IP Library Granted Patent US 11,102,530
Granted Patent B2
US 11,102,530 · App. 17/006,524 · Granted Aug 24, 2021

Adaptive processing and content control system

Inventors: Krishna James Kannan (Boston, MA); Nathan R. Walkingshaw (Sandy, UT); Gilbert Gomez Lee (Draper, UT)
Assignee: PLURALSIGHT LLC
H04N21/251G06F16/739G06K9/6219G06K9/6256H04N21/2181H04N21/2381
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Quick Facts
Patent No.
US 11,102,530
App. No.
17/006,524
Granted
Aug 24, 2021
Kind
B2
Abstract

Systems, methods, and non-transitory, machine-readable media to facilitate adaptive processing and content control are disclosed. Content composites may be created and configured according to a computational model that may include a hierarchical ordering of the content composites using a hierarchical data structure. The configured content composites may be presented with a graphical user interface of an endpoint device. Metrics of interactions with interface elements corresponding to the configured content composites may be determined using a processing device that monitors inputs. The computational model may be automatically trained using the metrics of interactions to create an adapted computational model. Adapted content composites may be created and configured according to the adapted computational model that may include a second hierarchical ordering using a second hierarchical data structure. The adapted content composites may be presented with the graphical user interface.

Claims (85)

1. A system to facilitate adaptive processing and content control, the system comprising:

one or more processing devices; and

memory readable by the one or more processing devices and having stored therein processor-readable instructions which, when executed by the one or more processing devices, cause the one or more processing devices to perform operations comprising:

collecting content composites in a data storage, where:

each content composite comprises audio and/or visual content; and

the collecting comprises:

one or both of receiving one or more content composites via a network interface and creating one or more content composites, and

storing the one or more content composites in the data storage;

based at least in part on the content composites, creating a configured set of content composites configured according to a computational model that comprises a hierarchical ordering of the set of content composites using a hierarchical data structure and to be presented with a graphical user interface;

causing presentation of at least part of the configured set of content composites with a graphical user interface of an endpoint device;

determining, using a processing device that monitors inputs, metrics of interactions with interface elements corresponding to the at least part of the configured set of content composites surfaced with the graphical user interface;

automatically training the computational model using the metrics of interactions to create an adapted computational model;

determining one or more baselines based at least in part on pattern recognition of previous metrics of interactions mapped to the at least part of the configured set of content composites;

determining one or more variances based at least in part on pattern recognition of the metrics of interactions mapped to the at least part of the configured set of content composite with respect to at least one baseline of normality of the one or more baselines of normality;

creating an adapted set of content composites configured according to the adapted computational model that comprises one or both of a second hierarchical ordering of the adapted set of content composites using a second hierarchical data structure and specifications of one or more content composites that are different from the configured set of content composites, where the adapted set of content composites is based at least in part on the one or more variances; and

causing presentation of at least part of the adapted set of content composites with one or both of the graphical user interface of the endpoint device and a second graphical user interface of a second endpoint device.

2. The system to facilitate adaptive processing and content control as recited in claim 1 , where the determining the metrics of interactions is based at least in part on:

collecting electronic communications received via a network, the electronic communications comprising data portions from a plurality of electronic devices;

processing the data portions to identify indicia of usage of one or more of the content composites; and

mapping at least a subset of the indicia to one or more specifications of one or more nodes in a network of nodes.

3. The system to facilitate adaptive processing and content control as recited in claim 1 , where the determining the metrics of interactions is based at least in part on:

collecting electronic communications received via a network, the electronic communications comprising data portions from a plurality of electronic devices;

processing the data portions using a natural language processing model to identify indicia of inputs in the data portions;

collecting the indicia of inputs and matching at least a subset of the indicia of inputs as sharing identical and/or similar components; and

mapping at least the subset of the indicia of inputs to one or more specifications of one or more nodes in a network of nodes.

4. The system to facilitate adaptive processing and content control as recited in claim 1 , the operations further comprising:

modifying a network data structure of nodes linked in a network of nodes, the network of nodes configured so that each node of the network of nodes comprises respective linked content, the respective linked content comprising node specifications corresponding to at least part of the adapted set of content composites.

