platforms such as e-commerce sites and social networking sites. For example, the collection unit can collect user click data and browsing history. The collection unit can also use AI to improve the efficiency of data collection. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to understand user behavior patterns and interests. For example, the analysis unit can analyze the level of interest in a specific product and the click rate of an advertisement. The analysis unit can also use a generative AI to improve the efficiency of data analysis. The verification unit verifies the advertising effectiveness based on the analysis results obtained by the analysis unit. For example, the verification unit evaluates the effectiveness of the advertising based on the collected data and the analysis results, and proposes an optimal advertising strategy. For example, the verification unit can evaluate the effectiveness of the advertising for a specific target demographic in real time and modify the advertising strategy as necessary. The verification unit can also use AI to improve the efficiency of the verification of advertising effectiveness. This allows the marketing support system according to the embodiment to efficiently perform survey design, data collection, data analysis, and verification of advertising effectiveness. For example, the marketing support system can use generative AI to improve the efficiency of survey design. The marketing support system can also use AI to improve the efficiency of data collection, data analysis, and verification of advertising effectiveness.

[0069] The collection unit can collect user behavior data from an e-commerce site or a social networking platform. The collection unit collects user behavior data from, for example, an e-commerce site. For example, the collection unit can collect user click data and browsing history. The collection unit can also collect user behavior data from a social networking platform. For example, the collection unit can collect user post content and the number of likes. This allows the collection unit to collect user behavior data from a variety of platforms. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user behavior data acquired from the e-commerce site into a generation AI and have the generation AI analyze the data.

[0070] The analysis unit can analyze the collected data to understand the user's behavioral patterns and interests. The analysis unit can, for example, analyze the collected data to understand the user's behavioral patterns. For example, the analysis unit can analyze the user's click data and browsing history to identify the user's behavioral patterns. The analysis unit can also analyze the collected data to understand the user's interests. For example, the analysis unit can analyze the content of the user's posts and the number of likes to identify the user's interests. This allows the analysis unit to understand the user's behavioral patterns and interests, enabling more precise analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI understand the user's behavioral patterns and interests.

[0071] The verification unit can evaluate the effectiveness of an advertisement based on the collected data and analysis results and propose an appropriate advertising strategy. The verification unit, for example, evaluates the effectiveness of an advertisement based on the collected data and analysis results. For example, the verification unit can evaluate the click-through rate and conversion rate of an advertisement. The verification unit can also propose an optimal advertising strategy based on the collected data and analysis results. For example, the verification unit can evaluate the effectiveness of an advertisement for a specific target demographic in real time and modify the advertising strategy as necessary. This allows the verification unit to evaluate the effectiveness of an advertisement and propose an optimal advertising strategy, thereby improving the effectiveness of the advertisement. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input the collected data and analysis results into the generation AI and cause the generation AI to evaluate the effectiveness of the advertisement and propose an advertising strategy.

[0072] The design department can analyze past data and trends and propose an appropriate survey design. The design department, for example, analyzes past data and proposes an appropriate survey design. For example, the design department can analyze past sales data and advertising data to create an optimal survey design. The design department can also analyze trends and propose an appropriate survey design. For example, the design department can use time series analysis and trend analysis to create an optimal survey design. This allows the design department to create an optimal survey design based on past data and trends, thereby improving the accuracy of the survey. Some or all of the above-mentioned processing in the design department may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the design department can input past data and trends into a generation AI and have the generation AI execute a survey design proposal.

[0073] The verification unit can evaluate the effectiveness of an advertisement for a specific target demographic in real time and modify the advertising strategy as necessary. The verification unit, for example, evaluates the effectiveness of an advertisement for a specific target demographic in real time. For example, the verification unit can evaluate the click-through rate and conversion rate of the advertisement in real time. The verification unit can also modify the advertising strategy as necessary. For example, the verification unit can quickly modify the advertising strategy based on the evaluation results obtained in real time. This allows the verification unit to evaluate the effectiveness of the advertisement in real time and quickly modify the advertising strategy, thereby maximizing the effectiveness of the advertisement. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can evaluate the effectiveness of the advertisement for a specific target demographic in real time and cause the generation AI to modify the advertising strategy.

