US-20260127633-A1 - SYSTEMS AND METHODS FOR OPTIMIZING ADVERTISEMENTS IN A TIERED SOFTWARE FRAMEWORK

US20260127633A1US 20260127633 A1US20260127633 A1US 20260127633A1US-20260127633-A1

Abstract

An application for optimizing advertisements in a tiered software framework is disclosed. The application may receive a request to optimize an advertisement. The application may predict an engagement rate of the advertisement and encode in a latent space using a machine model, an embedding from the advertisement, the latent space comprising embeddings encoded from previously published advertisements having corresponding actual engagement rates. The application may identify clusters in the latent space. The application may iteratively derive an optimized embedding based on purposeful movements in the latent space towards selected ones of the clusters, the purposeful movements being based at least on respective distances of the selected clusters from a candidate embedding derived in a preceding iteration. The application may generate a final advertisement from the optimized embedding, and publish it suitably.

Inventors

Hardik Bhatt
Karan Agarwal
Shaun Clark
Robin Alex
Varun Vairavan

Assignees

HighLevel Inc.

Dates

Publication Date: 20260507
Application Date: 20241104

Claims (20)

1 . A method for optimizing advertisements in a tiered software framework, the method comprising: receiving a request to optimize an advertisement, wherein: the request is received at one tier from another tier of a tiered software framework, and the tiered software framework comprises a plurality of tiers differentiated by access management policies; encoding in a latent space using a machine model, an embedding of the advertisement, the latent space comprising other embeddings of previously published advertisements having corresponding actual engagement rates; identifying clusters in the latent space; starting with the embedding of the advertisement, iteratively deriving an optimized embedding based at least on: (i) purposeful movements in the latent space towards selected ones of the clusters, and (ii) predicted engagement rates of candidate embeddings derived from the purposeful movements, wherein the purposeful movements are based at least on relative differences in engagement rates and respective distances of the selected clusters from candidate embeddings derived in preceding iterations; generating a final advertisement from the optimized embedding, the final advertisement generated at the one tier; and publishing the final advertisement at the another tier.
2 . The method of claim 1 , wherein iteratively deriving the optimized embedding comprises: setting the embedding as a first embedding; predicting a first engagement rate of the first advertisement; executing a sequence of steps, each purposeful movement comprising one sequence, the sequence comprising: calculating, for each cluster: (i) a distance between the first embedding and a centroid of the cluster, (ii) a difference in the first engagement rate and an average engagement rate of the cluster, and (iii) a gradient as a ratio of the difference to the distance; selecting one of the clusters having a larger gradient than other clusters; modifying the first embedding into a second embedding according to attributes of the selected cluster; predicting a second engagement rate of the second embedding; and responsive to determining that the second engagement rate is less than at least one of: (i) the first engagement rate or (ii) a predetermined engagement rate threshold: resetting the second embedding as the first embedding, and resetting the second engagement rate as the first engagement rate; and repeatedly executing the sequence of steps until the second engagement rate is not less than at least one of: (i) the first engagement rate or (ii) the predetermined engagement rate threshold.
3 . The method of claim 2 , wherein modifying the first embedding comprises altering parameter values in the first embedding by a scale factor proportional to the gradient calculated for the selected cluster.
4 . The method of claim 2 , wherein: the first advertisement in the request comprises an original image, the method further comprises, in each sequence of steps: decoding the second embedding into a second advertisement; and calculating a similarity metric between a generated image in the second advertisement and the original image, and resetting the second embedding and the second engagement rate is responsive further to determining that the similarity metric is below a predetermined similarity threshold.
5 . The method of claim 1 , wherein: the request originates from an account at the another tier of the tiered software framework, the another tier comprises multiple accounts, and the access management policies specify that data from the one tier is not accessible at the another tier, data from the another tier is accessible at the one tier, and data in one account is not accessible by another account.
6 . The method of claim 5 , wherein the previously published advertisements are associated with different accounts at the another tier.
7 . The method of claim 1 , wherein the engagement rate comprises click-through-rate (CTR).
8 . Non-transitory computer-readable tangible media that includes instructions for execution, which when executed by a processor of a computing device, is operable to perform operations comprising: receiving a request to optimize an advertisement, wherein: the request is received at one tier from another tier of a tiered software framework, and the tiered software framework comprises a plurality of tiers differentiated by access management policies; encoding in a latent space using a machine model, an embedding of the advertisement, the latent space comprising other embeddings of previously published advertisements having corresponding actual engagement rates; identifying clusters in the latent space; starting with the embedding of the advertisement, iteratively deriving an optimized embedding based at least on: (i) purposeful movements in the latent space towards selected ones of the clusters, and (ii) predicted engagement rates of candidate embeddings derived from the purposeful movements, wherein the purposeful movements are based at least on relative differences in engagement rates and respective distances of the selected clusters from candidate embeddings derived in preceding iterations; generating a final advertisement from the optimized embedding, the final advertisement generated at the one tier; and publishing the final advertisement at the another tier.
