System

Abstract

An object of the system according to the embodiment is to reduce the burden of advertisement creation and to provide efficient and effective advertisement creatives.SOLUTION: A system according to an embodiment includes a reception unit, a check unit, an analysis unit, and a provision unit. The reception unit receives information from an advertiser. The checking unit checks the guideline or the legal policy based on the information received by the receiving unit. The analysis unit analyzes the market marketing data based on the information checked by the check unit. The providing unit provides an advertisement creative based on the data analyzed by the analyzing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has had the problem of placing a heavy burden on advertisers at the ad production stage.

[0005] The system according to the embodiment aims to reduce the burden of creating advertisements and provide efficient and effective advertisement creatives. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a check unit, an analysis unit, and a provision unit. The reception unit receives information from an advertiser. The check unit checks guidelines or legal policies based on the information received by the reception unit. The analysis unit analyzes market marketing data based on the information checked by the check unit. The provision unit provides advertising creatives based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can reduce the burden of creating advertisements and provide efficient and effective advertisement creatives. [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) An advertising production support system according to an embodiment of the present invention accepts information from advertisers, and a generation AI checks guidelines and legal policies and analyzes market marketing data to provide advertising creatives. In the advertising production support system, advertisers input basic information for creating advertisements, and the generation AI analyzes manufacturer and advertiser guidelines, legal policies, and market marketing data to generate advertising creatives. For example, in the advertising production support system, advertisers input information such as the purpose of the advertisement, target audience, and budget. The generation AI then analyzes the input information and references manufacturer and advertiser guidelines, legal policies, and market marketing data. The generation AI generates advertising creatives based on this data. For example, the system generates advertisements that take into account trends and consumer preferences in specific markets. The generated advertising creatives are provided in a format that is easy to reach users. For example, the generation AI optimizes the design and copy of advertisements to attract user attention. This allows advertisers to create advertisements efficiently and reach more users. The advertising production support system thus reduces the burden on advertisers and improves the efficiency of advertising production. For example, advertisers no longer need to check complex guidelines and legal policies from scratch, as Generative AI can automatically check them, saving them time and effort. Furthermore, by utilizing market marketing data, advertisers can create more effective advertisements.

[0029] An advertising production support system according to an embodiment includes a receiving unit, a checking unit, an analyzing unit, and a providing unit. The receiving unit receives information from an advertiser. The information from the advertiser includes, but is not limited to, the purpose of the advertisement, the target audience, and the budget. The receiving unit receives, for example, information input by the advertiser in digital format. The receiving unit can also receive voice input or image input. For example, the advertiser explains the purpose of the advertisement through voice and converts it into text data. The checking unit checks guidelines and legal policies based on the information received by the receiving unit. For example, the checking unit refers to guidelines from manufacturers and advertisers and verifies whether the advertisement complies with these guidelines. The checking unit also refers to legal policies and verifies whether the advertisement is legally problematic. For example, the checking unit checks whether the content of the advertisement violates copyright law or consumer protection law. The analysis unit analyzes market marketing data based on the information checked by the checking unit. For example, the analysis unit analyzes trends and consumer preferences in a specific market. The analysis unit predicts how an advertisement will be received by a target audience based on consumer survey data and sales data. For example, the analysis unit analyzes consumer purchase histories and survey results to predict the effectiveness of an advertisement. The provision unit provides advertising creatives based on the data analyzed by the analysis unit. The provision unit uses a generation AI to optimize the design and copy of the advertisement and provide advertising creatives that attract users' attention. For example, the provision unit causes the generation AI to optimize the visual design and layout of the advertisement and adjust it to attract users' attention. The provision unit also causes the generation AI to generate catchphrases and descriptions for the advertisement and provide them in a format that is easy to reach users. As a result, the advertising production support system according to the embodiment can reduce the burden on advertisers and improve the efficiency of advertising production. For example, advertisers no longer need to check complex guidelines and legal policies from scratch; the generation AI automatically checks them, saving time and effort. Furthermore, by utilizing market marketing data, more effective advertisements can be produced.

[0030] The reception unit can receive information on the purpose of the advertisement, the target audience, and the budget. Examples of the purpose of the advertisement include, but are not limited to, increasing brand awareness and promoting product sales. The reception unit can, for example, digitally receive the purpose of the advertisement input by the advertiser. The reception unit can also receive information on the target audience. Examples of the target audience include, but are not limited to, age groups, genders, and regions. The reception unit can digitally receive the target audience information input by the advertiser. The reception unit can also receive budget information. Examples of the budget include, but are not limited to, monthly budgets and campaign budgets. The reception unit can digitally receive the budget information input by the advertiser. This allows for efficient reception of basic information necessary for advertisement production. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the information input by the advertiser to a generation AI, which then receives the information.

[0031] The checking unit can check the guidelines and legal policies of the manufacturer or advertiser. The manufacturer's or advertiser's guidelines include, but are not limited to, ad formats and content restrictions. For example, the checking unit references the manufacturer's or advertiser's guidelines to confirm whether the advertisement complies with these guidelines. The checking unit also references legal policies to confirm whether the advertisement is legally problematic. Legal policies include, but are not limited to, copyright law and consumer protection law. For example, the checking unit checks whether the content of the advertisement violates copyright law or consumer protection law. This allows the advertisement to be confirmed as complying with the guidelines and legal policies. Some or all of the above-described processing by the checking unit may be performed using, or without, AI. For example, the checking unit can input the content of the advertisement into a generation AI, which then checks the guidelines and legal policies.

[0032] The analysis unit can analyze trends or consumer preferences in a specific market. Examples of specific markets include, but are not limited to, regional markets, industry markets, etc. The analysis unit, for example, prioritizes analysis of trends in a specific market. Examples of trends include, but are not limited to, changes in consumer behavior and trends in popular products. The analysis unit, for example, analyzes detailed data based on consumer preferences. Examples of consumer preferences include, but are not limited to, purchase history and survey results. The analysis unit, for example, integrates market trends and consumer preferences and adjusts the level of detail of the analysis. This allows advertisements to be created based on market trends and consumer preferences. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input consumer preference data into a generation AI, which then analyzes the data.

[0033] The providing unit can optimize the design or copy of the advertisement using the generation AI and provide an advertisement creative that attracts the user's interest. Examples of the generation AI include, but are not limited to, technologies such as deep learning and natural language generation. For example, the providing unit can optimize the visual design and layout of the advertisement using the generation AI and adjust it to attract the user's interest. Examples of the advertisement design include, but are not limited to, visual design and layout. For example, the providing unit can generate a catchphrase and description for the advertisement and provide it in a format that is easy to reach the user. Examples of the copy include, but are not limited to, catchphrases and descriptions. This makes it possible to provide an advertisement creative that attracts the user's interest. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide the advertisement creative generated by the generation AI to the user.

[0034] The reception unit can analyze the advertiser's past advertising production history and select an information reception method. The advertiser's past advertising production history includes, for example, past campaign data, feedback, etc., but is not limited to these examples. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the advertiser has used in the past. The reception unit can also analyze patterns of advertisements created by the advertiser in the past and suggest similar input methods. The reception unit can also provide an optimal input interface based on the advertiser's past feedback. This makes it possible to provide an optimal information reception method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the advertiser's past advertising production history data into the generation AI, and the generation AI can select an information reception method.