[0074] The design unit can estimate the user's emotions and adjust the survey design based on the estimated user emotions. For example, the design unit can estimate the user's emotions and adjust the survey design based on the estimated user emotions. For example, if the user is feeling stressed, the design unit can simplify the questions to reduce the burden of answering. Furthermore, if the user is relaxed, the design unit can design a survey that includes detailed questions to gain deeper insights. Furthermore, if the user is excited, the design unit can design a survey that includes interactive elements to increase the user's motivation to answer. This allows the design unit to adjust the survey design according to the user's emotions, enabling a more appropriate survey. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the design unit can be performed using, for example, the generation AI, or can be performed without the generation AI. For example, the design unit can input the user's emotion data into the generation AI and have the generation AI adjust the survey design.

[0075] The design department can analyze past survey results and select the optimal questions and survey method. The design department, for example, analyzes past survey results and selects the optimal questions. For example, the design department can select questions that have received a high response rate from past survey results. The design department can also select a survey method that is effective for a specific target demographic based on past survey results. For example, the design department can select a survey method such as an online survey or a telephone survey based on past survey results. This allows the design department to select the optimal questions and survey method based on past survey results, thereby improving the accuracy of the survey. Some or all of the above-mentioned processing in the design department may be performed using, or without, a generation AI. For example, the design department can input past survey results into a generation AI and have the generation AI select the questions and survey method.

[0076] The design department can customize the survey design taking into account the attribute information of the survey subjects. The design department customizes the survey design taking into account, for example, the attribute information of the survey subjects. For example, the design department can adjust the difficulty level of questions depending on the age group of the survey subjects. The design department can also add highly relevant questions depending on the occupation of the survey subjects. The design department can also include region-specific questions depending on the region of the survey subjects. This allows the design department to customize the survey design based on the attribute information of the survey subjects, enabling a more appropriate survey. Some or all of the above-mentioned processing in the design department may be performed using, or without, a generation AI. For example, the design department can input attribute information of the survey subjects into the generation AI and have the generation AI customize the survey design.

[0077] The design department can adjust the design content when designing a survey, taking into account external factors such as seasons and events. For example, the design department can adjust the design content when designing a survey, taking into account external factors such as seasons and events. For example, the design department can add seasonally specific questions depending on the season. The design department can also include questions related to specific event periods. The design department can also predict response trends and adjust the design content depending on the season or event. This allows the design department to design a survey taking into account external factors such as seasons and events, enabling a more appropriate survey. Some or all of the above-mentioned processing in the design department can be performed using, or without, a generation AI. For example, the design department can input information about seasons and events into the generation AI and have the generation AI adjust the survey design.

[0078] The design unit can estimate a user's emotions and prioritize survey design based on the estimated user emotions. The design unit, for example, estimates a user's emotions and prioritizes survey design based on the estimated user emotions. For example, the design unit can prioritize simple questions when the user is stressed. The design unit can also prioritize detailed questions when the user is relaxed. The design unit can also prioritize interactive questions when the user is excited. This allows the design unit to prioritize survey design based on the user's emotions, enabling a more appropriate survey. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the design unit may be performed using, for example, the generation AI, or may be performed without the generation AI. For example, the design unit can input user emotion data into the generation AI and have the generation AI determine the priorities of the survey design.

[0079] The design unit can customize the design content by taking into account the user's geographic location information when designing a survey. For example, the design unit can customize the design content by taking into account the user's geographic location information when designing a survey. For example, the design unit can add region-specific questions based on the user's geographic location information. The design unit can also design questions related to regional trends by taking into account the user's geographic location information. The design unit can also design questions tailored to regional culture and customs based on the user's geographic location information. This allows the design unit to customize the survey design based on the user's geographic location information, enabling a more appropriate survey. Some or all of the above-described processing in the design unit may be performed using, or without, a generation AI. For example, the design unit can input the user's geographic location information into the generation AI and have the generation AI customize the survey design.

[0080] The design unit can analyze a user's social media activity and add relevant questions when designing a survey. For example, the design unit can analyze a user's social media activity and add relevant questions when designing a survey. For example, the design unit can analyze a user's social media posts and design relevant questions. The design unit can also customize questions based on the user's social media interests. The design unit can also design relevant questions based on the activities of the user's friends on social media. This allows the design unit to add questions based on the user's social media activity, enabling a more appropriate survey. Some or all of the above-described processing in the design unit can be performed using, or without, a generation AI. For example, the design unit can input the user's social media activity data into the generation AI and have the generation AI add questions.