5. The system to facilitate adaptive processing and content control as recited in claim 4 , the operations further comprising:

creating a graphical representation formatted to at least partially represent the network data structure of nodes linked in the network of nodes, where each represented node of the graphical representation comprises one or more selectable links to access underlying data corresponding to one of the nodes.

6. The system to facilitate adaptive processing and content control as recited in claim 5 , the operations further comprising:

causing presentation of visualization data corresponding to the graphical representation to the endpoint device to facilitate the graphical user interface and access to the graphical representation of at least part of the network data structure.

7. The system to facilitate adaptive processing and content control as recited in claim 6 , where the underlying data comprises one or both of the respective node specifications and one or more content composites corresponding to the node.

8. A method to facilitate adaptive processing and content control, the method comprising:

collecting content composites in a data storage, where:

each content composite comprises audio and/or visual content; and

the collecting comprises:

one or both of receiving one or more content composites via a network interface and creating one or more content composites, and

storing the one or more content composites in the data storage;

based at least in part on the content composites, creating a configured set of content composites configured according to a computational model that comprises a hierarchical ordering of the set of content composites using a hierarchical data structure and to be presented with a graphical user interface;

causing presentation of at least part of the configured set of content composites with a graphical user interface of an endpoint device;

determining, using a processing device that monitors inputs, metrics of interactions with interface elements corresponding to the at least part of the configured set of content composites surfaced with the graphical user interface;

automatically training the computational model using the metrics of interactions to create an adapted computational model;

determining one or more baselines based at least in part on pattern recognition of previous metrics of interactions mapped to the at least part of the configured set of content composites;

determining one or more variances based at least in part on pattern recognition of the metrics of interactions mapped to the at least part of the configured set of content composite with respect to at least one baseline of normality of the one or more baselines of normality;

creating an adapted set of content composites configured according to the adapted computational model that comprises one or both of a second hierarchical ordering of the adapted set of content composites using a second hierarchical data structure and specifications of one or more content composites that are different from the configured set of content composites, where the adapted set of content composites is based at least in part on the one or more variances; and

causing presentation of at least part of the adapted set of content composites with one or both of the graphical user interface of the endpoint device and a second graphical user interface of a second endpoint device.

9. The method to facilitate adaptive processing and content control as recited in claim 8 , where the determining the metrics of interactions is based at least in part on:

collecting electronic communications received via a network, the electronic communications comprising data portions from a plurality of electronic devices;

processing the data portions to identify indicia of usage of one or more of the content composites; and

mapping at least a subset of the indicia to one or more specifications of one or more nodes in a network of nodes.

10. The method to facilitate adaptive processing and content control as recited in claim 8 , where the determining the metrics of interactions is based at least in part on:

collecting electronic communications received via a network, the electronic communications comprising data portions from a plurality of electronic devices;

processing the data portions using a natural language processing model to identify indicia of inputs in the data portions;

collecting the indicia of inputs and matching at least a subset of the indicia of inputs as sharing identical and/or similar components; and

mapping at least the subset of the indicia of inputs to one or more specifications of one or more nodes in a network of nodes.

11. The method to facilitate adaptive processing and content control as recited in claim 8 , the method further comprising:

modifying a network data structure of nodes linked in a network of nodes, the network of nodes configured so that each node of the network of nodes comprises respective linked content, the respective linked content comprising node specifications corresponding to at least part of the adapted set of content composites.

12. The method to facilitate adaptive processing and content control as recited in claim 11 , the method further comprising:

creating a graphical representation formatted to at least partially represent the network data structure of nodes linked in the network of nodes, where each represented node of the graphical representation comprises one or more selectable links to access underlying data corresponding to one of the nodes.

13. The method to facilitate adaptive processing and content control as recited in claim 12 , the method further comprising:

causing presentation of visualization data corresponding to the graphical representation to the endpoint device to facilitate the graphical user interface and access to the graphical representation of at least part of the network data structure.

14. The method to facilitate adaptive processing and content control as recited in claim 13 , where the underlying data comprises one or both of the respective node specifications and one or more content composites corresponding to the node.