[0035] The reception unit can determine the priority of information to be received based on the purpose of the advertisement and the target audience. Examples of the purpose of the advertisement include, but are not limited to, increasing brand awareness and promoting product sales. For example, if the purpose of the advertisement is clear, the reception unit can prioritize information related to that purpose. Examples of the target audience include, but are not limited to, age groups, genders, and regions. For example, if the target audience is a specific age group, the reception unit can prioritize information related to that age group. The reception unit can also automatically classify and prioritize necessary information based on the purpose of the advertisement and the target audience. This allows the priority of information to be determined according to the purpose of the advertisement and the target audience. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input information about the purpose of the advertisement and the target audience into a generation AI, which can then prioritize the information.

[0036] The reception unit can select the reception means according to the advertiser's input method. Examples of the advertiser's input method include, but are not limited to, voice input, text input, and image input. For example, if the advertiser selects voice input, the reception unit can receive information using voice recognition technology. Furthermore, if the advertiser selects text input, the reception unit can also receive information using text analysis technology. Furthermore, if the advertiser selects image input, the reception unit can also receive information using image recognition technology. This makes it possible to provide the optimal reception means according to the advertiser's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the advertiser's input method data into a generation AI, which can then select the optimal reception means.

[0037] The reception unit can preferentially accept highly relevant information based on the advertiser's geographical location information. Examples of the advertiser's geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the advertiser is in a specific area, the reception unit can preferentially accept information related to that area. Furthermore, if the advertiser is traveling, the reception unit can also accept optimal information based on the advertiser's current location. Furthermore, the reception unit can preferentially accept related market data based on the advertiser's geographical location information. This allows highly relevant information based on the geographical location information to be preferentially accepted. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the advertiser's geographical location information data to the generation AI, which can then preferentially accept highly relevant information.

[0038] The reception unit may analyze the advertiser's social media activity and receive related information. The advertiser's social media activity may include, but is not limited to, the content of posts and reactions from followers. The reception unit may receive related advertising information, for example, based on information shared by the advertiser on social media. The reception unit may also analyze the advertiser's social media activity and receive related market data. The reception unit may also receive related information by referring to the activities of the advertiser's friends on social media. This allows reception of related information based on social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the advertiser's social media activity data into the generation AI, which may receive the related information.

[0039] The reception unit can customize the reception method based on the advertiser's past feedback. The advertiser's past feedback includes, but is not limited to, for example, survey results and reviews. The reception unit, for example, provides an optimal input interface based on the feedback provided by the advertiser in the past. The reception unit can also analyze the advertiser's past feedback and optimize the input procedure. The reception unit can also customize the reception method by reflecting the advertiser's feedback. This allows the reception method to be customized based on the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the advertiser's past feedback data into the generation AI, which can then customize the reception method.

[0040] The checking unit can adjust the level of detail of the check based on the purpose of the advertisement or the target audience. Examples of the purpose of the advertisement include, but are not limited to, increasing brand awareness and promoting product sales. For example, if the purpose of the advertisement is clear, the checking unit prioritizes checking guidelines related to that purpose. Examples of the target audience include, but are not limited to, age groups, genders, and regions. For example, if the target audience is a specific age group, the checking unit prioritizes checking legal policies related to that age group. The checking unit can also automatically classify necessary check items and adjust the level of detail based on the purpose of the advertisement and the target audience. This allows the level of detail of the check to be adjusted according to the purpose of the advertisement and the target audience. Some or all of the above-described processing in the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input information about the purpose of the advertisement and the target audience into a generation AI, which can then adjust the level of detail of the check.

[0041] The checking unit can improve the accuracy of the check by referring to guidelines from different industries or regions. Guidelines from different industries or regions include, but are not limited to, industry standards and region-specific regulations. For example, the checking unit can refer to guidelines from different industries to check whether the content of an advertisement complies with the guidelines. The checking unit can also refer to legal policies from different regions to check whether the advertisement is legally problematic. The checking unit can also integrate guidelines from different industries or regions to improve the accuracy of the check. This allows the accuracy of the check to be improved by referring to guidelines from different industries or regions. Some or all of the above-described processing in the checking unit can be performed using, for example, AI, or can be performed without using AI. For example, the checking unit can input guideline data from different industries or regions into the generation AI, which can then improve the accuracy of the check.

[0042] The check unit can improve the efficiency of checks based on past check results. Past check results include, but are not limited to, checklists and feedback. For example, the check unit automatically suggests check items for similar advertisements based on the past check results. The check unit can also analyze the past check results and provide an efficient check method. The check unit can also determine the priority of checks by referring to the past check results. This can improve the efficiency of checks by referring to the past check results. Some or all of the above-mentioned processing in the check unit can be performed using, for example, AI, or can be performed without using AI. For example, the check unit can input past check result data into a generation AI, which can improve the efficiency of checks.

[0043] The checking unit can determine the priority of checks depending on the time of advertisement submission. The time of advertisement submission includes, but is not limited to, for example, the campaign start date and closing date. For example, if the advertisement submission time is approaching, the checking unit will check the advertisement preferentially. Furthermore, if the advertisement submission time is far away, the checking unit can also perform a more detailed check. Furthermore, the checking unit can automatically determine the priority of checks based on the time of advertisement submission. This allows the priority of checks to be determined based on the time of advertisement submission. Some or all of the above-mentioned processing in the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input advertisement submission time data into the generation AI, and the generation AI can determine the priority of checks.

[0044] The checking unit can adjust the order of checks depending on the relevance of the advertisement. Examples of the relevance of the advertisement include, but are not limited to, relevance to the target audience and relevance to the purpose of the advertisement. For example, if the relevance of the advertisement is high, the checking unit can check the advertisement preferentially. Furthermore, if the relevance of the advertisement is low, the checking unit can also perform a more detailed check. Furthermore, the checking unit can automatically adjust the order of checks based on the relevance of the advertisement. This allows the order of checks to be adjusted based on the relevance of the advertisement. Some or all of the above-described processing in the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input relevance data of the advertisement into a generating AI, which can then adjust the order of checks.

[0045] The check unit can adjust the level of detail of the check based on the advertiser's level of expertise. The advertiser's level of expertise includes, but is not limited to, past experience, qualifications, etc. For example, the check unit performs a detailed check when the advertiser's level of expertise is high. The check unit can also perform a simple check when the advertiser's level of expertise is low. The check unit can also automatically adjust the level of detail of the check based on the advertiser's level of expertise. This allows the level of detail of the check to be adjusted according to the advertiser's level of expertise. Some or all of the above-mentioned processing in the check unit may be performed using, for example, AI, or may be performed without using AI. For example, the check unit can input the advertiser's level of expertise data into the generation AI, which can then adjust the level of detail of the check.

[0046] The analysis unit can adjust the level of detail of the analysis based on trends in a specific market or consumer preferences. Specific markets include, but are not limited to, regional markets, industry markets, etc. The analysis unit, for example, prioritizes analysis of trends in a specific market. Trends include, but are not limited to, changes in consumer behavior and trends in popular products. The analysis unit analyzes detailed data based on, for example, consumer preferences. Consumer preferences include, but are not limited to, purchase history and survey results. The analysis unit, for example, integrates market trends and consumer preferences to adjust the level of detail of the analysis. This allows the level of detail of the analysis to be adjusted based on market trends and consumer preferences. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input consumer preference data into a generation AI, which then adjusts the level of detail of the analysis.