[0081] The design unit can improve the design content by reflecting the user's past feedback when designing a survey. For example, the design unit can improve the design content by reflecting the user's past feedback when designing a survey. For example, the design unit can improve the content of questions based on the user's past feedback. The design unit can also optimize the survey method by referring to the user's past feedback. The design unit can also design a survey that reduces the burden of responding by reflecting the user's past feedback. This allows the design unit to improve the design content by reflecting the user's past feedback, enabling a more appropriate survey. Some or all of the above-mentioned processing in the design unit may be performed using, or without, a generation AI. For example, the design unit can input the user's past feedback data into the generation AI and have the generation AI improve the design content.

[0082] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, the collection unit can collect data during a time when the user is relaxed. The collection unit can also collect data during a time when the user is not feeling stressed. The collection unit can also collect data during a time when the user is excited to increase the user's motivation to respond. This enables the collection unit to adjust the timing of data collection according to the user's emotions, thereby enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0083] The collection unit can analyze the user's past behavioral data and select the optimal collection method. The collection unit, for example, analyzes the user's past behavioral data and selects the optimal collection method. For example, the collection unit can select the optimal data collection method based on the user's past behavioral data. The collection unit can also analyze the user's past behavioral data and select an effective timing for data collection. The collection unit can also customize the collection method by referring to the user's past behavioral data. This enables the collection unit to select the optimal collection method based on the user's past behavioral data, thereby enabling more effective data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's past behavioral data into the generation AI and have the generation AI select the collection method.

[0084] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can filter data based on the user's current living situation and areas of interest when collecting data. For example, the collection unit can prioritize collecting highly relevant data based on the user's current living situation. The collection unit can also filter the data to be collected based on the user's areas of interest. The collection unit can also determine the priority of the data to be collected taking into account the user's living situation and areas of interest. This allows the collection unit to collect more relevant data by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input data on the user's living situation and areas of interest into the generation AI and have the generation AI perform data filtering.

[0085] The collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, when the user uses voice input, the collection unit can prioritize collecting voice data. Furthermore, when the user uses text input, the collection unit can prioritize collecting text data. Furthermore, when the user uses image input, the collection unit can prioritize collecting image data. This allows the collection unit to select the optimal collection means depending on the user's input method, enabling more effective data collection. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's input data to the generation AI and cause the generation AI to select the optimal collection means.

[0086] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit, for example, estimates the user's emotions and determines the priority of data to be collected based on the estimated user emotions. For example, the collection unit can prioritize collecting detailed data when the user is relaxed. The collection unit can also prioritize collecting concise data when the user is stressed. The collection unit can also prioritize collecting interactive data when the user is excited. This enables the collection unit to prioritize data to be collected according to the user's emotions, thereby enabling more appropriate data collection. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the data.

[0087] The collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the collection unit can prioritize collecting region-specific data based on the user's geographical location information. The collection unit can also collect data related to regional trends by taking into account the user's geographical location information. The collection unit can also collect data related to regional culture and customs based on the user's geographical location information. This enables the collection unit to prioritize collecting highly relevant data based on the user's geographical location information, thereby enabling more effective data collection. Some or all of the above-described processing by the collection unit may be performed using, or without, a generation AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to collect highly relevant data.

[0088] The collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the collection unit can analyze the content of the user's social media posts and collect related data. The collection unit can also collect data based on the user's social media interests. The collection unit can also collect related data with reference to the activities of the user's friends on social media. This allows the collection unit to collect related data based on the user's social media activities, enabling more appropriate data collection. Some or all of the above-mentioned processing in the collection unit can be performed using, or without, the generation AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0089] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit customizes the collection method by reflecting the user's past feedback when collecting data. For example, the collection unit can improve the data collection method based on the user's past feedback. The collection unit can also adjust the type of data to be collected by referring to the user's past feedback. The collection unit can also determine the priority of the data to be collected by reflecting the user's past feedback. In this way, the collection unit can customize the collection method by reflecting the user's past feedback, thereby enabling more appropriate data collection. Some or all of the above-described processing in the collection unit may be performed using, or without, the generation AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0090] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, the analysis unit can perform a detailed data analysis when the user is relaxed. The analysis unit can also perform a concise data analysis when the user is stressed. The analysis unit can also perform an interactive data analysis when the user is excited. This allows the analysis unit to adjust the data analysis method according to the user's emotions, enabling more appropriate data analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, the generative AI, or can be performed without using the generative AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the data analysis method.

