SYSTEMS AND METHODS FOR AI-BASED DIGITAL CONTENT SCALING
Disclosed are systems and methods that provide a decision-intelligence (DI)-based, computerized framework for demand-side platforms (DSPs) to effectively, plan, launch, optimize and monitor the performance of content campaigns. The disclosed framework operates to perform strategic and data-driven processes for DSP initiatives that can define campaign parameters, and in real-time, monitor the effectiveness of campaigns such that their modifications and/or alterations can be dynamically performed so as to adapt to the changing landscapes of how the campaign is being disseminated over a network and received by users. Accordingly, the framework's mechanisms are implemented for new and/or existing campaigns, across various platforms and/or websites, in order to optimize visibility while increasing user experience, which benefits both DSPs as well as the targeted audience. The framework can implement AI/ML and/or LLM models and functionality to provide a comprehensive approach to managing, curating and analyzing content campaigns for optimal performance and impact.
1 . A method comprising:
identifying, by a device, a data structure on a platform that comprises at least one intended user action and a plurality of implementation parameters;
determining, by the device, a set of facets related to the data structure, the set of facets corresponding to intended activity related to the data structure on network;
providing, by the device, an input to a large language model (LLM), the input comprising text corresponding to the set of facets;
generating, by the device, via the LLM, an LLM output, the LLM output comprising information related to attributes indicating the activity related to the data structure;
curating, by the device, based on the LLM output, the data structure, the curation comprising:
analyzing, by a machine learning (ML) model, the attributes from the LLM output; and
determining an effectiveness of the data structure, wherein the curation of the data structure is based on the determined effectiveness;
determining whether the effectiveness of the data structure satisfies a performance threshold, wherein:
when the performance threshold is satisfied, the curation comprises maintaining the data structure in its current form, and
when the performance threshold is not satisfied, the curation comprises modifying the data structure; and
communicating, over the network, the curated data structure to a set of network resources.
2 . (canceled)
3 . (canceled)
4 . The method of claim 1 , wherein the modification of the data structure comprises at least one of adding content, removing content, changing content format, changing target information, changing frequency, changing timing, changing location, and changing platforms.
5 . The method of claim 1 , wherein when the data structure is an existing, launched data structure on the network, the determined effectiveness corresponds to a realized effectiveness.
6 . The method of claim 1 , wherein when the data structure is a new data structure to the network, the determined effectiveness corresponds to a predicted effectiveness.
7 . The method of claim 1 , further comprising:
retrieving data from a previous data structure that implemented parameters from within the plurality of implementation parameters, wherein the set of facets determination is further based on the previous data structure.
8 . The method of claim 1 , wherein the data structure corresponds to a content campaign from a demand side platform (DSP), wherein the at least one intended user action corresponds to an interaction with content associated with the content campaign.
9 . A device comprising:
a processor configured to:
identify a data structure on a platform that comprises at least one intended user action and a plurality of implementation parameters;
determine a set of facets related to the data structure, the set of facets corresponding to intended activity related to the data structure on network;
provide an input to a large language model (LLM), the input comprising text corresponding to the set of facets;
generate, via the LLM, an LLM output, the LLM output comprising information related to attributes indicating the activity related to the data structure;
curate, based on the LLM output, the data structure, the curation further causing the processor to:
analyze, by a machine learning (ML) model, the attributes from the LLM output; and
determine an effectiveness of the data structure, wherein the curation of the data structure is based on the determined effectiveness;
determine whether the effectiveness of the data structure satisfies a performance threshold, wherein:
when the performance threshold is satisfied, the curation comprises maintaining the data structure in its current form, and
when the performance threshold is not satisfied, the curation comprises modifying the data structure; and
communicate, over the network, the curated data structure to a set of network resources.
10 . (canceled)
11 . (canceled)
12 . The device of claim 9 , wherein the modification of the data structure comprises at least one of adding content, removing content, changing content format, changing target information, changing frequency, changing timing, changing location, and changing platforms.
13 . The device of claim 9 , wherein when the data structure is an existing, launched data structure on the network, the determined effectiveness corresponds to a realized effectiveness, and wherein when the data structure is a new data structure to the network, the determined effectiveness corresponds to a predicted effectiveness.
14 . The device of claim 9 , wherein the processor is further configured to:
retrieve data from a previous data structure that implemented parameters from within the plurality of implementation parameters, wherein the set of facets determination is further based on the previous data structure.
15 . A non-transitory computer-readable storage medium tangibly encoded with computer-executable instructions that when executed by a device, perform a method comprising:
identifying, by the device, a data structure on a platform that comprises at least one intended user action and a plurality of implementation parameters;
determining, by the device, a set of facets related to the data structure, the set of facets corresponding to intended activity related to the data structure on network;
providing, by the device, an input to a large language model (LLM), the input comprising text corresponding to the set of facets;
generating, by the device, via the LLM, an LLM output, the LLM output comprising information related to attributes indicating the activity related to the data structure;
curating, by the device, based on the LLM output, the data structure, the curation comprising:
analyzing, by a machine learning (ML) model, the attributes from the LLM output; and
determining an effectiveness of the data structure, wherein the curation of the data structure is based on the determined effectiveness;
determining whether the effectiveness of the data structure satisfies a performance threshold, wherein:
when the performance threshold is satisfied, the curation comprises maintaining the data structure in its current form, and
when the performance threshold is not satisfied, the curation comprises modifying the data structure; and
communicating, over the network, the curated data structure to a set of network resources.
16 . (canceled)
17 . (canceled)
18 . The non-transitory computer-readable storage medium of claim 15 , wherein the modification of the data structure comprises at least one of adding content, removing content, changing content format, changing target information, changing frequency, changing timing, changing location, and changing platforms.
19 . The non-transitory computer-readable storage medium of claim 15 , wherein when the data structure is an existing, launched data structure on the network, the determined effectiveness corresponds to a realized effectiveness, and wherein when the data structure is a new data structure to the network, the determined effectiveness corresponds to a predicted effectiveness.
20 . The non-transitory computer-readable storage medium of claim 15 , further comprising:
retrieving data from a previous data structure that implemented parameters from within the plurality of implementation parameters, wherein the set of facets determination is further based on the previous data structure.