[0047] The analysis unit can integrate different data sources to improve the accuracy of the analysis. Examples of different data sources include, but are not limited to, social media data and sales data. For example, the analysis unit can integrate different data sources to improve the accuracy of the analysis. The analysis unit can also integrate different market data to perform detailed analysis. The analysis unit can also integrate different consumer data to improve the accuracy of the analysis. Thus, by integrating different data sources, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input different data sources into a generation AI, which then performs data integration.

[0048] The analysis unit can improve the efficiency of analysis based on past analysis results. Past analysis results include, but are not limited to, past reports and feedback. For example, the analysis unit prioritizes analysis of similar market data based on past analysis results. The analysis unit can also analyze past analysis results and provide an efficient analysis method. The analysis unit can also determine the priority of analysis by referring to past analysis results. This can improve the efficiency of analysis by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, which can improve the efficiency of analysis.

[0049] The analysis unit can determine the priority of analysis based on the time of advertisement submission. The time of advertisement submission includes, but is not limited to, for example, the campaign start date and closing date. For example, if the advertisement submission time is approaching, the analysis unit prioritizes analyzing market data related to the advertisement. Furthermore, if the advertisement submission time is far away, the analysis unit can also analyze detailed market data. Furthermore, the analysis unit can automatically determine the priority of analysis based on the time of advertisement submission. This makes it possible to determine the priority of analysis based on the time of advertisement submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input advertisement submission time data into a generation AI, which can then determine the priority of analysis.

[0050] The analysis unit can adjust the order of analysis depending on the relevance of the advertisement. Examples of the relevance of the advertisement include, but are not limited to, relevance to the target audience and relevance to the purpose of the advertisement. For example, if the relevance of the advertisement is high, the analysis unit prioritizes analyzing market data related to the advertisement. Furthermore, if the relevance of the advertisement is low, the analysis unit can also analyze detailed market data. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of the advertisement. This allows the order of analysis based on the relevance of the advertisement to be adjusted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the advertisement to a generation AI, which can then adjust the order of analysis.

[0051] The analysis unit can adjust the level of detail of the analysis based on the advertiser's level of expertise. Examples of the advertiser's level of expertise include, but are not limited to, past experience and qualifications. For example, the analysis unit analyzes detailed market data when the advertiser's level of expertise is high. Alternatively, the analysis unit can analyze simple market data when the advertiser's level of expertise is low. Alternatively, the analysis unit can automatically adjust the level of detail of the analysis based on the advertiser's level of expertise. This allows the level of detail of the analysis to be adjusted according to the advertiser's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the advertiser's level of expertise data into the generation AI, which can then adjust the level of detail of the analysis.

[0052] The providing unit may adjust the level of detail of the provision based on the purpose of the advertisement or the target audience. Examples of the purpose of the advertisement include, but are not limited to, increasing brand awareness and promoting product sales. For example, if the purpose of the advertisement is clear, the providing unit may preferentially provide advertising creatives related to the purpose. Examples of the target audience include, but are not limited to, age groups, genders, and regions. For example, if the target audience is a specific age group, the providing unit may preferentially provide advertising creatives related to that age group. The providing unit may also automatically adjust the level of detail of the provision based on the purpose of the advertisement and the target audience. This allows the level of detail of the provision to be adjusted according to the purpose of the advertisement and the target audience. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input information about the purpose of the advertisement and the target audience into a generation AI, which may then adjust the level of detail of the provision.

[0053] The providing unit can generate different design or copy variations to improve the accuracy of the offering. Examples of different design or copy variations include, but are not limited to, color variations and differences in writing style. For example, the providing unit can generate advertising creatives with different designs and provide the optimal one. The providing unit can also generate different copy variations and provide the optimal one. The providing unit can also integrate design and copy variations to improve the accuracy of the offering. This allows the accuracy of the offering to be improved by generating different design and copy variations. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input data on different design and copy variations into a generation AI, which then generates the variations.

[0054] The provision unit can improve the efficiency of provision based on past provision results. Past provision results include, but are not limited to, campaign results, user feedback, etc. The provision unit, for example, preferentially provides similar advertising creatives based on past provision results. The provision unit can also analyze past provision results and provide an efficient provision method. The provision unit can also determine provision priorities by referring to past provision results. This can improve the efficiency of provision by referring to past provision results. Some or all of the above-described processing in the provision unit can be performed using, for example, AI, or can be performed without using AI. For example, the provision unit can input past provision result data into a generation AI, which can improve the efficiency of provision.

[0055] The provision unit can determine the priority of provision based on the time of advertisement submission. The time of advertisement submission includes, but is not limited to, for example, a campaign start date, a closing date, etc. For example, if the advertisement submission time is approaching, the provision unit can provide the advertisement creative with priority. Furthermore, if the advertisement submission time is far away, the provision unit can also provide detailed advertisement creative. Furthermore, the provision unit can automatically determine the priority of provision based on the time of advertisement submission. In this way, the priority of provision can be determined based on the time of advertisement submission. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input advertisement submission time data into a generation AI, and the generation AI can determine the priority of provision.

[0056] The providing unit can adjust the order of provision depending on the relevance of the advertisements. Examples of the relevance of the advertisements include, but are not limited to, relevance to the target audience and relevance to the purpose of the advertisement. For example, if the relevance of the advertisement is high, the providing unit can provide the advertisement creative with priority. Furthermore, if the relevance of the advertisement is low, the providing unit can also provide more detailed advertisement creative. Furthermore, the providing unit can automatically adjust the order of provision based on the relevance of the advertisements. This allows the order of provision based on the relevance of the advertisements to be adjusted. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the advertisements to a generation AI, which can then adjust the order of provision.

[0057] The providing unit can adjust the level of detail of the provision based on the advertiser's expertise level. The advertiser's expertise level includes, but is not limited to, past experience, qualifications, etc. For example, the providing unit can provide detailed advertising creatives when the advertiser's expertise level is high. Furthermore, the providing unit can also provide simple advertising creatives when the advertiser's expertise level is low. Furthermore, the providing unit can automatically adjust the level of detail of the provision based on the advertiser's expertise level. This allows the level of detail of the provision to be adjusted according to the advertiser's expertise level. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the advertiser's expertise level data to the generating AI, which can then adjust the level of detail of the provision.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The reception unit can analyze the success rate of the advertiser's past advertising campaigns and preferentially receive elements of successful campaigns. For example, it can extract elements of advertisements that have recorded high click-through rates in the past and preferentially receive information containing similar elements. It can also analyze elements of advertisements that have achieved high conversion rates in past campaigns and preferentially receive information containing these elements. It can also extract elements of advertisements that have received high ratings from users in past campaigns and preferentially receive information containing these elements. This makes it possible to provide an optimal information reception method based on past success factors.

[0060] The reception unit can preferentially receive highly relevant information based on the geographical location information of the advertiser. For example, if the advertiser is in a specific area, it can preferentially receive information related to that area. Also, if the advertiser is traveling, it can also receive optimal information based on the current location. Furthermore, it can also preferentially receive related market data based on the geographical location information of the advertiser. This makes it possible to preferentially receive highly relevant information based on the geographical location information.