[0091] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit, for example, adjusts the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. In this way, the analysis unit can adjust the level of detail of the analysis based on the importance of the data, thereby enabling more effective data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0092] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit can apply a natural language processing algorithm to text data. The analysis unit can also apply an image recognition algorithm to image data. The analysis unit can also apply a voice recognition algorithm to voice data. In this way, the analysis unit can apply different analysis algorithms depending on the category of data, enabling more appropriate data analysis. Some or all of the above-mentioned processing in the analysis unit can be performed using, or without, the generation AI, for example. For example, the analysis unit can input the category of data to the generation AI and have the generation AI select the analysis algorithm to be applied.

[0093] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can optimize the analysis algorithm based on the user's past analysis results. The analysis unit can also adjust the level of detail of the analysis by referring to the user's past analysis results. The analysis unit can also determine the priority of the analysis by reflecting the user's past analysis results. In this way, the analysis unit improves the accuracy of the analysis by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's past analysis results into the generation AI and have the generation AI improve the accuracy of the analysis.

[0094] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, the analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can provide a display method that focuses on the main points. This allows the analysis unit to adjust the display method of the analysis results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, the generation AI, or without the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the display method of the analysis results.

[0095] The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit, for example, determines the priority of analysis based on the time of data submission during analysis. For example, the analysis unit can prioritize analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. The analysis unit can also adjust the analysis schedule based on the time of submission. In this way, the analysis unit can determine the priority of analysis based on the time of data submission, thereby enabling more effective data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the time of data submission into the generation AI and have the generation AI determine the priority of analysis.

[0096] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. For example, the analysis unit can prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. The analysis unit can also adjust the analysis schedule based on the relevance of the data. In this way, the analysis unit can adjust the order of analysis based on the relevance of the data, thereby enabling more effective data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and have the generation AI adjust the order of analysis.

[0097] The analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis results according to the user's level of expertise during analysis. For example, the analysis unit can provide analysis results that use a lot of technical terms to a user with high level of expertise. The analysis unit can also provide analysis results that are concise and easy to understand to a user with low level of expertise. The analysis unit can also adjust the level of detail in the analysis results according to the user's level of expertise. This allows the analysis unit to provide more appropriate information by adjusting the use of technical terms in the analysis results according to the user's level of expertise. Some or all of the above-described processing in the analysis unit can be performed using, or without, a generation AI. For example, the analysis unit can input the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms in the analysis results.

[0098] The verification unit can estimate a user's emotions and adjust the method for verifying the advertising effectiveness based on the estimated user emotions. For example, the verification unit can estimate a user's emotions and adjust the method for verifying the advertising effectiveness based on the estimated user emotions. For example, the verification unit can provide a detailed verification method when the user is relaxed. Furthermore, the verification unit can provide a simple verification method when the user is stressed. Furthermore, the verification unit can provide an interactive verification method when the user is excited. This enables the verification unit to adjust the method for verifying the advertising effectiveness according to the user's emotions, thereby enabling more appropriate verification of the advertising effectiveness. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the verification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the verification unit can input user emotion data into the generation AI and cause the generation AI to adjust the method for verifying the advertising effectiveness.

[0099] The verification unit can optimize the verification algorithm by referring to past advertising effectiveness data during verification. The verification unit can optimize the verification algorithm by referring to past advertising effectiveness data during verification, for example. For example, the verification unit can optimize the verification algorithm based on past advertising effectiveness data. The verification unit can also adjust the level of verification detail by referring to past advertising effectiveness data. The verification unit can also determine the priority of verification by reflecting past advertising effectiveness data. In this way, the verification unit can optimize the verification algorithm based on past advertising effectiveness data, thereby enabling more accurate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit may be performed using, or without, the generation AI, for example. For example, the verification unit can input past advertising effectiveness data into the generation AI and cause the generation AI to optimize the verification algorithm.

