system

Abstract

The system according to this embodiment aims to automate and efficiently carry out everything from advertising planning to delivery and effectiveness measurement. [Solution] The system according to the embodiment comprises a purpose confirmation unit, a planning unit, a distribution unit, and a measurement unit. The purpose confirmation unit confirms the user's advertising distribution purpose. The planning unit creates a distribution plan based on the purpose confirmed by the purpose confirmation unit. The distribution unit distributes advertisements based on the distribution plan created by the planning unit. The measurement unit measures the effectiveness of the advertisements distributed by the distribution unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there are problems such as the burden of human resources, lack of consistency, and speed limitations in the process from advertising distribution planning to distribution and effect measurement.

[0005] The system according to the embodiment aims to automate and efficiently perform the process from advertising distribution planning to distribution and effect measurement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a purpose confirmation unit, a planning unit, a distribution unit, and a measurement unit. The purpose confirmation unit confirms the user's advertising distribution purpose. The planning unit creates a distribution plan based on the purpose confirmed by the purpose confirmation unit. The distribution unit distributes advertisements based on the distribution plan created by the planning unit. The measurement unit measures the effectiveness of the advertisements distributed by the distribution unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate and efficiently perform everything from advertising delivery planning to delivery and effectiveness measurement. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI ​​agent for ad delivery and performance measurement, according to an embodiment of the present invention, is a system that automates everything from ad delivery planning to delivery and performance measurement. This system was proposed to solve the problems of conventional ad delivery, such as the burden on human resources, lack of consistency, and limitations in speed. The AI ​​agent for ad delivery and performance measurement consists of the following three steps. First, in objective confirmation and delivery planning, the AI ​​confirms the user's ad delivery objective and creates an optimal delivery plan using LINE Yahoo data. Next, in ad delivery, the AI ​​sets up delivery from the LINE Yahoo ad management screen based on the delivery plan and executes the delivery. Finally, in performance measurement, after delivery, the AI ​​uses the delivery results and LINE Yahoo data to support performance measurement and reporting. For example, when the AI ​​agent for ad delivery confirms the user's ad delivery objective, the AI ​​evaluates whether achieving the set objective is necessary now using LINE Yahoo data, and if corrections are needed, it makes corrections while interacting with the user. The AI ​​also considers the elements necessary to achieve the set objective and clarifies the elements that can be supported by ad delivery. Furthermore, the AI ​​selects and proposes the most suitable LINE Yahoo! ads to achieve the listed advertising objectives. Based on LINE Yahoo! data, it proposes advertising content, delivery period, effectiveness measurement methods, and target audiences that are a good fit for achieving advertising objectives, and creates a delivery plan. As a result, the AI ​​agent supporting advertising delivery and effectiveness measurement automates everything from confirming advertising objectives to delivery and effectiveness measurement, reducing the burden on human resources and improving consistency and accuracy.

[0029] The AI ​​agent for ad delivery and performance measurement according to this embodiment comprises a purpose confirmation unit, a planning unit, a delivery unit, and a measurement unit. The purpose confirmation unit confirms the user's ad delivery purpose. For example, the purpose confirmation unit receives the ad delivery purpose set by the user as input and evaluates whether the purpose is appropriate. The purpose confirmation unit can use AI to evaluate the validity of the purpose based on LINE Yahoo data. For example, the purpose confirmation unit analyzes past ad delivery data and market trends to determine whether the set purpose is appropriate for the current situation. The planning unit creates a delivery plan based on the purpose confirmed by the purpose confirmation unit. The planning unit can use AI to create an optimal delivery plan based on LINE Yahoo data. For example, the planning unit selects target audiences, sets delivery schedules, and selects ad formats. The planning unit confirms the elements necessary to achieve the purpose and clarifies the elements that can be supported by ad delivery. The delivery unit delivers ads based on the delivery plan created by the planning unit. The delivery unit can use AI to configure delivery settings from the LINE Yahoo ad management screen and deliver ads. For example, the distribution unit automatically distributes advertisements according to the distribution schedule. The distribution unit includes a settings unit for configuring the distribution settings of advertisements. The measurement unit measures the effectiveness of advertisements distributed by the distribution unit. The measurement unit can use AI to measure effectiveness based on distribution results and LINE Yahoo data. For example, the measurement unit evaluates the effectiveness of advertisements using metrics such as click-through rate, conversion rate, and reach. The measurement unit includes a reporting unit that reports the effectiveness measurement results. As a result, the AI ​​agent supporting ad distribution and effectiveness measurement according to this embodiment can automate everything from confirming the purpose of ad distribution to distribution and effectiveness measurement, reducing the burden on human resources and improving consistency and accuracy.

[0030] The Objective Verification Unit verifies the user's advertising objectives. Specifically, it receives the user's set advertising objectives as input and evaluates whether those objectives are appropriate. The Objective Verification Unit uses AI to analyze past advertising data and market trends to determine if the set objectives are suitable for the current situation. For example, if a user sets the objective to "increase awareness of a new product," the Objective Verification Unit refers to data from past advertising campaigns with similar objectives and analyzes their effectiveness. Furthermore, it considers current market trends and the actions of competitors to evaluate whether the set objectives are realistic. The Objective Verification Unit provides feedback to the user regarding the validity of the objectives and can suggest revisions as needed. For example, if the objective is overly ambitious, it will advise setting more realistic goals. The Objective Verification Unit also collects information on the user's business objectives and target audience and evaluates the objectives based on this information. In this way, the Objective Verification Unit can ensure that the user's advertising objectives are clear and appropriate, thereby increasing the success rate of advertising.

[0031] The Planning Department creates a delivery plan based on the objectives confirmed by the Objective Confirmation Department. Specifically, it uses AI to analyze past advertising delivery data and market trends to create the optimal delivery plan. For example, to select a target audience, the Planning Department identifies the most effective audience segment based on the user's business goals and information about the target audience. Furthermore, it sets a delivery schedule to optimize the timing of ad delivery. For example, by delivering ads at specific times or days of the week, it is possible to effectively reach the target audience. The Planning Department also selects the ad format, choosing the most suitable ad format for the user's objectives. For example, it selects the most suitable ad format from various ad formats such as banner ads, video ads, and native ads. The Planning Department comprehensively considers these elements, confirms the elements necessary to achieve the objectives, and clarifies the elements that can be supported by ad delivery. As a result, the Planning Department can create the optimal delivery plan for the user's advertising objectives and maximize the effectiveness of ad delivery.

[0032] The distribution department delivers ads based on the distribution plan created by the planning department. Specifically, it uses AI to configure ad delivery settings and automatically delivers ads. For example, the distribution department delivers ads according to the distribution schedule, effectively reaching the target audience. The distribution department has a settings department for configuring ad delivery settings, allowing for detailed settings of ad delivery timing and destinations. For example, it can set up ad delivery to specific regions or devices. The distribution department monitors ad delivery status in real time and can adjust delivery settings as needed. For example, if the click-through rate or conversion rate of an ad is low, it will change the delivery settings to improve effectiveness. The distribution department also records ad delivery results and collects data for later performance measurement. As a result, the distribution department can efficiently and effectively deliver ads based on the distribution plan created by the planning department, maximizing the effectiveness of ad delivery.

[0033] The measurement unit measures the effectiveness of advertisements delivered by the delivery unit. Specifically, it uses AI to measure effectiveness based on delivery results and past ad delivery data. For example, the measurement unit evaluates the effectiveness of advertisements using metrics such as click-through rate, conversion rate, and reach. The measurement unit analyzes the ad delivery results in detail and identifies which elements contribute to the effectiveness of the advertisement. For example, it evaluates whether a particular target audience or ad format is effective. The measurement unit is equipped with a reporting unit that reports the effectiveness measurement results, providing users with easy-to-understand reports. For example, it uses graphs and charts to visually show the effectiveness of the advertisement. In addition, the measurement unit can suggest improvements for the next ad delivery based on the effectiveness measurement results. For example, it may suggest strengthening ad delivery to a specific target audience or changing the ad format. In this way, the measurement unit can accurately measure the effectiveness of ad delivery and clearly identify areas for improvement for the next ad delivery, thereby continuously improving the effectiveness of ad delivery.

[0034] The Planning Department includes an Objective Evaluation Department that uses LINE & Yahoo data to assess the validity of objective settings. For example, the Planning Department evaluates whether the set advertising delivery objectives are appropriate for the current situation based on LINE & Yahoo data. The Objective Evaluation Department can use AI to analyze past advertising delivery data and market trends to determine the validity of the objectives. For example, the Objective Evaluation Department can refer to past successful and unsuccessful advertising campaign examples to evaluate whether the set objectives are achievable. By evaluating the validity of the objective settings in this way, the effectiveness of advertising delivery can be maximized.