[0061] The checking unit can improve the accuracy of the check by referring to guidelines from different industries or regions. For example, it can refer to guidelines from different industries to check whether the content of an advertisement complies with them. It can also refer to legal policies from different regions to check whether the advertisement is legally problematic. Furthermore, it can integrate guidelines from different industries or regions to improve the accuracy of the check. This allows the accuracy of the check to be improved by referring to guidelines from different industries or regions.

[0062] The analysis unit can improve the accuracy of the analysis by integrating different data sources. For example, different data sources can be integrated to improve the accuracy of the analysis. In addition, different market data can be integrated to perform detailed analysis. Furthermore, different consumer data can be integrated to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by integrating different data sources.

[0063] The providing unit can improve the accuracy of the provision by generating different design or copy variations. For example, it can generate advertising creatives with different designs and provide the optimal one. It can also generate different copy variations and provide the optimal one. Furthermore, it can integrate design and copy variations to improve the accuracy of the provision. This makes it possible to improve the accuracy of the provision by generating different design and copy variations.

[0064] The provision unit can improve the efficiency of provision based on past provision results. For example, similar advertising creatives can be provided preferentially based on past provision results. Also, the provision unit can analyze past provision results and provide an efficient provision method. Furthermore, the provision priority can be determined by referring to past provision results. In this way, the efficiency of provision can be improved by referring to past provision results.

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

[0066] Step 1: The reception unit receives information from the advertiser. This information includes the purpose of the advertisement, the target audience, and the budget. The reception unit receives the information entered by the advertiser in digital form, and can also receive voice input and image input. For example, the advertiser may explain the purpose of the advertisement by voice, and this information is converted into text data. Step 2: The Checking Department checks the guidelines and legal policies based on the information received by the Reception Department. The Checking Department refers to the guidelines of the manufacturer and the advertising destination and verifies whether the advertisement complies with these guidelines. They also refer to legal policies to verify that the advertisement is legally sound. For example, they check whether the content of the advertisement violates copyright law or consumer protection law. Step 3: The analysis unit analyzes the market marketing data based on the information checked by the checking unit. The analysis unit analyzes trends and consumer preferences in specific markets and predicts how the advertisement will be received by the target audience based on consumer survey data and sales data. For example, it analyzes consumer purchasing history and survey results to predict the effectiveness of the advertisement. Step 4: The provision unit provides ad creatives based on the data analyzed by the analysis unit. The provision unit uses generation AI to optimize the ad design and copy and provide ad creatives that attract users' attention. For example, the generation AI optimizes the visual design and layout of the ad and adjusts it to attract users' attention. The generation AI also generates ad catchphrases and descriptions and provides them in a format that is easy for users to understand.

[0067] (Example 2) An advertising production support system according to an embodiment of the present invention accepts information from advertisers, and a generation AI checks guidelines and legal policies and analyzes market marketing data to provide advertising creatives. In the advertising production support system, advertisers input basic information for creating advertisements, and the generation AI analyzes manufacturer and advertiser guidelines, legal policies, and market marketing data to generate advertising creatives. For example, in the advertising production support system, advertisers input information such as the purpose of the advertisement, target audience, and budget. The generation AI then analyzes the input information and references manufacturer and advertiser guidelines, legal policies, and market marketing data. The generation AI generates advertising creatives based on this data. For example, the system generates advertisements that take into account trends and consumer preferences in specific markets. The generated advertising creatives are provided in a format that is easy to reach users. For example, the generation AI optimizes the design and copy of advertisements to attract user attention. This allows advertisers to create advertisements efficiently and reach more users. The advertising production support system thus reduces the burden on advertisers and improves the efficiency of advertising production. For example, advertisers no longer need to check complex guidelines and legal policies from scratch, as Generative AI can automatically check them, saving them time and effort. Furthermore, by utilizing market marketing data, advertisers can create more effective advertisements.

[0068] An advertising production support system according to an embodiment includes a receiving unit, a checking unit, an analyzing unit, and a providing unit. The receiving unit receives information from an advertiser. The information from the advertiser includes, but is not limited to, the purpose of the advertisement, the target audience, and the budget. The receiving unit receives, for example, information input by the advertiser in digital format. The receiving unit can also receive voice input or image input. For example, the advertiser explains the purpose of the advertisement through voice and converts it into text data. The checking unit checks guidelines and legal policies based on the information received by the receiving unit. For example, the checking unit refers to guidelines from manufacturers and advertisers and verifies whether the advertisement complies with these guidelines. The checking unit also refers to legal policies and verifies whether the advertisement is legally problematic. For example, the checking unit checks whether the content of the advertisement violates copyright law or consumer protection law. The analysis unit analyzes market marketing data based on the information checked by the checking unit. For example, the analysis unit analyzes trends and consumer preferences in a specific market. The analysis unit predicts how an advertisement will be received by a target audience based on consumer survey data and sales data. For example, the analysis unit analyzes consumer purchase histories and survey results to predict the effectiveness of an advertisement. The provision unit provides advertising creatives based on the data analyzed by the analysis unit. The provision unit uses a generation AI to optimize the design and copy of the advertisement and provide advertising creatives that attract users' attention. For example, the provision unit causes the generation AI to optimize the visual design and layout of the advertisement and adjust it to attract users' attention. The provision unit also causes the generation AI to generate catchphrases and descriptions for the advertisement and provide them in a format that is easy to reach users. As a result, the advertising production support system according to the embodiment can reduce the burden on advertisers and improve the efficiency of advertising production. For example, advertisers no longer need to check complex guidelines and legal policies from scratch; the generation AI automatically checks them, saving time and effort. Furthermore, by utilizing market marketing data, more effective advertisements can be produced.

[0069] The reception unit can receive information on the purpose of the advertisement, the target audience, and the budget. Examples of the purpose of the advertisement include, but are not limited to, increasing brand awareness and promoting product sales. The reception unit can, for example, digitally receive the purpose of the advertisement input by the advertiser. The reception unit can also receive information on the target audience. Examples of the target audience include, but are not limited to, age groups, genders, and regions. The reception unit can digitally receive the target audience information input by the advertiser. The reception unit can also receive budget information. Examples of the budget include, but are not limited to, monthly budgets and campaign budgets. The reception unit can digitally receive the budget information input by the advertiser. This allows for efficient reception of basic information necessary for advertisement production. Some or all of the above-described processing in the reception unit can be performed, for example, using AI or without AI. For example, the reception unit can input the information input by the advertiser to a generation AI, which then receives the information.

[0070] The checking unit can check the guidelines and legal policies of the manufacturer or advertiser. The manufacturer's or advertiser's guidelines include, but are not limited to, ad formats and content restrictions. For example, the checking unit references the manufacturer's or advertiser's guidelines to confirm whether the advertisement complies with these guidelines. The checking unit also references legal policies to confirm whether the advertisement is legally problematic. Legal policies include, but are not limited to, copyright law and consumer protection law. For example, the checking unit checks whether the content of the advertisement violates copyright law or consumer protection law. This allows the advertisement to be confirmed as complying with the guidelines and legal policies. Some or all of the above-described processing by the checking unit may be performed using, or without, AI. For example, the checking unit can input the content of the advertisement into a generation AI, which then checks the guidelines and legal policies.