[0100] The verification unit can improve the verification method by reflecting user feedback during verification. The verification unit can improve the verification method by reflecting user feedback during verification, for example. For example, the verification unit can improve the verification method based on user feedback. The verification unit can also adjust the level of verification detail by referring to user feedback. The verification unit can also determine the priority of verification by reflecting user feedback. In this way, the verification unit can improve the verification method by reflecting user feedback, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit can be performed using, or without, the generation AI, for example. For example, the verification unit can input user feedback data into the generation AI and cause the generation AI to improve the verification method.

[0101] The verification unit can adjust the verification content during verification, taking into account the timing and frequency of advertisement display. For example, the verification unit can adjust the verification content during verification, taking into account the timing and frequency of advertisement display. For example, the verification unit can adjust the verification content based on the timing of advertisement display. The verification unit can also adjust the verification content based on the frequency of advertisement display. The verification unit can also determine the priority of verification, taking into account the timing and frequency of advertisement display. This allows the verification unit to adjust the verification content taking into account the timing and frequency of advertisement display, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-described processing in the verification unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the verification unit can input data on the timing and frequency of advertisement display into the generation AI, and cause the generation AI to adjust the verification content.

[0102] The verification unit can estimate the user's emotions and adjust the display method of the verification results based on the estimated user emotions. For example, the verification unit can estimate the user's emotions and adjust the display method of the verification results based on the estimated user emotions. For example, if the user is nervous, the verification unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the verification unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the verification unit can provide a display method that focuses on the main points. This allows the verification unit to adjust the display method of the verification results according to the user's emotions, thereby providing more appropriate information. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the verification unit can be performed using, for example, the generation AI. For example, the verification unit can input user emotion data into the generation AI and cause the generation AI to adjust the display method of the verification results.

[0103] The verification unit can perform verification taking into account the geographic distribution of the advertisement during verification. For example, the verification unit can perform verification taking into account the geographic distribution of the advertisement during verification. For example, the verification unit can adjust the verification content based on the geographic distribution of the advertisement. The verification unit can also provide a region-specific verification method taking into account the geographic distribution of the advertisement. The verification unit can also determine the priority of verification based on the geographic distribution of the advertisement. In this way, the verification unit can perform verification taking into account the geographic distribution of the advertisement, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-described processing in the verification unit may be performed using, or without, the generation AI. For example, the verification unit can input geographic distribution data of the advertisement into the generation AI and cause the generation AI to adjust the verification content.

[0104] The verification unit can improve the accuracy of the verification by referring to literature related to the advertisement during verification. The verification unit can improve the accuracy of the verification by referring to literature related to the advertisement during verification, for example. For example, the verification unit can optimize the verification algorithm based on literature related to the advertisement. The verification unit can also adjust the level of verification detail by referring to literature related to the advertisement. The verification unit can also determine the priority of the verification by reflecting literature related to the advertisement. In this way, the verification unit can optimize the verification algorithm based on literature related to the advertisement, thereby enabling more accurate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit can be performed using, or without, the generation AI, for example. For example, the verification unit can input literature data related to the advertisement into the generation AI and cause the generation AI to optimize the verification algorithm.