[0035] The planning department includes an element verification department that confirms the elements essential for achieving the objective. For example, the planning department confirms the elements necessary to achieve the advertising delivery objective. The element verification department can use AI to confirm the characteristics of the target audience and the content of the advertising message. For example, the element verification department analyzes the behavioral data and attribute data of the target audience to identify the elements necessary for advertising delivery. By confirming the elements necessary to achieve the objective, effective advertising delivery can be realized.

[0036] The Planning Department includes an Advertising Proposal Department that proposes the most suitable advertising products. For example, the Planning Department proposes the most suitable advertising products to achieve advertising delivery objectives. The Advertising Proposal Department can use AI to select advertising formats, creatives, and delivery channels. For instance, the Advertising Proposal Department analyzes past advertising delivery data and market trends to propose the most suitable advertising products. This maximizes the effectiveness of advertising delivery by proposing the optimal advertising products.

[0037] The Planning Department includes a Target Proposal Department that proposes content and targets for ad delivery. For example, the Planning Department proposes ad delivery content and target audiences. The Target Proposal Department uses AI to analyze the characteristics of ad messages and target audiences, and can propose optimal delivery content and targets. For instance, the Target Proposal Department selects ad delivery content and targets based on past ad delivery data and user behavior data. This allows for the maximization of ad delivery effectiveness by proposing appropriate content and targets.

[0038] The delivery unit includes a settings unit for configuring ad delivery. The delivery unit handles tasks such as ad delivery schedules, target audience selection, and ad format settings. The settings unit can automate ad delivery settings using AI. For example, the settings unit automatically delivers ads according to the delivery schedule. This allows for automated ad delivery by configuring ad delivery settings.

[0039] The measurement unit includes a reporting unit that reports the results of the effectiveness measurement. For example, the measurement unit measures the effectiveness of ad delivery and reports the results. The reporting unit can automatically report the effectiveness measurement results using AI. For example, the reporting unit evaluates the effectiveness of ads using metrics such as click-through rate, conversion rate, and reach, and reports the results. This allows for improvement of ad delivery by reporting the effectiveness measurement results.

[0040] The objective confirmation unit can analyze a user's past ad delivery history and select the optimal objective confirmation method. For example, the objective confirmation unit can automatically display ad delivery objectives that the user has frequently set in the past as candidates. Furthermore, the objective confirmation unit can prioritize suggesting confirmation methods (voice, text, etc.) that the user has used in the past. In addition, the objective confirmation unit can predict and suggest ad delivery objectives to be used during specific time periods based on the user's past ad delivery history. This allows the system to provide the optimal objective confirmation method by analyzing the user's past ad delivery history.

[0041] The objective verification unit can filter ad delivery objectives based on the user's current business situation and market trends. For example, the objective verification unit can analyze the user's current business situation and propose the most suitable ad delivery objective. Furthermore, the objective verification unit can consider market trends and propose ad delivery objectives that align with current trends. In addition, the objective verification unit can propose appropriate ad delivery objectives according to the user's business growth stage. This allows for the proposal of the most suitable ad delivery objective by filtering based on the user's business situation and market trends.

[0042] The objective verification unit can prioritize identifying highly relevant objectives when verifying advertising delivery objectives, taking into account the user's geographical location information. For example, the objective verification unit can prioritize identifying regionally relevant advertising delivery objectives based on the user's current location. Furthermore, the objective verification unit can analyze the user's past location information and suggest highly relevant advertising delivery objectives. In addition, the objective verification unit can prioritize identifying the optimal advertising delivery objective based on the user's business location. This allows for the prioritization of highly relevant advertising delivery objectives by considering the user's geographical location information.

[0043] The objective verification unit can analyze the user's social media activity and identify relevant objectives when verifying advertising delivery objectives. For example, the objective verification unit can analyze the user's social media activity and suggest relevant advertising delivery objectives. Furthermore, the objective verification unit can identify highly relevant advertising delivery objectives based on the activity of the user's followers and friends. In addition, the objective verification unit can analyze the content of the user's social media posts and suggest the most suitable advertising delivery objectives. This allows for the identification of relevant advertising delivery objectives by analyzing the user's social media activity.

[0044] The planning department can adjust the level of detail in a delivery plan based on the importance of the advertisements. For example, the planning department can create detailed delivery plans for high-importance advertisements, and simpler plans for lower-importance advertisements. Furthermore, the planning department can adjust resource allocation according to the importance of the advertisements to create the optimal delivery plan. This allows for effective ad delivery by adjusting the level of detail in the plan based on the importance of the advertisements.

[0045] The planning department can apply different planning algorithms depending on the ad category when creating a delivery plan. For example, if the ad category is fashion, the planning department can apply a visually appealing planning algorithm. If the ad category is technology, the planning department can apply a planning algorithm that emphasizes technical details. Furthermore, if the ad category is food, the planning department can apply a planning algorithm that emphasizes sensory elements. By applying different planning algorithms according to the ad category, effective ad delivery can be achieved.

[0046] The planning department can prioritize advertising plans based on their submission dates when creating delivery plans. For example, the planning department can prioritize creating plans for ads with upcoming submission dates, and postpone creating plans for ads with later submission dates. Furthermore, the planning department can adjust resource allocation according to submission dates to create the optimal plan. This allows for effective ad delivery by prioritizing plans based on ad submission dates.

[0047] The planning department can adjust the order of advertising plans based on their relevance when creating delivery plans. For example, the planning department can prioritize creating plans for highly relevant ads. Conversely, it can postpone creating plans for less relevant ads. Furthermore, the planning department can adjust resource allocation according to the relevance of the ads to create the optimal plan. This allows for effective ad delivery by adjusting the order of plans based on ad relevance.

[0048] The ad delivery department can analyze a user's past delivery history to select the optimal delivery method when delivering ads. For example, the department can select the optimal delivery method based on the delivery methods the user has used in the past. Furthermore, the department can suggest effective delivery methods based on the user's past delivery history. In addition, the department can analyze the user's past delivery history to select the most efficient delivery method. This allows the department to provide the optimal delivery method by analyzing the user's past delivery history.

[0049] The ad delivery department can customize the delivery method based on the user's current business situation when delivering ads. For example, the delivery department can analyze the user's current business situation and propose the optimal delivery method. Furthermore, the delivery department can select an appropriate delivery method according to the user's business growth stage. In addition, the delivery department can provide customized delivery methods based on the user's business needs. This allows for optimal ad delivery by customizing the delivery method based on the user's business situation.

[0050] The ad delivery unit can select the optimal delivery method when delivering ads, taking into account the user's geographical location. For example, the delivery unit can prioritize delivering ads relevant to the user's location based on their current location. Furthermore, the delivery unit can analyze the user's past location data to suggest highly relevant ads. In addition, the delivery unit can select the optimal delivery method based on the user's business location. This allows for the provision of the most effective ad delivery method by considering the user's geographical location.

[0051] The ad delivery department can analyze users' social media activity and suggest appropriate delivery methods when delivering ads. For example, it can analyze users' social media activity and suggest the most suitable delivery method. Furthermore, it can deliver highly relevant ads based on the activity of users' followers and friends. In addition, it can analyze the content of users' social media posts and suggest the most suitable delivery method. This allows for the provision of optimal ad delivery methods by analyzing users' social media activity.

[0052] The measurement unit can optimize the measurement algorithm by referring to past measurement data during effect measurement. For example, the measurement unit can select the optimal measurement algorithm based on past measurement data. Furthermore, the measurement unit can propose effective measurement methods based on past measurement data. In addition, the measurement unit can analyze past measurement data and select the most efficient measurement algorithm. Thus, by referring to past measurement data, it can provide the optimal measurement algorithm.

[0053] The measurement unit can apply different measurement methods to each advertising category when measuring effectiveness. For example, if the advertising category is fashion, the measurement unit can apply a measurement method that emphasizes visual elements. If the advertising category is technology, the measurement unit can apply a measurement method that emphasizes technical details. Furthermore, if the advertising category is food, the measurement unit can apply a measurement method that emphasizes sensory elements. By applying different measurement methods to each advertising category, effective measurement can be achieved.

[0054] The measurement unit can weight the measurement data based on the timing of ad submissions when measuring effectiveness. For example, the measurement unit can give higher weight to ads that are submitted sooner, and lower weight to ads that are submitted far in the future. Furthermore, the measurement unit can adjust the weighting of the measurement data according to the submission timing to perform optimal measurements. This enables effective measurement by weighting the measurement data based on the timing of ad submissions.

[0055] The measurement unit can perform measurements by referring to relevant market data for advertising during effectiveness measurement. For example, the measurement unit can select the optimal measurement method based on relevant market data. Furthermore, the measurement unit can propose effective measurement methods based on relevant market data. In addition, the measurement unit can analyze relevant market data and select the most efficient measurement method. This allows the system to provide the optimal measurement method by referring to relevant market data for advertising.