[0071] The analysis unit can analyze trends or consumer preferences in a specific market. Examples of specific markets include, but are not limited to, regional markets, industry markets, etc. The analysis unit, for example, prioritizes analysis of trends in a specific market. Examples of trends include, but are not limited to, changes in consumer behavior and trends in popular products. The analysis unit, for example, analyzes detailed data based on consumer preferences. Examples of consumer preferences include, but are not limited to, purchase history and survey results. The analysis unit, for example, integrates market trends and consumer preferences and adjusts the level of detail of the analysis. This allows advertisements to be created based on market trends and consumer preferences. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input consumer preference data into a generation AI, which then analyzes the data.

[0072] The providing unit can optimize the design or copy of the advertisement using the generation AI and provide an advertisement creative that attracts the user's interest. Examples of the generation AI include, but are not limited to, technologies such as deep learning and natural language generation. For example, the providing unit can optimize the visual design and layout of the advertisement using the generation AI and adjust it to attract the user's interest. Examples of the advertisement design include, but are not limited to, visual design and layout. For example, the providing unit can generate a catchphrase and description for the advertisement and provide it in a format that is easy to reach the user. Examples of the copy include, but are not limited to, catchphrases and descriptions. This makes it possible to provide an advertisement creative that attracts the user's interest. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide the advertisement creative generated by the generation AI to the user.

[0073] The reception unit can estimate the advertiser's emotions and change the information reception method based on the estimated advertiser's emotions. Examples of the advertiser's emotions include, but are not limited to, stress, relaxation, and urgency. For example, if the advertiser is feeling stressed, the reception unit can provide a simple interface and minimize input steps. Furthermore, if the advertiser is feeling relaxed, the reception unit can provide detailed input options and suggest a customizable input method. Furthermore, if the advertiser is in a hurry, the reception unit can prioritize voice input to enable quick information input. This makes it possible to provide an optimal information reception method according to the advertiser's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 reception unit may be performed using AI, or without AI. For example, the reception unit can input the advertiser's emotion data into the generation AI, which then performs emotion estimation.

[0074] The reception unit can analyze the advertiser's past advertising production history and select an information reception method. The advertiser's past advertising production history includes, for example, past campaign data, feedback, etc., but is not limited to these examples. The reception unit, for example, preferentially suggests input methods (voice, text, etc.) that the advertiser has used in the past. The reception unit can also analyze patterns of advertisements created by the advertiser in the past and suggest similar input methods. The reception unit can also provide an optimal input interface based on the advertiser's past feedback. This makes it possible to provide an optimal information reception method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the advertiser's past advertising production history data into the generation AI, and the generation AI can select an information reception method.

[0075] The reception unit can determine the priority of information to be received based on the purpose of the advertisement and the target audience. Examples of the purpose of the advertisement include, but are not limited to, increasing brand awareness and promoting product sales. For example, if the purpose of the advertisement is clear, the reception unit can prioritize information related to that purpose. Examples of the target audience include, but are not limited to, age groups, genders, and regions. For example, if the target audience is a specific age group, the reception unit can prioritize information related to that age group. The reception unit can also automatically classify and prioritize necessary information based on the purpose of the advertisement and the target audience. This allows the priority of information to be determined according to the purpose of the advertisement and the target audience. Some or all of the above-described processing by the reception unit can be performed using, for example, AI, or without AI. For example, the reception unit can input information about the purpose of the advertisement and the target audience into a generation AI, which can then prioritize the information.

[0076] The reception unit can select the reception means according to the advertiser's input method. Examples of the advertiser's input method include, but are not limited to, voice input, text input, and image input. For example, if the advertiser selects voice input, the reception unit can receive information using voice recognition technology. Furthermore, if the advertiser selects text input, the reception unit can also receive information using text analysis technology. Furthermore, if the advertiser selects image input, the reception unit can also receive information using image recognition technology. This makes it possible to provide the optimal reception means according to the advertiser's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the advertiser's input method data into a generation AI, which can then select the optimal reception means.

[0077] The reception unit can estimate the advertiser's emotions and determine the priority of information based on the estimated emotions. Examples of the advertiser's emotions include, but are not limited to, nervousness, relaxation, and urgency. For example, if the advertiser is nervous, the reception unit can prioritize receiving important information. Furthermore, if the advertiser is relaxed, the reception unit can prioritize receiving detailed information. Furthermore, if the advertiser is in a hurry, the reception unit can prioritize receiving information that can be input quickly. This allows the priority of information to be determined according to the advertiser's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the advertiser's emotion data into the generation AI, which can then prioritize the information.

[0078] The reception unit can preferentially accept highly relevant information based on the advertiser's geographical location information. Examples of the advertiser's geographical location information include, but are not limited to, GPS data and IP addresses. For example, if the advertiser is in a specific area, the reception unit can preferentially accept information related to that area. Furthermore, if the advertiser is traveling, the reception unit can also accept optimal information based on the advertiser's current location. Furthermore, the reception unit can preferentially accept related market data based on the advertiser's geographical location information. This allows highly relevant information based on the geographical location information to be preferentially accepted. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI. For example, the reception unit can input the advertiser's geographical location information data to the generation AI, which can then preferentially accept highly relevant information.

[0079] The reception unit may analyze the advertiser's social media activity and receive related information. The advertiser's social media activity may include, but is not limited to, the content of posts and reactions from followers. The reception unit may receive related advertising information, for example, based on information shared by the advertiser on social media. The reception unit may also analyze the advertiser's social media activity and receive related market data. The reception unit may also receive related information by referring to the activities of the advertiser's friends on social media. This allows reception of related information based on social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the advertiser's social media activity data into the generation AI, which may receive the related information.

[0080] The reception unit can customize the reception method based on the advertiser's past feedback. The advertiser's past feedback includes, but is not limited to, for example, survey results and reviews. The reception unit, for example, provides an optimal input interface based on the feedback provided by the advertiser in the past. The reception unit can also analyze the advertiser's past feedback and optimize the input procedure. The reception unit can also customize the reception method by reflecting the advertiser's feedback. This allows the reception method to be customized based on the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the advertiser's past feedback data into the generation AI, which can then customize the reception method.

[0081] The check unit can estimate the advertiser's emotions and adjust the check method for guidelines and legal policies based on the estimated advertiser's emotions. Examples of the advertiser's emotions include, but are not limited to, nervousness, relaxation, and urgency. For example, if the advertiser is nervous, the check unit can provide a simple and highly visible check method. Furthermore, if the advertiser is relaxed, the check unit can provide a detailed check method. Furthermore, if the advertiser is hurried, the check unit can provide a quick check method. This allows for providing an optimal check method according to the advertiser's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 check unit may be performed using, for example, AI, or without AI. For example, the check unit can input the advertiser's emotion data into the generation AI, which can then adjust the check method.