[0105] The verification unit can perform verification taking into account the market value of the advertisement during verification. For example, the verification unit can perform verification taking into account the market value of the advertisement during verification. For example, the verification unit can adjust the verification content based on the market value of the advertisement. The verification unit can also adjust the level of verification detail taking into account the market value of the advertisement. The verification unit can also determine the priority of verification based on the market value of the advertisement. This allows the verification unit to perform verification taking into account the market value of the advertisement, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit may be performed using, or without, the generation AI, for example. For example, the verification unit can input market value data of the advertisement into the generation AI and cause the generation AI to adjust the verification content. === Hard Collateral 1-1 === Each of the multiple elements, including the design unit, collection unit, analysis unit, and verification unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the design unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes past data and trends to propose an optimal survey design. The collection unit is realized by the control unit 46A of the smart device 14, and collects user behavior data from platforms such as e-commerce sites and social networking sites. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to understand user behavior patterns and interests. The verification unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the effectiveness of advertising based on the collected data and analysis results, and proposes an optimal advertising strategy. === Hard Collateral 1-2 === Each of the multiple elements, including the design unit, collection unit, analysis unit, and verification unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the design unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes past data and trends to propose an optimal survey design. The collection unit is realized by the control unit 46A of the smart glasses 214, and collects user behavior data from platforms such as e-commerce sites and social networking sites. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to understand user behavior patterns and interests. The verification unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the effectiveness of advertising based on the collected data and analysis results, and proposes an optimal advertising strategy. === Hard Collateral 1-3 === Each of the multiple elements including the design unit, collection unit, analysis unit, and verification unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the design unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes past data and trends to propose an optimal survey design. The collection unit is realized by the control unit 46A of the headset-type terminal 314 and collects user behavior data from platforms such as e-commerce sites and social networking sites. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to understand user behavior patterns and interests. The verification unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of advertising based on the collected data and analysis results and proposes an optimal advertising strategy. === Hard Collateral 1-4 === Each of the multiple elements including the design unit, collection unit, analysis unit, and verification unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the design unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes past data and trends to propose an optimal survey design. The collection unit is realized by the control unit 46A of the robot 414, and collects user behavior data from platforms such as e-commerce sites and social networking sites. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, and analyzes the collected data to understand user behavior patterns and interests. The verification unit is realized by the specific processing unit 290 of the data processing device 12, and evaluates the effectiveness of advertising based on the collected data and analysis results, and proposes an optimal advertising strategy.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The design unit can estimate the user's emotions and adjust the survey design based on the estimated user emotions. For example, if the user is feeling stressed, the design unit can simplify the questions to reduce the burden of answering. Furthermore, if the user is relaxed, the design unit can design a survey that includes detailed questions to gain deeper insights. Furthermore, if the user is excited, the design unit can design a survey that includes interactive elements to increase the user's motivation to answer. This allows the design unit to adjust the survey design according to the user's emotions, enabling a more appropriate survey. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the design unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the design unit can input the user's emotion data into the generation AI and have the generation AI adjust the survey design.

[0108] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can collect data during a time when the user is relaxed. Alternatively, the collection unit can collect data during a time when the user is not feeling stressed. Alternatively, the collection unit can collect data during a time when the user is excited, thereby increasing the user's motivation to respond. This allows the collection unit to adjust the timing of data collection according to the user's emotions, thereby enabling more appropriate data collection. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of data collection.

[0109] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, the analysis unit can perform a detailed data analysis when the user is relaxed. The analysis unit can also perform a concise data analysis when the user is stressed. The analysis unit can also perform an interactive data analysis when the user is excited. This allows the analysis unit to adjust the data analysis method according to the user's emotions, enabling more appropriate data analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, the generative AI, or may be performed without using the generative AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the data analysis method.

[0110] The verification unit can estimate a user's emotions and adjust the method for verifying the advertising effectiveness based on the estimated user emotions. For example, the verification unit can provide a detailed verification method when the user is relaxed. The verification unit can also provide a simple verification method when the user is stressed. The verification unit can also provide an interactive verification method when the user is excited. This allows the verification unit to adjust the method for verifying the advertising effectiveness according to the user's emotions, thereby enabling more appropriate verification of the advertising effectiveness. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the verification unit can be performed using, for example, the generation AI, or can be performed without using the generation AI. For example, the verification unit can input user emotion data into the generation AI and cause the generation AI to adjust the method for verifying the advertising effectiveness.

[0111] The verification unit can estimate the user's emotions and adjust the display method of the verification results based on the estimated user emotions. For example, if the user is nervous, the verification unit can provide a simple, highly visible display method. If the user is relaxed, the verification unit can provide a display method including detailed information. If the user is in a hurry, the verification unit can provide a display method that focuses on the main points. This allows the verification unit to adjust the display method of the verification results according to the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the verification unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the verification unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display method of the verification results.

[0112] The design department can analyze past survey results and select the optimal questions and survey method. For example, the design department can select questions that have received a high response rate from past survey results. The design department can also select a survey method that is effective for a specific target demographic based on past survey results. For example, the design department can select a survey method such as an online survey or a telephone survey based on past survey results. In this way, the design department can improve the accuracy of the survey by selecting the optimal questions and survey method based on past survey results. Some or all of the above-mentioned processing in the design department may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the design department can input past survey results into a generation AI and have the generation AI select the questions and survey method.

[0113] The collection unit can analyze the user's past behavioral data and select the optimal collection method. For example, the collection unit can select the optimal data collection method based on the user's past behavioral data. The collection unit can also analyze the user's past behavioral data and select an effective timing for data collection. The collection unit can also customize the collection method by referring to the user's past behavioral data. This enables the collection unit to select the optimal collection method based on the user's past behavioral data, thereby enabling more effective data collection. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past behavioral data into the generation AI and have the generation AI select the collection method.