[0056] The objective evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during the objective evaluation. For example, the objective evaluation unit can select the optimal evaluation algorithm based on past evaluation data. Furthermore, the objective evaluation unit can propose an effective evaluation method based on past evaluation data. In addition, the objective evaluation unit can analyze past evaluation data and select the most efficient evaluation algorithm. Thus, by referring to past evaluation data, it can provide the optimal evaluation algorithm.

[0057] The objective evaluation unit can weight evaluation data based on the timing of ad submissions during the objective evaluation process. For example, the objective evaluation unit can give higher weight to ads with upcoming submission dates and lower weight to ads with later submission dates. Furthermore, the objective evaluation unit can adjust the weighting of evaluation data according to the submission dates to perform optimal evaluations. This enables effective evaluation by weighting evaluation data based on the timing of ad submissions.

[0058] The element verification unit can optimize the verification algorithm by referring to past verification data during element verification. For example, the element verification unit can select the optimal verification algorithm based on past verification data. Furthermore, the element verification unit can propose effective verification methods based on past verification data. In addition, the element verification unit can analyze past verification data and select the most efficient verification algorithm. Thus, by referring to past verification data, it can provide the optimal verification algorithm.

[0059] The element verification unit can weight verification data based on the ad submission date during element verification. For example, it can give higher weight to ads with upcoming submission dates and lower weight to ads with later submission dates. Furthermore, the element verification unit can adjust the weighting of verification data according to the submission date to perform optimal verification. This enables effective verification by weighting verification data based on the ad submission date.

[0060] The advertising proposal department can optimize its proposal algorithm by referring to past proposal data when creating advertising proposals. For example, the advertising proposal department can select the optimal proposal algorithm based on past proposal data. Furthermore, the advertising proposal department can propose effective proposal methods based on past proposal data. In addition, the advertising proposal department can analyze past proposal data and select the most efficient proposal algorithm. This allows the department to provide the optimal proposal algorithm by referring to past proposal data.

[0061] The advertising proposal department can weight proposal data based on the submission date of the advertisement. For example, the department can give higher weight to advertisements with an upcoming submission date and lower weight to advertisements with a distant submission date. Furthermore, the department can adjust the weighting of proposal data according to the submission date to make the most optimal proposal. This allows for effective proposals by weighting proposal data based on the advertisement submission date.

[0062] The target proposal unit can optimize the proposal algorithm by referring to past proposal data when proposing a target. For example, the target proposal unit can select the optimal proposal algorithm based on past proposal data. Furthermore, the target proposal unit can propose effective proposal methods based on past proposal data. In addition, the target proposal unit can analyze past proposal data and select the most efficient proposal algorithm. Thus, by referring to past proposal data, it can provide the optimal proposal algorithm.

[0063] The target proposal department can weight proposal data based on the ad submission timing when making target proposals. For example, the department can give higher weight to ads with upcoming submission dates and lower weight to ads with later submission dates. Furthermore, the department can adjust the weighting of proposal data according to the submission timing to make optimal proposals. This allows for effective proposals by weighting proposal data based on the ad submission timing.

[0064] The settings unit can optimize the settings algorithm by referring to past settings data when configuring ad delivery. For example, the settings unit can select the optimal settings algorithm based on past settings data. Furthermore, the settings unit can suggest effective settings methods based on past settings data. In addition, the settings unit can analyze past settings data and select the most efficient settings algorithm. This allows the system to provide the optimal settings algorithm by referring to past settings data.

[0065] The settings unit can weight the setting data based on the ad submission timing when configuring ad delivery. For example, it can assign higher weights to ads with upcoming submission dates and lower weights to ads with later submission dates. Furthermore, the settings unit can adjust the weighting of the setting data according to the submission timing to achieve optimal settings. This allows for effective settings by weighting the setting data based on the ad submission timing.

[0066] The reporting unit can optimize its report creation algorithm by referring to past report data during report generation. For example, the reporting unit can select the optimal creation algorithm based on past report data. Furthermore, the reporting unit can propose effective creation methods based on past report data. In addition, the reporting unit can analyze past report data and select the most efficient creation algorithm. This allows the system to provide the optimal creation algorithm by referring to past report data.

[0067] The reporting system can weight report data based on the submission date of advertisements when creating reports. For example, it can give higher weight to advertisements with upcoming submission dates and lower weight to advertisements with later submission dates. Furthermore, the reporting system can adjust the weighting of report data according to the submission date to create an optimal report. This enables the creation of effective reports by weighting report data based on the submission date of advertisements.

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

[0069] The planning department can dynamically adjust the ad delivery schedule. For example, it can change the timing of ad delivery to match specific events or seasons. It can also concentrate ad delivery to coincide with the user's business peak periods. Furthermore, it can monitor the ad delivery status of competitors in real time and optimize the delivery schedule accordingly. This maximizes the effectiveness of ad delivery.

[0070] The ad delivery unit can dynamically change the creative elements of ads. For example, it can select the most effective creative elements based on a user's past ad viewing history. It can also analyze user responses in real time and change creative elements as needed. Furthermore, by using different creative elements for different target audiences, the effectiveness of ads can be maximized. In this way, optimizing the creative elements of ads can improve the effectiveness of ad delivery.

[0071] The measurement unit can monitor the effectiveness of ad delivery in real time and adjust the delivery strategy as needed. For example, if the click-through rate or conversion rate of an ad is low, the delivery strategy can be changed to improve its effectiveness. Also, if the ad is highly effective for a specific target audience, delivery to that audience can be strengthened. Furthermore, the system can monitor the ad delivery status of competitors and adjust the delivery strategy accordingly. This maximizes the effectiveness of ad delivery.

[0072] The planning department can dynamically change the target audience for ad delivery. For example, it can change the target audience according to specific times of day or days of the week. It can also analyze user behavior data in real time and select the most effective target audience. Furthermore, it can monitor the target audiences of competitors and adjust the target audience accordingly. This maximizes the effectiveness of ad delivery.

[0073] The ad delivery unit can dynamically change ad delivery channels. For example, it can select the most effective channel for a specific target audience. It can also analyze the effectiveness of ad delivery in real time and change delivery channels as needed. Furthermore, it can monitor the ad delivery channels of competitors and adjust its own channels accordingly. This maximizes the effectiveness of ad delivery.

[0074] The objective confirmation unit can analyze a user's past ad delivery history and select the optimal objective confirmation method. For example, it can automatically display ad delivery objectives that the user has frequently set in the past as candidates. It can also prioritize suggesting confirmation methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest ad delivery objectives to be used during specific time periods based on the user's past ad delivery history. In this way, by analyzing the user's past ad delivery history, it can provide the optimal objective confirmation method.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The Objective Confirmation Unit confirms the user's advertising objective. The Objective Confirmation Unit receives the advertising objective set by the user as input and evaluates whether that objective is appropriate. Using AI, the validity of the objective can be evaluated based on LINE Yahoo data. For example, it can analyze past advertising data and market trends to determine whether the set objective is appropriate for the current situation. Step 2: The Planning Department creates a delivery plan based on the objectives confirmed by the Objective Confirmation Department. The Planning Department can use AI to create the optimal delivery plan based on LINE Yahoo data. For example, it can select target audiences, set delivery schedules, and select ad formats. It confirms the elements necessary to achieve the objectives and clarifies the elements that can be supported by ad delivery. Step 3: The distribution unit delivers ads based on the distribution plan created by the planning unit. The distribution unit can use AI to configure distribution settings from the LINE Yahoo! Ads management screen and deliver ads. For example, it can automatically deliver ads according to the distribution schedule. The distribution unit has a settings unit for configuring ad distribution settings. Step 4: The measurement unit measures the effectiveness of the advertisements delivered by the delivery unit. The measurement unit can use AI to measure effectiveness based on delivery results and LINE Yahoo data. For example, it evaluates the effectiveness of the advertisement using metrics such as click-through rate, conversion rate, and reach. The measurement unit is equipped with a reporting unit that reports the effectiveness measurement results.