[0082] The checking unit can adjust the level of detail of the check based on the purpose of the advertisement or the target audience. Examples of the purpose of the advertisement include, but are not limited to, increasing brand awareness and promoting product sales. For example, if the purpose of the advertisement is clear, the checking unit prioritizes checking guidelines related to that purpose. Examples of the target audience include, but are not limited to, age groups, genders, and regions. For example, if the target audience is a specific age group, the checking unit prioritizes checking legal policies related to that age group. The checking unit can also automatically classify necessary check items and adjust the level of detail based on the purpose of the advertisement and the target audience. This allows the level of detail of the check to be adjusted according to the purpose of the advertisement and the target audience. Some or all of the above-described processing in the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input information about the purpose of the advertisement and the target audience into a generation AI, which can then adjust the level of detail of the check.

[0083] The checking unit can improve the accuracy of the check by referring to guidelines from different industries or regions. Guidelines from different industries or regions include, but are not limited to, industry standards and region-specific regulations. For example, the checking unit can refer to guidelines from different industries to check whether the content of an advertisement complies with the guidelines. The checking unit can also refer to legal policies from different regions to check whether the advertisement is legally problematic. The checking unit can also integrate guidelines from different industries or regions to improve the accuracy of the check. This allows the accuracy of the check to be improved by referring to guidelines from different industries or regions. Some or all of the above-described processing in the checking unit can be performed using, for example, AI, or can be performed without using AI. For example, the checking unit can input guideline data from different industries or regions into the generation AI, which can then improve the accuracy of the check.

[0084] The check unit can improve the efficiency of checks based on past check results. Past check results include, but are not limited to, checklists and feedback. For example, the check unit automatically suggests check items for similar advertisements based on the past check results. The check unit can also analyze the past check results and provide an efficient check method. The check unit can also determine the priority of checks by referring to the past check results. This can improve the efficiency of checks by referring to the past check results. Some or all of the above-mentioned processing in the check unit can be performed using, for example, AI, or can be performed without using AI. For example, the check unit can input past check result data into a generation AI, which can improve the efficiency of checks.

[0085] The check unit can estimate the advertiser's emotions and determine priorities based on the estimated advertiser emotions. Examples of the advertiser's emotions include, but are not limited to, nervousness, relaxation, and urgency. For example, if the advertiser is nervous, the check unit can prioritize checking important check items. Furthermore, if the advertiser is relaxed, the check unit can prioritize checking detailed check items. Furthermore, if the advertiser is in a hurry, the check unit can prioritize checking items that can be checked quickly. This allows the prioritization of checks to be determined according to the advertiser's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 check unit may be performed using, for example, AI, or without AI. For example, the check unit can input the advertiser's emotion data into the generation AI, which can then determine the priorities.

[0086] The checking unit can determine the priority of checks depending on the time of advertisement submission. The time of advertisement submission includes, but is not limited to, for example, the campaign start date and closing date. For example, if the advertisement submission time is approaching, the checking unit will check the advertisement preferentially. Furthermore, if the advertisement submission time is far away, the checking unit can also perform a more detailed check. Furthermore, the checking unit can automatically determine the priority of checks based on the time of advertisement submission. This allows the priority of checks to be determined based on the time of advertisement submission. Some or all of the above-mentioned processing in the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input advertisement submission time data into the generation AI, and the generation AI can determine the priority of checks.

[0087] The checking unit can adjust the order of checks depending on the relevance of the advertisement. Examples of the relevance of the advertisement include, but are not limited to, relevance to the target audience and relevance to the purpose of the advertisement. For example, if the relevance of the advertisement is high, the checking unit can check the advertisement preferentially. Furthermore, if the relevance of the advertisement is low, the checking unit can also perform a more detailed check. Furthermore, the checking unit can automatically adjust the order of checks based on the relevance of the advertisement. This allows the order of checks to be adjusted based on the relevance of the advertisement. Some or all of the above-described processing in the checking unit may be performed using, for example, AI, or may be performed without using AI. For example, the checking unit can input relevance data of the advertisement into a generating AI, which can then adjust the order of checks.

[0088] The check unit can adjust the level of detail of the check based on the advertiser's level of expertise. The advertiser's level of expertise includes, but is not limited to, past experience, qualifications, etc. For example, the check unit performs a detailed check when the advertiser's level of expertise is high. The check unit can also perform a simple check when the advertiser's level of expertise is low. The check unit can also automatically adjust the level of detail of the check based on the advertiser's level of expertise. This allows the level of detail of the check to be adjusted according to the advertiser's level of expertise. Some or all of the above-mentioned processing in the check unit may be performed using, for example, AI, or may be performed without using AI. For example, the check unit can input the advertiser's level of expertise data into the generation AI, which can then adjust the level of detail of the check.

[0089] The analysis unit can estimate the advertiser's emotions and change the analysis method of the market marketing data based on the estimated advertiser's emotions. Examples of the advertiser's emotions include, but are not limited to, relaxation, hurry, and excitement. For example, if the advertiser is relaxed, the analysis unit analyzes detailed market marketing data. Furthermore, if the advertiser is in a hurry, the analysis unit can prioritize analyzing data that can be analyzed quickly. Furthermore, if the advertiser is excited, the analysis unit can analyze visually stimulating data. This makes it possible to provide an optimal analysis method according to the advertiser's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the advertiser's emotion data into the generation AI, which can then change the analysis method.

[0090] The analysis unit can adjust the level of detail of the analysis based on trends in a specific market or consumer preferences. Specific markets include, but are not limited to, regional markets, industry markets, etc. The analysis unit, for example, prioritizes analysis of trends in a specific market. Trends include, but are not limited to, changes in consumer behavior and trends in popular products. The analysis unit analyzes detailed data based on, for example, consumer preferences. Consumer preferences include, but are not limited to, purchase history and survey results. The analysis unit, for example, integrates market trends and consumer preferences to adjust the level of detail of the analysis. This allows the level of detail of the analysis to be adjusted based on market trends and consumer preferences. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input consumer preference data into a generation AI, which then adjusts the level of detail of the analysis.

[0091] The analysis unit can integrate different data sources to improve the accuracy of the analysis. Examples of different data sources include, but are not limited to, social media data and sales data. For example, the analysis unit can integrate different data sources to improve the accuracy of the analysis. The analysis unit can also integrate different market data to perform detailed analysis. The analysis unit can also integrate different consumer data to improve the accuracy of the analysis. Thus, by integrating different data sources, the accuracy of the analysis can be improved. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input different data sources into a generation AI, which then performs data integration.

[0092] The analysis unit can improve the efficiency of analysis based on past analysis results. Past analysis results include, but are not limited to, past reports and feedback. For example, the analysis unit prioritizes analysis of similar market data based on past analysis results. The analysis unit can also analyze past analysis results and provide an efficient analysis method. The analysis unit can also determine the priority of analysis by referring to past analysis results. This can improve the efficiency of analysis by referring to past analysis results. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input past analysis result data into a generation AI, which can improve the efficiency of analysis.

[0093] The analysis unit can estimate the advertiser's emotions and determine priorities based on the estimated advertiser emotions. Examples of advertiser emotions include, but are not limited to, nervousness, relaxation, and urgency. For example, if the advertiser is nervous, the analysis unit can prioritize analyzing important market data. Furthermore, if the advertiser is relaxed, the analysis unit can prioritize analyzing detailed market data. Furthermore, if the advertiser is in a hurry, the analysis unit can prioritize analyzing data that can be analyzed quickly. This allows the analysis priorities to be determined according to the advertiser's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the advertiser's emotion data into the generation AI, which can then determine the priorities.