9 . The non-transitory computer-readable tangible media of claim 8 , wherein iteratively deriving the optimized embedding comprises: setting the embedding as a first embedding; predicting a first engagement rate of the first advertisement; executing a sequence of steps, each purposeful movement comprising one sequence, the sequence comprising: calculating, for each cluster: (i) a distance between the first embedding and a centroid of the cluster, (ii) a difference in the first engagement rate and an average engagement rate of the cluster, and (iii) a gradient as a ratio of the difference to the distance; selecting one of the clusters having a larger gradient than other clusters; modifying the first embedding into a second embedding according to attributes of the selected cluster; predicting a second engagement rate of the second embedding; and responsive to determining that the second engagement rate is less than at least one of: (i) the first engagement rate or (ii) a predetermined engagement rate threshold: resetting the second embedding as the first embedding, and resetting the second engagement rate as the first engagement rate; and repeatedly executing the sequence of steps until the second engagement rate is not less than at least one of: (i) the first engagement rate or (ii) the predetermined engagement rate threshold.
10 . The non-transitory computer-readable tangible media of claim 9 , wherein modifying the first embedding comprises altering parameter values in the first embedding by a scale factor proportional to the gradient calculated for the selected cluster.
11 . The non-transitory computer-readable tangible media of claim 9 , wherein: the first advertisement in the request comprises an original image, the operations further comprise, in each sequence of steps: decoding the second embedding into a second advertisement; and calculating a similarity metric between a generated image in the second advertisement and the original image, and resetting the second embedding, and the second engagement rate is responsive further to determining that the similarity metric is below a predetermined similarity threshold.
12 . The non-transitory computer-readable tangible media of claim 8 , wherein: the request originates from an account at the another tier of the tiered software framework, the another tier comprises multiple accounts, and the access management policies specify that data from the one tier is not accessible at the another tier, data from the another tier is accessible at the one tier, and data in one account is not accessible by another account.
13 . The non-transitory computer-readable tangible media of claim 12 , wherein the previously published advertisements are associated with different accounts at the another tier.
14 . The non-transitory computer-readable tangible media of claim 8 , wherein the engagement rate comprises click-through-rate (CTR).
15 . An apparatus comprising: a processing circuitry; a memory storing data; and a communication circuitry, wherein the processing circuitry executes instructions associated with the data, the processing circuitry is coupled to the communication circuitry and the memory, and the processing circuitry and the memory cooperate, such that the apparatus is configured for: receiving a request to optimize an advertisement, wherein: the request is received at one tier from another tier of a tiered software framework, and the tiered software framework comprises a plurality of tiers differentiated by access management policies; encoding in a latent space using a machine model, an embedding of the advertisement, the latent space comprising other embeddings of previously published advertisements having corresponding actual engagement rates; identifying clusters in the latent space; starting with the embedding of the advertisement, iteratively deriving an optimized embedding based at least on: (i) purposeful movements in the latent space towards selected ones of the clusters, and (ii) predicted engagement rates of candidate embeddings derived from the purposeful movements, wherein the purposeful movements are based at least on relative differences in engagement rates and respective distances of the selected clusters from candidate embeddings derived in preceding iterations; generating a final advertisement from the optimized embedding, the final advertisement generated at the one tier; and publishing the final advertisement at the another tier.
16 . The apparatus of claim 15 , wherein iteratively deriving the optimized embedding comprises: setting the embedding as a first embedding; predicting a first engagement rate of the first advertisement; executing a sequence of steps, each purposeful movement comprising one sequence, the sequence comprising: calculating, for each cluster: (i) a distance between the first embedding and a centroid of the cluster, (ii) a difference in the first engagement rate and an average engagement rate of the cluster, and (iii) a gradient as a ratio of the difference to the distance; selecting one of the clusters having a larger gradient than other clusters; modifying the first embedding into a second embedding according to attributes of the selected cluster; predicting a second engagement rate of the second embedding; and responsive to determining that the second engagement rate is less than at least one of: (i) the first engagement rate or (ii) a predetermined engagement rate threshold: resetting the second embedding as the first embedding, and resetting the second engagement rate as the first engagement rate; and repeatedly executing the sequence of steps until the second engagement rate is not less than at least one of: (i) the first engagement rate or (ii) the predetermined engagement rate threshold.
17 . The apparatus of claim 16 , wherein modifying the first embedding comprises altering parameter values in the first embedding by a scale factor proportional to the gradient calculated for the selected cluster.
18 . The apparatus of claim 16 , wherein: the first advertisement in the request comprises an original image, the apparatus is further configured for: in each sequence of steps: decoding the second embedding into a second advertisement; and calculating a similarity metric between a generated image in the second advertisement and the original image, and resetting the second embedding, and the second engagement rate is responsive further to determining that the similarity metric is below a predetermined similarity threshold.
19 . The apparatus of claim 15 , wherein: the request originates from an account at the another tier of the tiered software framework, the another tier comprises multiple accounts, and the access management policies specify that data from the one tier is not accessible at the another tier, data from the another tier is accessible at the one tier, and data in one account is not accessible by another account.
20 . The apparatus of claim 19 , wherein the previously published advertisements are associated with different accounts at the another tier.