[0077] (Example of form 2) The AI ​​agent for ad delivery and performance measurement, according to an embodiment of the present invention, is a system that automates everything from ad delivery planning to delivery and performance measurement. This system was proposed to solve the problems of conventional ad delivery, such as the burden on human resources, lack of consistency, and limitations in speed. The AI ​​agent for ad delivery and performance measurement consists of the following three steps. First, in objective confirmation and delivery planning, the AI ​​confirms the user's ad delivery objective and creates an optimal delivery plan using LINE Yahoo data. Next, in ad delivery, the AI ​​sets up delivery from the LINE Yahoo ad management screen based on the delivery plan and executes the delivery. Finally, in performance measurement, after delivery, the AI ​​uses the delivery results and LINE Yahoo data to support performance measurement and reporting. For example, when the AI ​​agent for ad delivery confirms the user's ad delivery objective, the AI ​​evaluates whether achieving the set objective is necessary now using LINE Yahoo data, and if corrections are needed, it makes corrections while interacting with the user. The AI ​​also considers the elements necessary to achieve the set objective and clarifies the elements that can be supported by ad delivery. Furthermore, the AI ​​selects and proposes the most suitable LINE Yahoo! ads to achieve the listed advertising objectives. Based on LINE Yahoo! data, it proposes advertising content, delivery period, effectiveness measurement methods, and target audiences that are a good fit for achieving advertising objectives, and creates a delivery plan. As a result, the AI ​​agent supporting advertising delivery and effectiveness measurement automates everything from confirming advertising objectives to delivery and effectiveness measurement, reducing the burden on human resources and improving consistency and accuracy.

[0078] The AI ​​agent for ad delivery and performance measurement according to this embodiment comprises a purpose confirmation unit, a planning unit, a delivery unit, and a measurement unit. The purpose confirmation unit confirms the user's ad delivery purpose. For example, the purpose confirmation unit receives the ad delivery purpose set by the user as input and evaluates whether the purpose is appropriate. The purpose confirmation unit can use AI to evaluate the validity of the purpose based on LINE Yahoo data. For example, the purpose confirmation unit analyzes past ad delivery data and market trends to determine whether the set purpose is appropriate for the current situation. The planning unit creates a delivery plan based on the purpose confirmed by the purpose confirmation unit. The planning unit can use AI to create an optimal delivery plan based on LINE Yahoo data. For example, the planning unit selects target audiences, sets delivery schedules, and selects ad formats. The planning unit confirms the elements necessary to achieve the purpose and clarifies the elements that can be supported by ad delivery. The delivery unit delivers ads based on the delivery plan created by the planning unit. The delivery unit can use AI to configure delivery settings from the LINE Yahoo ad management screen and deliver ads. For example, the distribution unit automatically distributes advertisements according to the distribution schedule. The distribution unit includes a settings unit for configuring the distribution settings of advertisements. The measurement unit measures the effectiveness of advertisements distributed by the distribution unit. The measurement unit can use AI to measure effectiveness based on distribution results and LINE Yahoo data. For example, the measurement unit evaluates the effectiveness of advertisements using metrics such as click-through rate, conversion rate, and reach. The measurement unit includes a reporting unit that reports the effectiveness measurement results. As a result, the AI ​​agent supporting ad distribution and effectiveness measurement according to this embodiment can automate everything from confirming the purpose of ad distribution to distribution and effectiveness measurement, reducing the burden on human resources and improving consistency and accuracy.

[0079] The Objective Verification Unit verifies the user's advertising objectives. Specifically, it receives the user's set advertising objectives as input and evaluates whether those objectives are appropriate. The Objective Verification Unit uses AI to analyze past advertising data and market trends to determine if the set objectives are suitable for the current situation. For example, if a user sets the objective to "increase awareness of a new product," the Objective Verification Unit refers to data from past advertising campaigns with similar objectives and analyzes their effectiveness. Furthermore, it considers current market trends and the actions of competitors to evaluate whether the set objectives are realistic. The Objective Verification Unit provides feedback to the user regarding the validity of the objectives and can suggest revisions as needed. For example, if the objective is overly ambitious, it will advise setting more realistic goals. The Objective Verification Unit also collects information on the user's business objectives and target audience and evaluates the objectives based on this information. In this way, the Objective Verification Unit can ensure that the user's advertising objectives are clear and appropriate, thereby increasing the success rate of advertising.

[0080] The Planning Department creates a delivery plan based on the objectives confirmed by the Objective Confirmation Department. Specifically, it uses AI to analyze past advertising delivery data and market trends to create the optimal delivery plan. For example, to select a target audience, the Planning Department identifies the most effective audience segment based on the user's business goals and information about the target audience. Furthermore, it sets a delivery schedule to optimize the timing of ad delivery. For example, by delivering ads at specific times or days of the week, it is possible to effectively reach the target audience. The Planning Department also selects the ad format, choosing the most suitable ad format for the user's objectives. For example, it selects the most suitable ad format from various ad formats such as banner ads, video ads, and native ads. The Planning Department comprehensively considers these elements, confirms the elements necessary to achieve the objectives, and clarifies the elements that can be supported by ad delivery. As a result, the Planning Department can create the optimal delivery plan for the user's advertising objectives and maximize the effectiveness of ad delivery.

[0081] The distribution department delivers ads based on the distribution plan created by the planning department. Specifically, it uses AI to configure ad delivery settings and automatically delivers ads. For example, the distribution department delivers ads according to the distribution schedule, effectively reaching the target audience. The distribution department has a settings department for configuring ad delivery settings, allowing for detailed settings of ad delivery timing and destinations. For example, it can set up ad delivery to specific regions or devices. The distribution department monitors ad delivery status in real time and can adjust delivery settings as needed. For example, if the click-through rate or conversion rate of an ad is low, it will change the delivery settings to improve effectiveness. The distribution department also records ad delivery results and collects data for later performance measurement. As a result, the distribution department can efficiently and effectively deliver ads based on the distribution plan created by the planning department, maximizing the effectiveness of ad delivery.

[0082] The measurement unit measures the effectiveness of advertisements delivered by the delivery unit. Specifically, it uses AI to measure effectiveness based on delivery results and past ad delivery data. For example, the measurement unit evaluates the effectiveness of advertisements using metrics such as click-through rate, conversion rate, and reach. The measurement unit analyzes the ad delivery results in detail and identifies which elements contribute to the effectiveness of the advertisement. For example, it evaluates whether a particular target audience or ad format is effective. The measurement unit is equipped with a reporting unit that reports the effectiveness measurement results, providing users with easy-to-understand reports. For example, it uses graphs and charts to visually show the effectiveness of the advertisement. In addition, the measurement unit can suggest improvements for the next ad delivery based on the effectiveness measurement results. For example, it may suggest strengthening ad delivery to a specific target audience or changing the ad format. In this way, the measurement unit can accurately measure the effectiveness of ad delivery and clearly identify areas for improvement for the next ad delivery, thereby continuously improving the effectiveness of ad delivery.

[0083] The Planning Department includes an Objective Evaluation Department that uses LINE & Yahoo data to assess the validity of objective settings. For example, the Planning Department evaluates whether the set advertising delivery objectives are appropriate for the current situation based on LINE & Yahoo data. The Objective Evaluation Department can use AI to analyze past advertising delivery data and market trends to determine the validity of the objectives. For example, the Objective Evaluation Department can refer to past successful and unsuccessful advertising campaign examples to evaluate whether the set objectives are achievable. By evaluating the validity of the objective settings in this way, the effectiveness of advertising delivery can be maximized.

[0084] The planning department includes an element verification department that confirms the elements essential for achieving the objective. For example, the planning department confirms the elements necessary to achieve the advertising delivery objective. The element verification department can use AI to confirm the characteristics of the target audience and the content of the advertising message. For example, the element verification department analyzes the behavioral data and attribute data of the target audience to identify the elements necessary for advertising delivery. By confirming the elements necessary to achieve the objective, effective advertising delivery can be realized.

[0085] The Planning Department includes an Advertising Proposal Department that proposes the most suitable advertising products. For example, the Planning Department proposes the most suitable advertising products to achieve advertising delivery objectives. The Advertising Proposal Department can use AI to select advertising formats, creatives, and delivery channels. For instance, the Advertising Proposal Department analyzes past advertising delivery data and market trends to propose the most suitable advertising products. This maximizes the effectiveness of advertising delivery by proposing the optimal advertising products.

[0086] The Planning Department includes a Target Proposal Department that proposes content and targets for ad delivery. For example, the Planning Department proposes ad delivery content and target audiences. The Target Proposal Department uses AI to analyze the characteristics of ad messages and target audiences, and can propose optimal delivery content and targets. For instance, the Target Proposal Department selects ad delivery content and targets based on past ad delivery data and user behavior data. This allows for the maximization of ad delivery effectiveness by proposing appropriate content and targets.

[0087] The delivery unit includes a settings unit for configuring ad delivery. The delivery unit handles tasks such as ad delivery schedules, target audience selection, and ad format settings. The settings unit can automate ad delivery settings using AI. For example, the settings unit automatically delivers ads according to the delivery schedule. This allows for automated ad delivery by configuring ad delivery settings.

[0088] The measurement unit includes a reporting unit that reports the results of the effectiveness measurement. For example, the measurement unit measures the effectiveness of ad delivery and reports the results. The reporting unit can automatically report the effectiveness measurement results using AI. For example, the reporting unit evaluates the effectiveness of ads using metrics such as click-through rate, conversion rate, and reach, and reports the results. This allows for improvement of ad delivery by reporting the effectiveness measurement results.