[0094] The analysis unit can determine the priority of analysis based on the time of advertisement submission. The time of advertisement submission includes, but is not limited to, for example, the campaign start date and closing date. For example, if the advertisement submission time is approaching, the analysis unit prioritizes analyzing market data related to the advertisement. Furthermore, if the advertisement submission time is far away, the analysis unit can also analyze detailed market data. Furthermore, the analysis unit can automatically determine the priority of analysis based on the time of advertisement submission. This makes it possible to determine the priority of analysis based on the time of advertisement submission. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input advertisement submission time data into a generation AI, which can then determine the priority of analysis.

[0095] The analysis unit can adjust the order of analysis depending on the relevance of the advertisement. Examples of the relevance of the advertisement include, but are not limited to, relevance to the target audience and relevance to the purpose of the advertisement. For example, if the relevance of the advertisement is high, the analysis unit prioritizes analyzing market data related to the advertisement. Furthermore, if the relevance of the advertisement is low, the analysis unit can also analyze detailed market data. Furthermore, the analysis unit can automatically adjust the order of analysis based on the relevance of the advertisement. This allows the order of analysis based on the relevance of the advertisement to be adjusted. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the relevance data of the advertisement to a generation AI, which can then adjust the order of analysis.

[0096] The analysis unit can adjust the level of detail of the analysis based on the advertiser's level of expertise. Examples of the advertiser's level of expertise include, but are not limited to, past experience and qualifications. For example, the analysis unit analyzes detailed market data when the advertiser's level of expertise is high. Alternatively, the analysis unit can analyze simple market data when the advertiser's level of expertise is low. Alternatively, the analysis unit can automatically adjust the level of detail of the analysis based on the advertiser's level of expertise. This allows the level of detail of the analysis to be adjusted according to the advertiser's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the advertiser's level of expertise data into the generation AI, which can then adjust the level of detail of the analysis.

[0097] The providing unit can estimate the advertiser's emotions and change the method of providing the advertising creative based on the estimated advertiser's emotions. Examples of the advertiser's emotions include, but are not limited to, relaxation, hurry, and excitement. For example, if the advertiser is relaxed, the providing unit can provide a detailed advertising creative. Furthermore, if the advertiser is in a hurry, the providing unit can prioritize providing advertising creative that can be provided quickly. Furthermore, if the advertiser is excited, the providing unit can provide a visually stimulating advertising creative. This makes it possible to provide an optimal advertising creative providing method according to the advertiser's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 providing unit may be performed using, for example, AI, or without AI. For example, the providing unit can input the advertiser's emotion data into the generation AI, which can then change the presentation method.

[0098] The providing unit may adjust the level of detail of the provision based on the purpose of the advertisement or the target audience. Examples of the purpose of the advertisement include, but are not limited to, increasing brand awareness and promoting product sales. For example, if the purpose of the advertisement is clear, the providing unit may preferentially provide advertising creatives related to the purpose. Examples of the target audience include, but are not limited to, age groups, genders, and regions. For example, if the target audience is a specific age group, the providing unit may preferentially provide advertising creatives related to that age group. The providing unit may also automatically adjust the level of detail of the provision based on the purpose of the advertisement and the target audience. This allows the level of detail of the provision to be adjusted according to the purpose of the advertisement and the target audience. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input information about the purpose of the advertisement and the target audience into a generation AI, which may then adjust the level of detail of the provision.

[0099] The providing unit can generate different design or copy variations to improve the accuracy of the offering. Examples of different design or copy variations include, but are not limited to, color variations and differences in writing style. For example, the providing unit can generate advertising creatives with different designs and provide the optimal one. The providing unit can also generate different copy variations and provide the optimal one. The providing unit can also integrate design and copy variations to improve the accuracy of the offering. This allows the accuracy of the offering to be improved by generating different design and copy variations. Some or all of the above-described processing in the providing unit can be performed, for example, using AI or without AI. For example, the providing unit can input data on different design and copy variations into a generation AI, which then generates the variations.

[0100] The provision unit can improve the efficiency of provision based on past provision results. Past provision results include, but are not limited to, campaign results, user feedback, etc. The provision unit, for example, preferentially provides similar advertising creatives based on past provision results. The provision unit can also analyze past provision results and provide an efficient provision method. The provision unit can also determine provision priorities by referring to past provision results. This can improve the efficiency of provision by referring to past provision results. Some or all of the above-described processing in the provision unit can be performed using, for example, AI, or can be performed without using AI. For example, the provision unit can input past provision result data into a generation AI, which can improve the efficiency of provision.

[0101] The providing unit can estimate the advertiser's emotions and prioritize ad creatives based on the estimated advertiser emotions. Examples of the advertiser's emotions include, but are not limited to, nervousness, relaxation, and urgency. For example, if the advertiser is nervous, the providing unit can prioritize providing important ad creatives. Furthermore, if the advertiser is relaxed, the providing unit can prioritize providing detailed ad creatives. Furthermore, if the advertiser is in a hurry, the providing unit can prioritize providing ad creatives that can be provided quickly. This allows the prioritization of ad creatives according to the advertiser's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, 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 providing unit may be performed using AI, or may be performed without AI. For example, the providing unit can input the advertiser's emotion data into the generation AI, which can then prioritize the ad creatives.

[0102] The provision unit can determine the priority of provision based on the time of advertisement submission. The time of advertisement submission includes, but is not limited to, for example, a campaign start date, a closing date, etc. For example, if the advertisement submission time is approaching, the provision unit can provide the advertisement creative with priority. Furthermore, if the advertisement submission time is far away, the provision unit can also provide detailed advertisement creative. Furthermore, the provision unit can automatically determine the priority of provision based on the time of advertisement submission. In this way, the priority of provision can be determined based on the time of advertisement submission. Some or all of the above-described processing in the provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the provision unit can input advertisement submission time data into a generation AI, and the generation AI can determine the priority of provision.

[0103] The providing unit can adjust the order of provision depending on the relevance of the advertisements. Examples of the relevance of the advertisements include, but are not limited to, relevance to the target audience and relevance to the purpose of the advertisement. For example, if the relevance of the advertisement is high, the providing unit can provide the advertisement creative with priority. Furthermore, if the relevance of the advertisement is low, the providing unit can also provide more detailed advertisement creative. Furthermore, the providing unit can automatically adjust the order of provision based on the relevance of the advertisements. This allows the order of provision based on the relevance of the advertisements to be adjusted. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input relevance data of the advertisements to a generation AI, which can then adjust the order of provision.

[0104] The providing unit can adjust the level of detail of the provision based on the advertiser's expertise level. The advertiser's expertise level includes, but is not limited to, past experience, qualifications, etc. For example, the providing unit can provide detailed advertising creatives when the advertiser's expertise level is high. Furthermore, the providing unit can also provide simple advertising creatives when the advertiser's expertise level is low. Furthermore, the providing unit can automatically adjust the level of detail of the provision based on the advertiser's expertise level. This allows the level of detail of the provision to be adjusted according to the advertiser's expertise level. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input the advertiser's expertise level data to the generating AI, which can then adjust the level of detail of the provision. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, check unit, analysis unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives information from an advertiser. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks guidelines and legal policies. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes market marketing data. The provision unit is realized, for example, by the output device 40 of the smart device 14 and provides advertising creatives. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, check unit, analysis unit, and provision unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives information from the advertiser. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks guidelines and legal policies. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes market marketing data. The provision unit is realized, for example, by the speaker 240 of the smart glasses 214 and provides advertising creatives. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, check unit, analysis unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives information from the advertiser. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks guidelines and legal policies. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes market marketing data. The provision unit is realized, for example, by the display 343 of the headset type terminal 314 and provides advertising creatives. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, check unit, analysis unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives information from the advertiser. The check unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks guidelines and legal policies. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes market marketing data. The provision unit is realized, for example, by the speaker 240 of the robot 414 and provides advertising creatives.