15. One or more non-transitory, machine-readable media having machine-readable instructions thereon which, when executed by one or more processing devices, cause the one or more processing devices to perform operations comprising:

collecting content composites in a data storage, where:

each content composite comprises audio and/or visual content; and

the collecting comprises:

one or both of receiving one or more content composites via a network interface and creating one or more content composites, and

storing the one or more content composites in the data storage;

based at least in part on the content composites, creating a configured set of content composites configured according to a computational model that comprises a hierarchical ordering of the set of content composites using a hierarchical data structure and to be presented with a graphical user interface;

causing presentation of at least part of the configured set of content composites with a graphical user interface of an endpoint device;

determining, using a processing device that monitors inputs, metrics of interactions with interface elements corresponding to the at least part of the configured set of content composites surfaced with the graphical user interface;

automatically training the computational model using the metrics of interactions to create an adapted computational model;

determining one or more baselines based at least in part on pattern recognition of previous metrics of interactions mapped to the at least part of the configured set of content composites;

determining one or more variances based at least in part on pattern recognition of the metrics of interactions mapped to the at least part of the configured set of content composite with respect to at least one baseline of normality of the one or more baselines of normality;

creating an adapted set of content composites configured according to the adapted computational model that comprises one or both of a second hierarchical ordering of the adapted set of content composites using a second hierarchical data structure and specifications of one or more content composites that are different from the configured set of content composites, where the adapted set of content composites is based at least in part on the one or more variances; and

causing presentation of at least part of the adapted set of content composites with one or both of the graphical user interface of the endpoint device and a second graphical user interface of a second endpoint device.

16. The one or more non-transitory, machine-readable media as recited in claim 15 , where the determining the metrics of interactions is based at least in part on:

collecting electronic communications received via a network, the electronic communications comprising data portions from a plurality of electronic devices;

processing the data portions to identify indicia of usage of one or more of the content composites; and

mapping at least a subset of the indicia to one or more specifications of one or more nodes in a network of nodes.

17. The one or more non-transitory, machine-readable media as recited in claim 15 , where the determining the metrics of interactions is based at least in part on:

collecting electronic communications received via a network, the electronic communications comprising data portions from a plurality of electronic devices;

processing the data portions using a natural language processing model to identify indicia of inputs in the data portions;

collecting the indicia of inputs and matching at least a subset of the indicia of inputs as sharing identical and/or similar components; and

mapping at least the subset of the indicia of inputs to one or more specifications of one or more nodes in a network of nodes.

Assignments (7)
RELEASE OF SECURITY INTEREST Recorded Aug 23, 2024
From: VEPF LAKE, LP
To: MULTIVISION NEWCO LLC
Reel/Frame 068389/0089 →
SECURITY INTEREST Recorded Aug 22, 2024
From: MULTIVISION NEWCO LLC
To: BLUE OWL CAPITAL CORPORATION
Reel/Frame 068373/0371 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Apr 11, 2024
From: PLURALSIGHT, LLC
To: MULTIVISION NEWCO LLC
Reel/Frame 067159/0352 →
SECURITY INTEREST Recorded Apr 11, 2024
From: MULTIVISION NEWCO LLC
To: VEPF LAKE, LP
Reel/Frame 067075/0949 →
AFFIDAVIT TO AFFIRM THE RELEASE OF SECURITY AGREEMENT Recorded Apr 11, 2024
From: OWL ROCK CAPITAL CORPORATION
To: PLURALSIGHT HOLDINGS, LLC; PLURALSIGHT, LLC
Reel/Frame 067077/0759 →
SECURITY INTEREST Recorded Apr 7, 2021
From: PLURALSIGHT HOLDINGS, LLC; PLURALSIGHT, LLC
To: OWL ROCK CAPITAL CORPORATION
Reel/Frame 055851/0834 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Nov 24, 2020
From: KANNAN, KRISHNA JAMES; WALKINGSHAW, NATHAN R.; LEE, GILBERT GOMEZ
To: PLURALSIGHT LLC
Reel/Frame 054463/0155 →
Continuity (4)
Continuation 17003956 · Aug 26, 2020
Provisional Application 62893038 · Aug 28, 2019
Provisional Application 62891824 · Aug 26, 2019
Related Publication 20210105518A1 · Apr 8, 2021