[0089] The objective confirmation unit can estimate the user's emotions and adjust the method of confirming the advertising objective based on the estimated emotions. For example, if the user is stressed, the objective confirmation unit can provide a simple interface and minimize the confirmation steps. If the user is relaxed, the objective confirmation unit can provide detailed confirmation options and suggest a customizable confirmation method. Furthermore, if the user is in a hurry, the objective confirmation unit can prioritize voice input to allow for quick confirmation of the advertising objective. This reduces the burden on the user by adjusting the method of confirming the advertising objective according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The objective confirmation unit can analyze a user's past ad delivery history and select the optimal objective confirmation method. For example, the objective confirmation unit can automatically display ad delivery objectives that the user has frequently set in the past as candidates. Furthermore, the objective confirmation unit can prioritize suggesting confirmation methods (voice, text, etc.) that the user has used in the past. In addition, the objective confirmation unit can predict and suggest ad delivery objectives to be used during specific time periods based on the user's past ad delivery history. This allows the system to provide the optimal objective confirmation method by analyzing the user's past ad delivery history.

[0091] The objective verification unit can filter ad delivery objectives based on the user's current business situation and market trends. For example, the objective verification unit can analyze the user's current business situation and propose the most suitable ad delivery objective. Furthermore, the objective verification unit can consider market trends and propose ad delivery objectives that align with current trends. In addition, the objective verification unit can propose appropriate ad delivery objectives according to the user's business growth stage. This allows for the proposal of the most suitable ad delivery objective by filtering based on the user's business situation and market trends.

[0092] The objective confirmation unit can estimate the user's emotions and determine the priority of ad delivery objectives to be reviewed based on the estimated emotions. For example, if the user is stressed, the objective confirmation unit will prioritize reviewing high-priority ad delivery objectives. If the user is relaxed, the objective confirmation unit can perform a more detailed review and adjust the priorities. Furthermore, if the user is in a hurry, the objective confirmation unit can prioritize displaying important ad delivery objectives for quick review. This reduces the user's burden by determining the priority of ad delivery objectives according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The objective verification unit can prioritize identifying highly relevant objectives when verifying advertising delivery objectives, taking into account the user's geographical location information. For example, the objective verification unit can prioritize identifying regionally relevant advertising delivery objectives based on the user's current location. Furthermore, the objective verification unit can analyze the user's past location information and suggest highly relevant advertising delivery objectives. In addition, the objective verification unit can prioritize identifying the optimal advertising delivery objective based on the user's business location. This allows for the prioritization of highly relevant advertising delivery objectives by considering the user's geographical location information.

[0094] The objective verification unit can analyze the user's social media activity and identify relevant objectives when verifying advertising delivery objectives. For example, the objective verification unit can analyze the user's social media activity and suggest relevant advertising delivery objectives. Furthermore, the objective verification unit can identify highly relevant advertising delivery objectives based on the activity of the user's followers and friends. In addition, the objective verification unit can analyze the content of the user's social media posts and suggest the most suitable advertising delivery objectives. This allows for the identification of relevant advertising delivery objectives by analyzing the user's social media activity.

[0095] The planning unit can estimate the user's emotions and adjust how the delivery plan is created based on those emotions. For example, if the user is relaxed, the planning unit can create a detailed delivery plan. If the user is in a hurry, the planning unit can create a concise and quick-execute delivery plan. Furthermore, if the user is excited, the planning unit can create a visually appealing delivery plan. This reduces the user's burden by adjusting how the delivery plan is created according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The planning department can adjust the level of detail in a delivery plan based on the importance of the advertisements. For example, the planning department can create detailed delivery plans for high-importance advertisements, and simpler plans for lower-importance advertisements. Furthermore, the planning department can adjust resource allocation according to the importance of the advertisements to create the optimal delivery plan. This allows for effective ad delivery by adjusting the level of detail in the plan based on the importance of the advertisements.

[0097] The planning department can apply different planning algorithms depending on the ad category when creating a delivery plan. For example, if the ad category is fashion, the planning department can apply a visually appealing planning algorithm. If the ad category is technology, the planning department can apply a planning algorithm that emphasizes technical details. Furthermore, if the ad category is food, the planning department can apply a planning algorithm that emphasizes sensory elements. By applying different planning algorithms according to the ad category, effective ad delivery can be achieved.

[0098] The planning unit can estimate the user's emotions and adjust the length of the plan based on those emotions. For example, if the user is in a hurry, the planning unit can create a short, concise plan. If the user is relaxed, the planning unit can create a longer plan with detailed explanations. Furthermore, if the user is excited, the planning unit can create a plan with visually stimulating elements. By adjusting the length of the plan according to the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The planning department can prioritize advertising plans based on their submission dates when creating delivery plans. For example, the planning department can prioritize creating plans for ads with upcoming submission dates, and postpone creating plans for ads with later submission dates. Furthermore, the planning department can adjust resource allocation according to submission dates to create the optimal plan. This allows for effective ad delivery by prioritizing plans based on ad submission dates.

[0100] The planning department can adjust the order of advertising plans based on their relevance when creating delivery plans. For example, the planning department can prioritize creating plans for highly relevant ads. Conversely, it can postpone creating plans for less relevant ads. Furthermore, the planning department can adjust resource allocation according to the relevance of the ads to create the optimal plan. This allows for effective ad delivery by adjusting the order of plans based on ad relevance.

[0101] The ad delivery unit can estimate the user's emotions and adjust the ad delivery method based on the estimated emotions. For example, if the user is relaxed, the ad delivery unit can deliver ads at a relaxed pace. If the user is in a hurry, the ad delivery unit can deliver ads quickly. Furthermore, if the user is excited, the ad delivery unit can deliver visually stimulating ads. In this way, the burden on the user can be reduced by adjusting the ad delivery method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The ad delivery department can analyze a user's past delivery history to select the optimal delivery method when delivering ads. For example, the department can select the optimal delivery method based on the delivery methods the user has used in the past. Furthermore, the department can suggest effective delivery methods based on the user's past delivery history. In addition, the department can analyze the user's past delivery history to select the most efficient delivery method. This allows the department to provide the optimal delivery method by analyzing the user's past delivery history.

[0103] The ad delivery department can customize the delivery method based on the user's current business situation when delivering ads. For example, the delivery department can analyze the user's current business situation and propose the optimal delivery method. Furthermore, the delivery department can select an appropriate delivery method according to the user's business growth stage. In addition, the delivery department can provide customized delivery methods based on the user's business needs. This allows for optimal ad delivery by customizing the delivery method based on the user's business situation.

[0104] The ad delivery unit can estimate the user's emotions and prioritize ad delivery based on those emotions. For example, if the user is stressed, the unit will prioritize delivering high-priority ads. If the user is relaxed, the unit can perform a more detailed review and adjust priorities. Furthermore, if the user is in a hurry, the unit can prioritize delivering important ads quickly. This reduces the user's burden by prioritizing ad delivery according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The ad delivery unit can select the optimal delivery method when delivering ads, taking into account the user's geographical location. For example, the delivery unit can prioritize delivering ads relevant to the user's location based on their current location. Furthermore, the delivery unit can analyze the user's past location data to suggest highly relevant ads. In addition, the delivery unit can select the optimal delivery method based on the user's business location. This allows for the provision of the most effective ad delivery method by considering the user's geographical location.

[0106] The ad delivery department can analyze users' social media activity and suggest appropriate delivery methods when delivering ads. For example, it can analyze users' social media activity and suggest the most suitable delivery method. Furthermore, it can deliver highly relevant ads based on the activity of users' followers and friends. In addition, it can analyze the content of users' social media posts and suggest the most suitable delivery method. This allows for the provision of optimal ad delivery methods by analyzing users' social media activity.

[0107] The measurement unit can estimate the user's emotions and adjust the effectiveness measurement method based on the estimated emotions. For example, if the user is relaxed, the measurement unit can perform a detailed effectiveness measurement. If the user is in a hurry, the measurement unit can perform a concise and quick effectiveness measurement. Furthermore, if the user is excited, the measurement unit can perform a visually appealing effectiveness measurement. By adjusting the effectiveness measurement method according to the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The measurement unit can optimize the measurement algorithm by referring to past measurement data during effect measurement. For example, the measurement unit can select the optimal measurement algorithm based on past measurement data. Furthermore, the measurement unit can propose effective measurement methods based on past measurement data. In addition, the measurement unit can analyze past measurement data and select the most efficient measurement algorithm. Thus, by referring to past measurement data, it can provide the optimal measurement algorithm.