[0105] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0106] The reception unit can analyze the success rate of the advertiser's past advertising campaigns and preferentially receive elements of successful campaigns. For example, it can extract elements of advertisements that have recorded high click-through rates in the past and preferentially receive information containing similar elements. It can also analyze elements of advertisements that have achieved high conversion rates in past campaigns and preferentially receive information containing these elements. It can also extract elements of advertisements that have received high ratings from users in past campaigns and preferentially receive information containing these elements. This makes it possible to provide an optimal information reception method based on past success factors.

[0107] The checking unit can estimate the advertiser's emotions and adjust the method of checking guidelines and legal policies based on the estimated emotions of the advertiser. For example, if the advertiser is nervous, a simple and highly visible checking method can be provided. If the advertiser is relaxed, a detailed checking method can be provided. Furthermore, if the advertiser is in a hurry, a quick checking method can be provided. This makes it possible to provide the optimal checking method according to the advertiser's emotions.

[0108] The analysis unit can estimate the advertiser's emotions and change the analysis method of the market marketing data based on the estimated emotions of the advertiser. For example, if the advertiser is relaxed, detailed market marketing data can be analyzed. If the advertiser is in a hurry, data that can be analyzed quickly can be prioritized for analysis. Furthermore, if the advertiser is excited, visually stimulating data can be analyzed. This makes it possible to provide an optimal analysis method according to the advertiser's emotions.

[0109] The providing unit can estimate the advertiser's emotions and change the method of providing advertising creatives based on the estimated emotions of the advertiser. For example, if the advertiser is relaxed, detailed advertising creatives can be provided. Also, if the advertiser is in a hurry, advertising creatives that can be provided quickly can be provided preferentially. Furthermore, if the advertiser is excited, visually stimulating advertising creatives can be provided. In this way, it is possible to provide an optimal advertising creative providing method according to the advertiser's emotions.

[0110] The providing unit can estimate the advertiser's emotions and determine the priority of advertising creatives based on the estimated emotions of the advertiser. For example, if the advertiser is nervous, important advertising creatives can be provided preferentially. Also, if the advertiser is relaxed, detailed advertising creatives can be provided preferentially. Furthermore, if the advertiser is in a hurry, advertising creatives that can be provided quickly can be provided preferentially. In this way, the priority of advertising creatives can be determined according to the advertiser's emotions.

[0111] The reception unit can preferentially receive highly relevant information based on the geographical location information of the advertiser. For example, if the advertiser is in a specific area, it can preferentially receive information related to that area. Also, if the advertiser is traveling, it can also receive optimal information based on the current location. Furthermore, it can also preferentially receive related market data based on the geographical location information of the advertiser. This makes it possible to preferentially receive highly relevant information based on the geographical location information.

[0112] The checking unit can improve the accuracy of the check by referring to guidelines from different industries or regions. For example, it can refer to guidelines from different industries to check whether the content of an advertisement complies with them. It can also refer to legal policies from different regions to check whether the advertisement is legally problematic. Furthermore, it can integrate guidelines from different industries or regions to improve the accuracy of the check. This allows the accuracy of the check to be improved by referring to guidelines from different industries or regions.

[0113] The analysis unit can improve the accuracy of the analysis by integrating different data sources. For example, different data sources can be integrated to improve the accuracy of the analysis. In addition, different market data can be integrated to perform detailed analysis. Furthermore, different consumer data can be integrated to improve the accuracy of the analysis. In this way, the accuracy of the analysis can be improved by integrating different data sources.

[0114] The providing unit can improve the accuracy of the provision by generating different design or copy variations. For example, it can generate advertising creatives with different designs and provide the optimal one. It can also generate different copy variations and provide the optimal one. Furthermore, it can integrate design and copy variations to improve the accuracy of the provision. This makes it possible to improve the accuracy of the provision by generating different design and copy variations.

[0115] The provision unit can improve the efficiency of provision based on past provision results. For example, similar advertising creatives can be provided preferentially based on past provision results. Also, the provision unit can analyze past provision results and provide an efficient provision method. Furthermore, the provision priority can be determined by referring to past provision results. In this way, the efficiency of provision can be improved by referring to past provision results.

[0116] The processing flow of the second embodiment will be briefly explained below.

[0117] Step 1: The reception unit receives information from the advertiser. This information includes the purpose of the advertisement, the target audience, and the budget. The reception unit receives the information entered by the advertiser in digital form, and can also receive voice input and image input. For example, the advertiser may explain the purpose of the advertisement by voice, and this information is converted into text data. Step 2: The Checking Department checks the guidelines and legal policies based on the information received by the Reception Department. The Checking Department refers to the guidelines of the manufacturer and the advertising destination and verifies whether the advertisement complies with these guidelines. They also refer to legal policies to verify that the advertisement is legally sound. For example, they check whether the content of the advertisement violates copyright law or consumer protection law. Step 3: The analysis unit analyzes the market marketing data based on the information checked by the checking unit. The analysis unit analyzes trends and consumer preferences in specific markets and predicts how the advertisement will be received by the target audience based on consumer survey data and sales data. For example, it analyzes consumer purchasing history and survey results to predict the effectiveness of the advertisement. Step 4: The provision unit provides ad creatives based on the data analyzed by the analysis unit. The provision unit uses generation AI to optimize the ad design and copy and provide ad creatives that attract users' attention. For example, the generation AI optimizes the visual design and layout of the ad and adjusts it to attract users' attention. The generation AI also generates ad catchphrases and descriptions and provides them in a format that is easy for users to understand.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0122] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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).

[0175] 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.

[0176] 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."

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] [Explanation of symbols]

[0190] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives information from advertisers; a checking unit that checks guidelines or legal policies based on the information received by the receiving unit; an analysis unit that analyzes market marketing data based on the information checked by the checking unit; a providing unit that provides advertising creatives based on the data analyzed by the analyzing unit. A system characterized by:

2. The reception unit Accepts advertising objectives, target audience, and budget information 2. The system of claim 1.

3. The checking unit Check the manufacturer's or publisher's guidelines and legal policies 2. The system of claim 1.

4. The analysis unit Analyze trends or consumer preferences in a particular market 2. The system of claim 1.

5. The providing unit Generative AI optimizes ad design or copy to provide engaging ad creatives 2. The system of claim 1.

6. The reception unit Estimate the advertiser's emotions and change the way information is received based on the estimated emotions of the advertiser 2. The system of claim 1.

7. The reception unit Analyze the advertiser's past advertising history and select the method of receiving information 2. The system of claim 1.

8. The reception unit Prioritize the information you accept based on your advertising objectives or target audience 2. The system of claim 1.

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