ADVERTISING STRATEGIES UTILIZING DIVERSE AI ENTITIES

Abstract

ABSTRACT Advertising Strategies Utilizing Diverse AI Entities The present disclosure introduces an advanced advertising strategy system utilizing diverse AI entities to enhance campaign effectiveness and personalization. The system comprises of Predictive Analytics AI 102 for analyzing consumer data and Natural Language Processing AI 104 for assessing consumer sentiment, and content creation AI 106 for generating personalized multimedia. Ad placement optimization AI 108 dynamically selects optimal platforms, while a/b testing mechanism 116 tests ad variations in real-time. Geo-targeting module 130 customizes ads based on location, and multilingual and cross-cultural adaptation module 134 ensures cultural relevance. Customer engagement AI 110 facilitates direct consumer interactions through virtual assistants, and sentiment-driven content modification system 120 adjusts messaging based on real-time sentiment. Additional components are cross-channel attribution model 128, analytics dashboard, cognitive load assessment component 136, AR/VR integration module 140, and collaborative campaign planning tool 142 for strategic planning. Reference Fig 1

Specification

Description:DETAILED DESCRIPTION

[00022] The following detailed description illustrates embodiments of the present disclosure and ways in which they can be implemented. Although some modes of carrying out the present disclosure have been disclosed, those skilled in the art would recognise that other embodiments for carrying out or practising the present disclosure are also possible.

[00023] The description set forth below in connection with the appended drawings is intended as a description of certain embodiments of advertising strategies utilizing diverse AI entities and is not intended to represent the only forms that may be developed or utilised. The description sets forth the various structures and/or functions in connection with the illustrated embodiments; however, it is to be understood that the disclosed embodiments are merely exemplary of the disclosure that may be embodied in various and alternative forms. The figures are not necessarily to scale; some features may be exaggerated or minimised to show details of particular components. Therefore, specific structural and functional details disclosed herein are not to be interpreted as limiting, but merely as a representative basis for teaching one skilled in the art to variously employ the present invention.

[00024] While the disclosure is susceptible to various modifications and alternative forms, specific embodiment thereof has been shown by way of example in the drawings and will be described in detail below. It should be understood, however, that it is not intended to limit the disclosure to the particular forms disclosed, but on the contrary, the disclosure is to cover all modifications, equivalents, and alternatives falling within the scope of the disclosure.

[00025] The terms "comprises", "comprising", "include(s)", or any other variations thereof, are intended to cover a non-exclusive inclusion, such that a setup, or system that comprises a list of components or steps does not include only those components or steps but may include other components or steps not expressly listed or inherent to such setup or system. In other words, one or more elements in a system or apparatus preceded by "comprises... a" does not, without more constraints, preclude the existence of other elements or additional elements in the system or apparatus.

[00026] In the following detailed description of the embodiments of the disclosure, reference is made to the accompanying drawings and which are shown by way of illustration-specific embodiments in which the disclosure may be practiced. These embodiments are described in sufficient detail to enable those skilled in the art to practice the disclosure, and it is to be understood that other embodiments may be utilized and that changes may be made without departing from the scope of the present disclosure. The following description is, therefore, not to be taken in a limiting sense.

[00027] The present disclosure will be described herein below with reference to the accompanying drawings. In the following description, well-known functions or constructions are not described in detail since they would obscure the description with unnecessary detail.

[00028] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is disclosed, in accordance with one embodiment of the present invention. It comprises of Predictive Analytics AI 102, Natural Language Processing AI 104, content creation AI 106, ad placement optimization AI 108, customer engagement AI 110, data integration and analysis system 112, ethical AI compliance module 114, a/b testing mechanism 116, analytics dashboard 118, sentiment-driven content modification system 120, modular deployment architecture 122, predictive market trend analysis component 124, conversational AI for enhanced consumer interaction 126, cross-channel attribution model 128, geo-targeting module 130, user journey mapping feature 132, multilingual and cross-cultural adaptation module 134, cognitive load assessment component 136, content reusability protocols 138, AR/VR integration module 140, collaborative campaign planning tool 142.