[0109] The measurement unit can apply different measurement methods to each advertising category when measuring effectiveness. For example, if the advertising category is fashion, the measurement unit can apply a measurement method that emphasizes visual elements. If the advertising category is technology, the measurement unit can apply a measurement method that emphasizes technical details. Furthermore, if the advertising category is food, the measurement unit can apply a measurement method that emphasizes sensory elements. By applying different measurement methods to each advertising category, effective measurement can be achieved.

[0110] The measurement unit can estimate the user's emotions and determine the priority of effect measurements based on the estimated emotions. For example, if the user is tense, the measurement unit will prioritize high-priority effect measurements. If the user is relaxed, the measurement unit can perform more detailed checks and adjust priorities. Furthermore, if the user is in a hurry, the measurement unit can prioritize important effect measurements so that they can be measured quickly. In this way, the burden on the user can be reduced by determining the priority of effect measurements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The measurement unit can weight the measurement data based on the timing of ad submissions when measuring effectiveness. For example, the measurement unit can give higher weight to ads that are submitted sooner, and lower weight to ads that are submitted far in the future. Furthermore, the measurement unit can adjust the weighting of the measurement data according to the submission timing to perform optimal measurements. This enables effective measurement by weighting the measurement data based on the timing of ad submissions.

[0112] The measurement unit can perform measurements by referring to relevant market data for advertising during effectiveness measurement. For example, the measurement unit can select the optimal measurement method based on relevant market data. Furthermore, the measurement unit can propose effective measurement methods based on relevant market data. In addition, the measurement unit can analyze relevant market data and select the most efficient measurement method. This allows the system to provide the optimal measurement method by referring to relevant market data for advertising.

[0113] The objective evaluation unit can estimate the user's emotions and adjust the objective evaluation method based on the estimated user emotions. For example, if the user is relaxed, the objective evaluation unit can perform a detailed objective evaluation. If the user is in a hurry, the objective evaluation unit can perform a concise and quickly actionable objective evaluation. Furthermore, if the user is excited, the objective evaluation unit can perform a visually appealing objective evaluation. In this way, the burden on the user can be reduced by adjusting the objective evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The objective evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during the objective evaluation. For example, the objective evaluation unit can select the optimal evaluation algorithm based on past evaluation data. Furthermore, the objective evaluation unit can propose an effective evaluation method based on past evaluation data. In addition, the objective evaluation unit can analyze past evaluation data and select the most efficient evaluation algorithm. Thus, by referring to past evaluation data, it can provide the optimal evaluation algorithm.

[0115] The objective evaluation unit can estimate the user's emotions and determine the priority of objective evaluations based on the estimated emotions. For example, if the user is stressed, the objective evaluation unit will prioritize high-importance objective evaluations. If the user is relaxed, the objective evaluation unit can perform more detailed checks and adjust priorities. Furthermore, if the user is in a hurry, the objective evaluation unit can prioritize important objective evaluations so that they can be evaluated quickly. In this way, the user's burden can be reduced by determining the priority of objective evaluations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0116] The objective evaluation unit can weight evaluation data based on the timing of ad submissions during the objective evaluation process. For example, the objective evaluation unit can give higher weight to ads with upcoming submission dates and lower weight to ads with later submission dates. Furthermore, the objective evaluation unit can adjust the weighting of evaluation data according to the submission dates to perform optimal evaluations. This enables effective evaluation by weighting evaluation data based on the timing of ad submissions.

[0117] The element verification unit can estimate the user's emotions and adjust the element verification method based on the estimated emotions. For example, if the user is relaxed, the element verification unit can perform a detailed element verification. If the user is in a hurry, the element verification unit can perform a concise and quick element verification. Furthermore, if the user is excited, the element verification unit can perform a visually appealing element verification. In this way, the burden on the user can be reduced by adjusting the element verification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0118] The element verification unit can optimize the verification algorithm by referring to past verification data during element verification. For example, the element verification unit can select the optimal verification algorithm based on past verification data. Furthermore, the element verification unit can propose effective verification methods based on past verification data. In addition, the element verification unit can analyze past verification data and select the most efficient verification algorithm. Thus, by referring to past verification data, it can provide the optimal verification algorithm.

[0119] The element verification unit can estimate the user's emotions and determine the priority of element verification based on the estimated emotions. For example, if the user is nervous, the element verification unit will prioritize verifying high-importance elements. If the user is relaxed, the element verification unit can perform more detailed verification and adjust priorities. Furthermore, if the user is in a hurry, the element verification unit can prioritize verifying important elements so that they can be checked quickly. In this way, the user's burden can be reduced by determining the priority of element verification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0120] The element verification unit can weight verification data based on the ad submission date during element verification. For example, it can give higher weight to ads with upcoming submission dates and lower weight to ads with later submission dates. Furthermore, the element verification unit can adjust the weighting of verification data according to the submission date to perform optimal verification. This enables effective verification by weighting verification data based on the ad submission date.

[0121] The advertising recommendation department can estimate the user's emotions and adjust the advertising recommendation method based on the estimated emotions. For example, if the user is relaxed, the advertising recommendation department can provide detailed advertising recommendations. If the user is in a hurry, the advertising recommendation department can provide concise and quick-action advertising recommendations. Furthermore, if the user is excited, the advertising recommendation department can provide visually appealing advertising recommendations. In this way, the burden on the user can be reduced by adjusting the advertising recommendation method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0122] The advertising proposal department can optimize its proposal algorithm by referring to past proposal data when creating advertising proposals. For example, the advertising proposal department can select the optimal proposal algorithm based on past proposal data. Furthermore, the advertising proposal department can propose effective proposal methods based on past proposal data. In addition, the advertising proposal department can analyze past proposal data and select the most efficient proposal algorithm. This allows the department to provide the optimal proposal algorithm by referring to past proposal data.

[0123] The advertising recommendation department can estimate the user's emotions and prioritize advertising recommendations based on those emotions. For example, if the user is stressed, the department will prioritize high-priority advertising recommendations. If the user is relaxed, the department can perform a more detailed review and adjust priorities. Furthermore, if the user is in a hurry, the department can prioritize important advertising recommendations to provide them quickly. This reduces the user's burden by prioritizing advertising recommendations according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0124] The advertising proposal department can weight proposal data based on the submission date of the advertisement. For example, the department can give higher weight to advertisements with an upcoming submission date and lower weight to advertisements with a distant submission date. Furthermore, the department can adjust the weighting of proposal data according to the submission date to make the most optimal proposal. This allows for effective proposals by weighting proposal data based on the advertisement submission date.

[0125] The target suggestion unit can estimate the user's emotions and adjust the method of target suggestion based on the estimated emotions. For example, if the user is relaxed, the target suggestion unit can provide detailed target suggestions. If the user is in a hurry, the target suggestion unit can provide concise and actionable target suggestions. Furthermore, if the user is excited, the target suggestion unit can provide visually appealing target suggestions. By adjusting the method of target suggestion according to the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0126] The target proposal unit can optimize the proposal algorithm by referring to past proposal data when proposing a target. For example, the target proposal unit can select the optimal proposal algorithm based on past proposal data. Furthermore, the target proposal unit can propose effective proposal methods based on past proposal data. In addition, the target proposal unit can analyze past proposal data and select the most efficient proposal algorithm. Thus, by referring to past proposal data, it can provide the optimal proposal algorithm.

[0127] The target suggestion unit can estimate the user's emotions and prioritize target suggestions based on those emotions. For example, if the user is stressed, the target suggestion unit will prioritize high-priority target suggestions. If the user is relaxed, the target suggestion unit can perform a more detailed review and adjust priorities. Furthermore, if the user is in a hurry, the target suggestion unit can prioritize important target suggestions to provide them quickly. This reduces the user's burden by prioritizing target suggestions according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0128] The target proposal department can weight proposal data based on the ad submission timing when making target proposals. For example, the department can give higher weight to ads with upcoming submission dates and lower weight to ads with later submission dates. Furthermore, the department can adjust the weighting of proposal data according to the submission timing to make optimal proposals. This allows for effective proposals by weighting proposal data based on the ad submission timing.

[0129] The settings unit can estimate the user's emotions and adjust the ad delivery settings based on those emotions. For example, if the user is relaxed, the settings unit can make detailed settings. If the user is in a hurry, the settings unit can make simple and quick settings. Furthermore, if the user is excited, the settings unit can make visually appealing settings. By adjusting the ad delivery settings according to the user's emotions, the burden on the user can be reduced. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0130] The settings unit can optimize the settings algorithm by referring to past settings data when configuring ad delivery. For example, the settings unit can select the optimal settings algorithm based on past settings data. Furthermore, the settings unit can suggest effective settings methods based on past settings data. In addition, the settings unit can analyze past settings data and select the most efficient settings algorithm. This allows the system to provide the optimal settings algorithm by referring to past settings data.