[0114] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, the analysis unit can perform a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. The analysis unit can also determine the priority of the analysis according to the importance of the data. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data, thereby enabling more effective data analysis. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and have the generation AI adjust the level of detail of the analysis.

[0115] During verification, the verification unit can adjust the verification content taking into account the timing and frequency of advertisement display. For example, the verification unit can adjust the verification content based on the timing of advertisement display. The verification unit can also adjust the verification content based on the frequency of advertisement display. The verification unit can also determine the priority of verification taking into account the timing and frequency of advertisement display. This allows the verification unit to adjust the verification content taking into account the timing and frequency of advertisement display, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-described processing in the verification unit may be performed using, or without, the generation AI, for example. For example, the verification unit can input data on the timing and frequency of advertisement display into the generation AI and cause the generation AI to adjust the verification content.

[0116] The verification unit can perform verification taking into account the market value of the advertisement during verification. For example, the verification unit can adjust the verification content based on the market value of the advertisement. The verification unit can also adjust the level of verification detail taking into account the market value of the advertisement. The verification unit can also determine the priority of verification based on the market value of the advertisement. This allows the verification unit to perform verification taking into account the market value of the advertisement, thereby enabling more appropriate verification of advertising effectiveness. Some or all of the above-mentioned processing in the verification unit may be performed using, or without, the generation AI, for example. For example, the verification unit can input market value data of the advertisement into the generation AI and cause the generation AI to adjust the verification content.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The design department designs the survey. For example, the design department analyzes past data and trends and proposes the optimal survey design. For example, the design department can design a questionnaire for a specific target demographic and select effective questions. The design department can also use generative AI to improve the efficiency of survey design. Step 2: The collection unit collects data from the user base. The collection unit collects user behavior data from platforms such as e-commerce sites and social media. For example, the collection unit can collect user click data and browsing history. The collection unit can also use AI to improve the efficiency of data collection. Step 3: The analysis unit analyzes the data collected by the collection unit. The analysis unit, for example, analyzes the collected data to understand user behavior patterns and interests. For example, the analysis unit can analyze the level of interest in a particular product or the click rate of an advertisement. The analysis unit can also use generative AI to improve the efficiency of data analysis. Step 4: The verification department verifies the effectiveness of the advertisement based on the analysis results obtained by the analysis department. For example, the verification department evaluates the effectiveness of the advertisement based on the collected data and analysis results and proposes the optimal advertising strategy. For example, the verification department can evaluate the effectiveness of the advertisement for a specific target demographic in real time and modify the advertising strategy as necessary. The verification department can also use AI to improve the efficiency of verifying the effectiveness of the advertisement.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0142] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0149] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0158] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

[0160] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0161] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0162] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0163] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0164] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0165] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0166] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0167] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0168] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0169] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0170] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0173] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0174] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0175] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0176] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0177] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0178] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0179] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0180] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0181] 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.

[0182] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0183] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0184] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0185] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0186] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0187] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0188] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0189] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0190] [Explanation of symbols]

[0191] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The design department, which carries out research and design; a collection unit that collects data from a user base; an analysis unit that analyzes the data collected by the collection unit; a verification unit that verifies advertising effectiveness based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collecting user behavior data from e-commerce sites or social media platforms 2. The system of claim 1.

3. The analysis unit Analyze collected data to understand user behavior patterns and interests 2. The system of claim 1.

4. The verification unit Evaluate advertising effectiveness based on collected data and analysis results, and propose appropriate advertising strategies 2. The system of claim 1.

5. The design unit Analyze past data and trends to propose appropriate survey designs 2. The system of claim 1.

6. The verification unit Evaluate the effectiveness of your ads for specific audiences in real time and adjust your advertising strategy as needed 2. The system of claim 1.

7. The design unit Estimate user sentiment and adjust survey design based on estimated user sentiment 2. The system of claim 1.

8. The design unit Analyze past survey results and select the most appropriate questions and survey methods 2. The system of claim 1.

9. The design unit Customize your survey design by taking into account the demographic information of your survey subjects 2. The system of claim 1.

10. The design unit When designing a survey, adjust the design to take into account external factors such as seasons and events.

2. The system of claim 1.

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