[00029] Referring to Fig. 1, the present disclosure provides details of advertising strategies utilizing diverse AI entities for targeted marketing and enhanced consumer engagement. This framework leverages Predictive Analytics AI 102, Natural Language Processing AI 104, and content creation AI 106 to deliver personalized, adaptive advertisements. The invention further includes ad placement optimization AI 108 and customer engagement AI 110 to maximize reach and direct interaction. In one embodiment, the advertising strategy may feature key components such as data integration and analysis system 112, ethical AI compliance module 114, and a/b testing mechanism 116 for real-time optimization and ethical standards. Additional functionalities include geo-targeting module 130 and user journey mapping feature 132 for location-specific and journey-based insights. Components like cognitive load assessment component 136 and AR/VR integration module 140 enhance user experience and support immersive advertising methods.

[00030] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with Predictive Analytics AI 102, which plays a crucial role in analyzing historical consumer data to predict future behaviors and trends. This component enables the system to identify patterns and correlations, tailoring campaigns to specific audience segments for increased relevance. It works closely with ad placement optimization AI 108 to ensure ads reach predicted audiences at optimal times, enhancing engagement. Additionally, Predictive Analytics AI 102 collaborates with content creation AI 106 to generate relevant content based on predicted consumer interests, boosting personalization.

[00031] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with Natural Language Processing AI 104, which processes textual data, including consumer reviews and social media comments, to understand sentiment and emotional states. This AI component enables the creation of emotionally resonant content, improving campaign effectiveness. Natural Language Processing AI 104 works with sentiment-driven content modification system 120 to dynamically adjust ad messaging based on consumer reactions, maintaining positive brand perception. It also feeds insights to customer engagement AI 110 to provide responses aligned with consumer sentiment, fostering brand loyalty.

[00032] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with content creation AI 106, which uses machine learning algorithms to generate personalized ad copy, visuals, and multimedia, ensuring a consistent brand voice. This component accelerates campaign deployment by automating content generation and adapting it to consumer preferences. content creation AI 106 integrates with Predictive Analytics AI 102 to incorporate data-driven insights into the content, and with analytics dashboard 118 for ongoing performance evaluation, enabling refinement of content strategies based on real-time metrics.

[00033] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with ad placement optimization AI 108, which optimizes where and when ads appear by using real-time performance data from multiple digital platforms. This component ensures maximum ad visibility and engagement by adjusting placements dynamically based on platform-specific metrics. ad placement optimization AI 108 collaborates with Predictive Analytics AI 102 to align ad timing with predicted consumer activity and with cross-channel attribution model 128 to refine placements based on cross-platform performance insights.

[00034] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with customer engagement AI 110, which includes chatbots and virtual assistants that facilitate real-time, interactive consumer communication. This component enables direct responses to consumer inquiries, personalized product recommendations, and feedback collection, enhancing user engagement and loyalty. customer engagement AI 110 works in tandem with Natural Language Processing AI 104 to provide emotionally resonant responses and with user journey mapping feature 132 to tailor interactions to the consumer's journey stage, improving conversion potential.

[00035] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with data integration and analysis system 112, which aggregates consumer data from multiple sources, including browsing history, purchase behavior, and social media insights. This system provides a comprehensive view of consumer profiles, informing other AI entities for more accurate targeting. data integration and analysis system 112 supports Predictive Analytics AI 102 by supplying historical data for trend analysis and works with analytics dashboard 118 to visualize insights for data-driven decision-making.

[00036] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with ethical ai compliance module 114, which ensures the responsible use of AI by implementing data privacy, transparency, and bias mitigation protocols. This component aligns the advertising framework with legal and ethical standards, building consumer trust. ethical AI compliance module 114 works alongside sentiment-driven content modification system 120 to ensure modifications respect privacy guidelines and collaborates with modular deployment architecture 122 to incorporate ethical compliance across AI entities.

[00037] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with a/b testing mechanism 116, which automates the testing and optimization of ad variations by using real-time performance data. This component continuously evaluates different ad elements, selecting the most effective ones based on engagement metrics. a/b testing mechanism 116 interworks with analytics dashboard 118 to track and compare performance, and collaborates with content creation ai 106 to refine ad content dynamically, ensuring maximum impact.

[00038] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with analytics dashboard 118, which consolidates data from all AI entities to provide actionable insights through visualizations and metrics. This component empowers businesses to make informed decisions and continuously optimize campaigns. analytics dashboard 118 works with a/b testing mechanism 116 to display real-time performance comparisons and with cross-channel attribution model 128 to provide insights on the effectiveness of each advertising channel, supporting optimal budget allocation.

[00039] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with sentiment-driven content modification system 120, which dynamically adjusts ad messaging based on real-time consumer sentiment analysis. This component ensures that advertisements reflect positive consumer sentiment and address negative reactions effectively. sentiment-driven content modification system 120 collaborates with Natural Language Processing AI 104 for sentiment insights and customer engagement AI 110 to adjust messaging in consumer interactions, maintaining brand reputation.