[0131] The settings unit can estimate the user's emotions and determine the priority of ad delivery settings based on the estimated emotions. For example, if the user is stressed, the settings unit will prioritize high-priority settings. If the user is relaxed, the settings unit can perform a more detailed review and adjust priorities. Furthermore, if the user is in a hurry, the settings unit can prioritize important settings so that they can be configured quickly. This reduces the burden on the user by determining the priority of ad delivery settings according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0132] The settings unit can weight the setting data based on the ad submission timing when configuring ad delivery. For example, it can assign higher weights to ads with upcoming submission dates and lower weights to ads with later submission dates. Furthermore, the settings unit can adjust the weighting of the setting data according to the submission timing to achieve optimal settings. This allows for effective settings by weighting the setting data based on the ad submission timing.

[0133] The reporting unit can estimate the user's emotions and adjust how the report is generated based on those emotions. For example, if the user is relaxed, the reporting unit can create a detailed report. If the user is in a hurry, it can create a concise and actionable report. Furthermore, if the user is excited, it can create a visually appealing report. This reduces the user's burden by adjusting how the report is generated according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0134] The reporting unit can optimize its report creation algorithm by referring to past report data during report generation. For example, the reporting unit can select the optimal creation algorithm based on past report data. Furthermore, the reporting unit can propose effective creation methods based on past report data. In addition, the reporting unit can analyze past report data and select the most efficient creation algorithm. This allows the system to provide the optimal creation algorithm by referring to past report data.

[0135] The reporting system can estimate the user's emotions and prioritize reports based on those emotions. For example, if the user is stressed, the reporting system will prioritize creating high-priority reports. If the user is relaxed, the reporting system can perform more detailed checks and adjust priorities accordingly. Furthermore, if the user is in a hurry, the reporting system can prioritize creating important reports quickly. This reduces the user's burden by prioritizing reports according to their emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0136] The reporting system can weight report data based on the submission date of advertisements when creating reports. For example, it can give higher weight to advertisements with upcoming submission dates and lower weight to advertisements with later submission dates. Furthermore, the reporting system can adjust the weighting of report data according to the submission date to create an optimal report. This enables the creation of effective reports by weighting report data based on the submission date of advertisements.

[0137] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0138] The objective confirmation unit can estimate the user's emotions and adjust the method of confirming the ad delivery objective based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the confirmation steps. If the user is relaxed, it can provide detailed confirmation options and suggest a customizable confirmation method. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick confirmation of the ad delivery objective. In this way, the user's burden can be reduced by adjusting the method of confirming the ad delivery objective according to their emotions.

[0139] The planning department can dynamically adjust the ad delivery schedule. For example, it can change the timing of ad delivery to match specific events or seasons. It can also concentrate ad delivery to coincide with the user's business peak periods. Furthermore, it can monitor the ad delivery status of competitors in real time and optimize the delivery schedule accordingly. This maximizes the effectiveness of ad delivery.

[0140] The ad delivery unit can dynamically change the creative elements of ads. For example, it can select the most effective creative elements based on a user's past ad viewing history. It can also analyze user responses in real time and change creative elements as needed. Furthermore, by using different creative elements for different target audiences, the effectiveness of ads can be maximized. In this way, optimizing the creative elements of ads can improve the effectiveness of ad delivery.

[0141] The measurement unit can monitor the effectiveness of ad delivery in real time and adjust the delivery strategy as needed. For example, if the click-through rate or conversion rate of an ad is low, the delivery strategy can be changed to improve its effectiveness. Also, if the ad is highly effective for a specific target audience, delivery to that audience can be strengthened. Furthermore, the system can monitor the ad delivery status of competitors and adjust the delivery strategy accordingly. This maximizes the effectiveness of ad delivery.

[0142] The objective confirmation unit can estimate the user's emotions and determine the priority of ad delivery objectives to be reviewed based on those emotions. For example, if the user is stressed, high-priority ad delivery objectives will be prioritized. If the user is relaxed, more detailed reviews can be conducted, and priorities can be adjusted. Furthermore, if the user is in a hurry, important ad delivery objectives can be displayed preferentially for quick review. In this way, the burden on the user can be reduced by determining the priority of ad delivery objectives according to the user's emotions.

[0143] The planning department can dynamically change the target audience for ad delivery. For example, it can change the target audience according to specific times of day or days of the week. It can also analyze user behavior data in real time and select the most effective target audience. Furthermore, it can monitor the target audiences of competitors and adjust the target audience accordingly. This maximizes the effectiveness of ad delivery.

[0144] The ad delivery unit can dynamically change ad delivery channels. For example, it can select the most effective channel for a specific target audience. It can also analyze the effectiveness of ad delivery in real time and change delivery channels as needed. Furthermore, it can monitor the ad delivery channels of competitors and adjust its own channels accordingly. This maximizes the effectiveness of ad delivery.

[0145] The measurement unit can evaluate the effectiveness of ad delivery based on the user's emotions. For example, if the user is relaxed, it can perform detailed effectiveness measurements. If the user is in a hurry, it can perform concise and quick effectiveness measurements. Furthermore, if the user is excited, it can perform visually appealing effectiveness measurements. By adjusting the effectiveness measurement method according to the user's emotions, the burden on the user can be reduced.

[0146] The objective confirmation unit can analyze a user's past ad delivery history and select the optimal objective confirmation method. For example, it can automatically display ad delivery objectives that the user has frequently set in the past as candidates. It can also prioritize suggesting confirmation methods (voice, text, etc.) that the user has used in the past. Furthermore, it can predict and suggest ad delivery objectives to be used during specific time periods based on the user's past ad delivery history. In this way, by analyzing the user's past ad delivery history, it can provide the optimal objective confirmation method.

[0147] The planning department can estimate the user's emotions and adjust how delivery plans are created based on those estimates. For example, if the user is relaxed, a detailed delivery plan can be created. If the user is in a hurry, a concise and quick-execute delivery plan can be created. Furthermore, if the user is excited, a visually appealing delivery plan can be created. By adjusting how delivery plans are created according to the user's emotions, the burden on the user can be reduced.

[0148] The following briefly describes the processing flow for example form 2.

[0149] Step 1: The Objective Confirmation Unit confirms the user's advertising objective. The Objective Confirmation Unit receives the advertising objective set by the user as input and evaluates whether that objective is appropriate. Using AI, the validity of the objective can be evaluated based on LINE Yahoo data. For example, it can analyze past advertising data and market trends to determine whether the set objective is appropriate for the current situation. Step 2: The Planning Department creates a delivery plan based on the objectives confirmed by the Objective Confirmation Department. The Planning Department can use AI to create the optimal delivery plan based on LINE Yahoo data. For example, it can select target audiences, set delivery schedules, and select ad formats. It confirms the elements necessary to achieve the objectives and clarifies the elements that can be supported by ad delivery. Step 3: The distribution unit delivers ads based on the distribution plan created by the planning unit. The distribution unit can use AI to configure distribution settings from the LINE Yahoo! Ads management screen and deliver ads. For example, it can automatically deliver ads according to the distribution schedule. The distribution unit has a settings unit for configuring ad distribution settings. Step 4: The measurement unit measures the effectiveness of the advertisements delivered by the delivery unit. The measurement unit can use AI to measure effectiveness based on delivery results and LINE Yahoo data. For example, it evaluates the effectiveness of the advertisement using metrics such as click-through rate, conversion rate, and reach. The measurement unit is equipped with a reporting unit that reports the effectiveness measurement results.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0152] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] Each of the multiple elements described above, including the objective confirmation unit, planning unit, distribution unit, and measurement unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the objective confirmation unit is implemented by the control unit 46A of the smart device 14 and confirms the user's advertising distribution objective. The planning unit is implemented by the specific processing unit 290 of the data processing device 12 and creates an optimal distribution plan. The distribution unit is implemented by the control unit 46A of the smart device 14 and distributes the advertisement. The measurement unit is implemented by the specific processing unit 290 of the data processing device 12 and measures the effectiveness of the advertisement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0154] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0155] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0156] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0157] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0158] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0160] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0161] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the objective confirmation unit, planning unit, distribution unit, and measurement unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the objective confirmation unit is implemented by the control unit 46A of the smart glasses 214 and confirms the user's advertising distribution objective. The planning unit is implemented by the specific processing unit 290 of the data processing device 12 and creates an optimal distribution plan. The distribution unit is implemented by the control unit 46A of the smart glasses 214 and distributes the advertisement. The measurement unit is implemented by the specific processing unit 290 of the data processing device 12 and measures the effectiveness of the advertisement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0171] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0172] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0173] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0174] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0176] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0177] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0180] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0181] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0182] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0184] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0185] Each of the multiple elements described above, including the objective confirmation unit, planning unit, distribution unit, and measurement unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the objective confirmation unit is implemented by the control unit 46A of the headset terminal 314 and confirms the user's advertising distribution objective. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and creates an optimal distribution plan. The distribution unit is implemented by the control unit 46A of the headset terminal 314 and distributes the advertisement. The measurement unit is implemented by the specific processing unit 290 of the data processing unit 12 and measures the effectiveness of the advertisement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0186] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0187] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0188] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0190] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0192] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0193] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0194] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0195] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0196] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0197] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0198] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0199] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0200] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0201] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0202] Each of the multiple elements described above, including the objective confirmation unit, planning unit, distribution unit, and measurement unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the objective confirmation unit is implemented by the control unit 46A of the robot 414 and confirms the user's advertising distribution objective. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and creates an optimal distribution plan. The distribution unit is implemented by, for example, the control unit 46A of the robot 414 and distributes the advertisement. The measurement unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and measures the effectiveness of the advertisement. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0203] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0204] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0205] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0206] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0207] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0208] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0210] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0211] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0212] 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.