Description

TECHNICAL FIELD The present disclosure relates to systems, techniques, and methods directed to systems and methods for optimizing advertisements in a tiered software framework. BACKGROUND Artificial intelligence (AI) is a growing field in computer science that uses machine learning models to make predictions, recommendations, or classifications based on input data. Revenue from the AI software market worldwide is expected to reach 126 billion dollars by 2025 according to some estimates. In some domains, such as marketing, AI has the potential to significantly impact the delivery of marketing services using behavioral analysis, pattern recognition, and other learning algorithms. BRIEF DESCRIPTION OF THE DRAWINGS Embodiments will be readily understood by the following detailed description in conjunction with the accompanying drawings. To facilitate this description, like reference numerals designate like elements. Embodiments are illustrated by way of example, and not by way of limitation, in the figures of the accompanying drawings. FIG. 1 is a simplified block diagram illustrating an example system for optimizing advertisements in a tiered software framework. FIG. 2 is a simplified block diagram illustrating an example tiered software framework in an example system for optimizing advertisements. FIG. 3 is a simplified block diagram illustrating example details of an example tiered software framework in an example system for optimizing advertisements. FIG. 4 is a simplified diagram illustrating example details of an example system for optimizing advertisements in a tiered software framework. FIGS. 5A-5B are simplified diagrams illustrating example details of an example system for optimizing advertisements in a tiered software framework. FIG. 6 is a simplified block diagram illustrating example details of an example system for optimizing advertisements in a tiered software framework. FIG. 7 is a simplified diagram illustrating example details of an example system for optimizing advertisements in a tiered software framework. FIG. 8 is a simplified flow diagram illustrating example operations associated with an example method for optimizing advertisements in a tiered software framework. FIGS. 9A-9B are simplified flow diagrams illustrating example operations associated with an example method for optimizing advertisements in a tiered software framework. FIG. 10 is a simplified flow diagram illustrating example operations associated with an example method for optimizing advertisements in a tiered software framework. DETAILED DESCRIPTION Overview For purposes of illustrating the embodiments described herein, it is important to understand certain terminology and operations of technology networks. The following foundational information may be viewed as a basis from which the present disclosure may be properly explained. Such information is offered for purposes of explanation only and, accordingly, should not be construed in any way to limit the broad scope of the present disclosure and its potential applications. Modern technological advancements in AI have enhanced the efficiency of some manual tasks. AI uses machine learning models to make predictions, recommendations, and classifications. In general, machine learning models use algorithms to parse data, learn from the parsed data, and make informed decisions based on what has been learned. According to some classifications, deep learning models are subsets of machine learning models, being machine learning algorithms that operate in multiple layers, creating an artificial neural network. According to some other classifications, machine learning models are those that rely on human intervention to learn, whereas deep learning models automatically learn without human intervention. Because the learning algorithms are more relevant to the disclosure herein than any human intervention to provide training data, the former classification is employed herein, such that wherever “machine learning models” is used, it is intended that deep learning models are included as well. Deep learning models, in particular, enable AI algorithms such as generative AI models (e.g., ChatGPT™). In a general sense, AI algorithms have three qualities that differentiate them from other algorithms: intentionality, intelligence, and adaptability. As intentional algorithms, they make decisions, often using real-time data, combining information from a variety of different sources, analyzing the combined information instantly, and acting on insights derived from such data. As intelligent algorithms, they are capable of spotting patterns in underlying data. As adaptable algorithms, they learn and adapt their analyses based on shifting input data. Recent trends in AI technology include commercially available AI engines that expose application programming interfaces (APIs) for other applications to consume. In a general sense, the API is a set of rules and protocols that defines how two software syste