[00040] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with modular deployment architecture 122, which enables flexible and scalable integration of AI entities within the advertising framework. This component supports customization based on business needs and the addition of new AI technologies over time. modular deployment architecture 122 facilitates interworking among AI entities like ad placement optimization ai 108 and geo-targeting module 130, ensuring seamless communication and coordination.

[00041] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with predictive market trend analysis component 124, which analyzes market shifts and consumer behavior changes to allow proactive adjustments in advertising strategies. This component helps businesses stay competitive by anticipating trends and adapting content accordingly. predictive market trend analysis component 124 works with predictive analytics AI 102 for aligning predicted consumer interests and collaborates with content creation AI 106 to ensure content relevance.

[00042] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with conversational AI for enhanced consumer interaction 126, which enables real-time communication with consumers through chatbots and virtual assistants. This component enhances customer engagement by providing personalized responses and product recommendations. conversational ai for enhanced consumer interaction 126 integrates with customer engagement ai 110 for consistent communication and with user journey mapping feature 132 to adapt interactions based on consumer journey stages.

[00043] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with cross-channel attribution model 128, which evaluates the effectiveness of different advertising channels, helping to optimize budget allocation. This component uses AI-driven analytics to track each interaction's contribution to conversions. cross-channel attribution model 128 collaborates with analytics dashboard 118 to visualize channel performance and with ad placement optimization ai 108 to adjust placements based on cross-platform insights.

[00044] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with geo-targeting module 130, which delivers location-specific advertisements to consumers, enhancing relevance based on geolocation data. This component personalizes ads by considering regional preferences and needs. geo-targeting module 130 works closely with ad placement optimization AI 108 to ensure location-based ads appear on relevant platforms and integrates with multilingual and cross-cultural adaptation module 134 for cultural customization.

[00045] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with user journey mapping feature 132, which visualizes and tracks the consumer's journey from initial engagement to conversion. This component allows advertisers to optimize strategies at each stage of the journey. user journey mapping feature 132 collaborates with customer engagement AI 110 to personalize interactions and with predictive analytics AI 102 to tailor ad timing according to the consumer's journey stage.

[00046] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with multilingual and cross-cultural adaptation module 134, which translates and culturally customizes ad content for diverse audiences. This component ensures that ads resonate across languages and cultural contexts. multilingual and cross-cultural adaptation module 134 works with geo-targeting module 130 for location-specific relevance and with content creation AI 106 to produce culturally adapted content.

[00047] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with cognitive load assessment component 136, which uses AI to evaluate the cognitive load on consumers, optimizing ad complexity for better comprehension and engagement. This component ensures that ads are neither too simplistic nor overly complex. cognitive load assessment component 136 works with analytics dashboard 118 to monitor engagement and with content creation AI 106 to adjust ad content based on cognitive load insights.

[00048] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with content reusability protocols 138, which identify and repurpose successful ad content across campaigns, saving time and resources. This component ensures consistent messaging and brand voice across platforms. content reusability protocols 138 collaborates with a/b testing mechanism 116 to determine high-performing content and with analytics dashboard 118 for performance tracking.

[00049] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with AR/VR integration module 140, which supports immersive advertising experiences through augmented and virtual reality technologies. This component enhances consumer engagement by delivering interactive, experiential content. AR/VR integration module 140 works with geo-targeting module 130 for location-specific VR/AR content and collaborates with content creation AI 106 for immersive ad production.

[00050] Referring to Fig. 1, advertising strategies utilizing diverse AI entities 100 is provided with collaborative campaign planning tool 142, which enables teams to strategize using data-driven insights and stakeholder input. This component aligns all campaign elements with overall business objectives. collaborative campaign planning tool 142 works with analytics dashboard 118 to display relevant insights and with predictive market trend analysis component 124 to adapt campaigns to emerging trends.