[0213] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0214] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0215] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0216] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0217] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0218] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0219] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0220] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0221] (Note 1) A purpose confirmation unit that confirms the user's advertising delivery purpose, A planning unit creates a distribution plan based on the objectives confirmed by the aforementioned objective confirmation unit, The distribution unit distributes advertisements based on the distribution plan created by the aforementioned planning unit, The system includes a measurement unit for measuring the effectiveness of advertisements delivered by the aforementioned distribution unit. A system characterized by the following features. (Note 2) The aforementioned planning unit, It includes an objective evaluation unit that uses LINE and Yahoo data to evaluate the validity of the objective setting. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned planning unit, It includes an element verification unit that confirms the elements essential for achieving the objective. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned planning unit, We have an advertising proposal department that proposes the most suitable advertising products. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned planning unit, We have a target proposal department that suggests content and target audiences for our broadcasts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned distribution unit, It includes a settings section for configuring ad delivery settings. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned measuring unit is It includes a reporting section that reports the results of effectiveness measurement. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned objective confirmation unit, We estimate user sentiment and adjust the method for confirming ad delivery objectives based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned objective confirmation unit, Analyze the user's past ad delivery history and select the most suitable method for confirming objectives. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned objective confirmation unit, When verifying the purpose of ad delivery, filtering is performed based on the user's current business situation and market trends. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned objective confirmation unit, It estimates user sentiment and determines the priority of ad delivery objectives based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned objective confirmation unit, When reviewing ad delivery objectives, the system prioritizes identifying highly relevant objectives by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned objective confirmation unit, When confirming the purpose of ad delivery, we analyze the user's social media activity and identify the relevant objectives. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned planning unit, We estimate user sentiment and adjust how delivery plans are created based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned planning unit, When creating a delivery plan, adjust the level of detail in the plan based on the importance of the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned planning unit, When creating a delivery plan, different planning algorithms are applied depending on the ad category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned planning unit, It estimates the user's emotions and adjusts the length of the plan based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned planning unit, When creating a distribution plan, prioritize the plan based on the timing of ad submission. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned planning unit, When creating a delivery plan, adjust the order of the plans based on the relevance of the ads. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned distribution unit, We estimate the user's emotions and adjust the ad delivery method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned distribution unit, When delivering ads, the system analyzes the user's past viewing history to select the optimal delivery method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned distribution unit, When delivering ads, customize the delivery method based on the user's current business situation. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned distribution unit, It estimates user sentiment and prioritizes ad delivery based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned distribution unit, When delivering ads, the optimal delivery method is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned distribution unit, When delivering ads, we analyze users' social media activity and suggest appropriate delivery methods. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned measuring unit is We estimate user emotions and adjust the effectiveness measurement method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned measuring unit is When measuring effectiveness, the measurement algorithm is optimized by referring to past measurement data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned measuring unit is When measuring effectiveness, different measurement methods are applied for each ad category. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned measuring unit is We estimate user emotions and determine the priority of effectiveness measurement based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned measuring unit is When measuring effectiveness, weight the measurement data based on when the advertisement was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned measuring unit is When measuring effectiveness, the measurement is performed by referring to relevant market data for the advertisement. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned objective evaluation unit is, We estimate the user's emotions and adjust the objective evaluation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned objective evaluation unit is, During the objective evaluation, the evaluation algorithm is optimized by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned objective evaluation unit is, The system estimates user emotions and determines the priority of objective evaluation based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned objective evaluation unit is, During the objective evaluation, evaluation data is weighted based on the timing of ad submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The element verification unit is, We estimate the user's emotions and adjust the way we check elements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The element verification unit is, When verifying elements, the verification algorithm is optimized by referring to past verification data. The system described in Appendix 1, characterized by the features described herein. (Note 38) The element verification unit is, The system estimates the user's emotions and determines the priority of element review based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The element verification unit is, During element verification, the verification data is weighted based on the ad submission date. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned advertising proposal department, We estimate the user's emotions and adjust the ad recommendation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned advertising proposal department, When making an advertisement proposal, optimize the proposal algorithm by referring to past proposal data The system according to appended note 1, characterized in that it is such that (Appended note 42) The advertisement proposal unit Estimate the user's sentiment and determine the priority of advertisement proposals based on the estimated user sentiment The system according to appended note 1, characterized in that it is such that (Appended note 43) The advertisement proposal unit When making an advertisement proposal, weight the proposal data based on the advertisement submission time The system according to appended note 1, characterized in that it is such that (Appended note 44) The target proposal unit Estimate the user's sentiment and adjust the method of target proposal based on the estimated user sentiment The system according to appended note 1, characterized in that it is such that (Appended note 45) The target proposal unit When making a target proposal, optimize the proposal algorithm by referring to past proposal data The system according to appended note 1, characterized in that it is such that (Appended note 46) The target proposal unit Estimate the user's sentiment and determine the priority of target proposals based on the estimated user sentiment The system according to appended note 1, characterized in that it is such that (Appended note 47) The target proposal unit When making a target proposal, weight the proposal data based on the advertisement submission time The system according to appended note 1, characterized in that it is such that (Appended note 48) The setting unit Estimate the user's sentiment and adjust the method of advertisement distribution setting based on the estimated user sentiment The system according to appended note 1, characterized in that it is such that (Appended note 49) The setting unit When setting up ad delivery, the setting algorithm is optimized by referring to past setting data. The system described in Appendix 1, characterized by the features described herein. (Note 50) The setting unit is, It estimates user sentiment and prioritizes ad delivery settings based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 51) The setting unit is, When setting up ad delivery, weight the setting data based on the ad submission timing. The system described in Appendix 1, characterized by the features described herein. (Note 52) The aforementioned report section is, We estimate user sentiment and adjust how reports are generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 53) The aforementioned report section is, When creating reports, the report generation algorithm is optimized by referring to past report data. The system described in Appendix 1, characterized by the features described herein. (Note 54) The aforementioned report section is, It estimates user sentiment and prioritizes reports based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 55) The aforementioned report section is, When creating the report, weight the report data based on when the advertisement was submitted. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0222] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A purpose confirmation unit that confirms the user's advertising delivery purpose, A planning unit creates a distribution plan based on the objectives confirmed by the aforementioned objective confirmation unit, The distribution unit distributes advertisements based on the distribution plan created by the aforementioned planning unit, The system includes a measurement unit for measuring the effectiveness of advertisements delivered by the aforementioned distribution unit. A system characterized by the following features.

2. The aforementioned planning unit, It includes an objective evaluation unit that uses LINE and Yahoo data to evaluate the validity of the objective setting. The system according to feature 1.

3. The aforementioned planning unit, It includes an element verification unit that confirms the elements essential for achieving the objective. The system according to feature 1.

4. The aforementioned planning unit, We have an advertising proposal department that proposes the most suitable advertising products. The system according to feature 1.

5. The aforementioned planning unit, We have a target proposal department that suggests content and target audiences for our broadcasts. The system according to feature 1.

6. The aforementioned distribution unit, It includes a settings section for configuring ad delivery settings. The system according to feature 1.

7. The aforementioned measuring unit is It includes a reporting section that reports the results of effectiveness measurement. The system according to feature 1.

8. The aforementioned objective confirmation unit, We estimate user sentiment and adjust the method for confirming ad delivery objectives based on the estimated user sentiment. The system according to feature 1.

9. The aforementioned objective confirmation unit, Analyze the user's past ad delivery history and select the most suitable method for confirming objectives. The system according to feature 1.

10. The aforementioned objective confirmation unit, When verifying the purpose of ad delivery, filtering is performed based on the user's current business situation and market trends. The system according to feature 1.

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