[00051] Referring to Fig 4, there is illustrated method 200 for advertising strategies utilizing diverse AI entities 100. The method comprises:
At step 202, method 200 includes data integration and analysis system 112 consolidating consumer data from various sources to create comprehensive profiles;
At step 204, method 200 includes Predictive Analytics AI 102 analyzing profiles to forecast behaviors and identify target audience segments for personalized campaigns;
At step 206, method 200 includes Natural Language Processing AI 104 assessing sentiment in consumer-generated content, adding emotional context to profile insights;
At step 208, method 200 includes content creation AI 106 producing personalized ad content based on Predictive Analytics AI 102 and sentiment insights from Natural Language Processing AI 104;
At step 210, method 200 includes a/b testing mechanism 116 testing content variations in real time, identifying top-performing ads for deployment;
At step 212, method 200 includes ad placement optimization AI 108 selecting optimal platforms, formats, and timings to maximize ad reach and engagement;
At step 214, method 200 includes geo-targeting module 130 customizing ad placements by location for enhanced relevance;
At step 216, method 200 includes multilingual and cross-cultural adaptation module 134 translating and culturally tailoring content for diverse audiences;
At step 218, method 200 includes customer engagement AI 110 facilitating direct consumer interaction through chatbots and virtual assistants;
At step 220, method 200 includes sentiment-driven content modification system 120 dynamically adjusting ad messaging based on real-time sentiment data;
At step 222, method 200 includes cross-channel attribution model 128 analyzing channel performance, optimizing ad spend for maximum ROI;
At step 224, method 200 includes predictive market trend analysis component 124 monitoring trends to provide strategic adjustments for campaigns;
At step 226, method 200 includes user journey mapping feature 132 tracking the consumer's journey to identify and optimize engagement points;
At step 228, method 200 includes cognitive load assessment component 136 adjusting ad complexity to match consumer comprehension levels;
At step 230, method 200 includes content reusability protocols 138 repurposing high-performing ad content to improve consistency and resource use;
At step 232, method 200 includes AR/VR integration module 140 providing immersive, interactive advertising experiences;
At step 234, method 200 includes analytics dashboard 118 visualizing data from all components for informed decision-making;
At step 236, method 200 includes ethical AI compliance module 114 ensuring compliance with privacy standards and transparency;
At step 238, method 200 includes collaborative campaign planning tool 142 enabling data-driven, strategic campaign planning aligned with business objectives.
[00052] In the description of the present invention, it is also to be noted that, unless otherwise explicitly specified or limited, the terms "fixed" "attached" "disposed," "mounted," and "connected" are to be construed broadly, and may for example be fixedly connected, detachably connected, or integrally connected, either mechanically or electrically. They may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases to those skilled in the art.

[00053] Modifications to embodiments of the present disclosure described in the foregoing are possible without departing from the scope of the present disclosure as defined by the accompanying claims. Expressions such as "including", "comprising", "incorporating", "have", "is" used to describe and claim the present disclosure are intended to be construed in a non- exclusive manner, namely allowing for items, components or elements not explicitly described also to be present. Reference to the singular is also to be construed to relate to the plural where appropriate.

[00054] Although embodiments have been described with reference to a number of illustrative embodiments thereof, it should be understood that numerous other modifications and embodiments can be devised by those skilled in the art that will fall within the spirit and scope of the principles of this disclosure. More particularly, various variations and modifications are possible in the component parts and/or arrangements of the subject combination arrangement within the scope of the present disclosure, the drawings and the appended claims. In addition to variations and modifications in the component parts and/or arrangements, alternative uses will also be apparent to those skilled in the art.
, Claims:WE CLAIM:
1. An advertising strategies utilizing diverse AI entities 100 comprising of
Predictive Analytics AI 102 to analyze consumer profiles and predict future behaviors for targeted campaigns;
Natural Language Processing AI 104 to assess sentiment in consumer-generated content for emotional insights;
content creation AI 106 to generate personalized ad content based on predictive and sentiment data;
ad placement optimization AI 108 to select optimal platforms, formats, and timings for ad placement;
customer engagement AI 110 to facilitate direct consumer interactions through chatbots and virtual assistants;
data integration and analysis system 112 to consolidate consumer data from various sources for comprehensive profiling;
ethical AI compliance module 114 to ensure AI and data usage comply with privacy standards and transparency;
a/b testing mechanism 116 to test and identify top-performing ad variations in real time;
analytics dashboard 118 to visualize data from all components for informed decision-making;


sentiment-driven content modification system 120 to adjust ad messaging dynamically based on real-time sentiment;
modular deployment architecture 122 to enable flexible integration and scalability of AI entities within the advertising system;
predictive market trend analysis component 124 to monitor market trends and suggest strategic adjustments;
conversational AI for enhanced consumer interaction 126 facilitating real-time, personalized communication with consumers through chatbots and virtual assistants;
cross-channel attribution model 128 to analyze and optimize ad spend across various advertising channels;
geo-targeting module 130 to customize ad placements by location for enhanced consumer relevance;
user journey mapping feature 132 to track and optimize key engagement points throughout the consumer journey;
multilingual and cross-cultural adaptation module 134 to translate and culturally tailor content for diverse audiences;
cognitive load assessment component 136 to adjust ad complexity for optimal consumer comprehension;
content reusability protocols 138 to identify and repurpose effective ad content for consistent messaging;
ar/vr integration module 140 to provide immersive, interactive advertising experiences through AR/VR; and
collaborative campaign planning tool 142 to enable strategic, data-driven campaign planning aligned with business objectives.
2. The advertising strategy system with diverse AI entities 100 as claimed in claim 1, wherein predictive analytics AI 102 is configured to analyze historical consumer data, predict purchasing behaviors, and identify target segments, enabling real-time campaign adjustments for enhanced targeting precision.

3. The advertising strategy system with diverse AI entities 100 as claimed in claim 1, wherein Natural Language Processing AI 104 is configured to assess consumer sentiment from social media and reviews, integrating emotional context into profiles to enhance personalization and resonance in advertising content.

4. The advertising strategy system with diverse AI entities 100 as claimed in claim 1, wherein content creation ai 106 is configured to generate personalized multimedia content dynamically based on real-time data from Predictive Analytics AI 102 and Natural Language Processing AI 104, enabling rapid and relevant content deployment.

5. The advertising strategy system with diverse AI entities 100 as claimed in claim 1, wherein ad placement optimization AI 108 is configured to select optimal ad platforms and formats based on real-time performance data, maximizing reach and engagement by dynamically adjusting placements.

6. The advertising strategy system with diverse AI entities 100 as claimed in claim 1, wherein a/b testing mechanism 116 is configured to automatically test multiple ad variations in real-time, selecting top-performing content based on engagement metrics to drive campaign effectiveness.

7. The advertising strategy system with diverse AI entities 100 as claimed in claim 1, wherein cross-channel attribution model 128 is configured to evaluate the effectiveness of multiple advertising channels, allowing for optimized ad spend allocation based on data-driven insights into channel performance.

8. The advertising strategy system with diverse AI entities 100 as claimed in claim 1, wherein sentiment-driven content modification system 120 is configured to dynamically adjust ad messaging in real-time according to consumer sentiment analysis, maintaining positive brand perception and message relevance.

9. The advertising strategy system with diverse AI entities 100 as claimed in claim 1, wherein analytics dashboard 118 is configured to consolidate data from all AI components, providing actionable insights and visualizations to enable informed decision-making and continuous campaign optimization.

10. The systems and methods for AI-driven ride vehicle operations 100 as claimed, wherein method comprises of
data integration and analysis system 112 consolidating consumer data from various sources to create comprehensive profiles;
Predictive Analytics AI 102 analyzing profiles to forecast behaviors and identify target audience segments for personalized campaigns;
Natural Language Processing AI 104 assessing sentiment in consumer-generated content, adding emotional context to profile insights;
content creation AI 106 producing personalized ad content based on Predictive Analytics AI 102 and sentiment insights from Natural Language Processing AI 104;
a/b testing mechanism 116 testing content variations in real time, identifying top-performing ads for deployment;
ad placement optimization AI 108 selecting optimal platforms, formats, and timings to maximize ad reach and engagement;
geo-targeting module 130 customizing ad placements by location for enhanced relevance;
multilingual and cross-cultural adaptation module 134 translating and culturally tailoring content for diverse audiences;
customer engagement AI 110 facilitating direct consumer interaction through chatbots and virtual assistants;
sentiment-driven content modification system 120 dynamically adjusting ad messaging based on real-time sentiment data;
cross-channel attribution model 128 analyzing channel performance, optimizing ad spend for maximum ROI;
predictive market trend analysis component 124 monitoring trends to provide strategic adjustments for campaigns;
user journey mapping feature 132 tracking the consumer's journey to identify and optimize engagement points;
cognitive load assessment component 136 adjusting ad complexity to match consumer comprehension levels;
content reusability protocols 138 repurposing high-performing ad content to improve consistency and resource use;
AR/VR integration module 140 providing immersive, interactive advertising experiences;
analytics dashboard 118 visualizing data from all components for informed decision-making;
ethical AI compliance module 114 ensuring compliance with privacy standards and transparency; and
collaborative campaign planning tool 142 enabling data-driven, strategic campaign planning aligned with business objectives.

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