# US 12,223,525 B2 · System and method of providing a platform for managing data content campaign on social networks

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- Number: US 12,223,525 B2
- Document ID: US12223525B2
- Status: Granted
- Kind: B2
- Application number: 18/072,678
- Filed: 2022-11-30
- Published: 2023-09-07
- Granted: 2025-02-11
- Earliest claimed: 2017-04-28
- Assignees: Khoros, LLC
- Applicants: Khoros, LLC
- Inventors: Allison Savage; Morten Moeller; Phu Nguyen; Gouning Hu; Nemanja Spasojevic
- Web page: https://patentgrep.com/patent/US12223525B2

## CPC

- G06Q 30/0255: {based on user history}
- G06N 5/022: Knowledge engineering; Knowledge acquisition
- G06N 20/00: Machine learning
- G06Q 10/40: Business processes related to social networking or social networking services
- G06Q 10/48: using social graphs
- G06Q 30/0201: Market modelling; Market analysis; Collecting market data
- G06Q 30/0244: {Optimization}
- H04L 51/52: for supporting social networking services
- H04L 67/55: Push-based network services
- G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- H04L 51/214: using selective forwarding
- G06Q: INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M)

## Abstract

A computer-implemented system including a data store: a content items database, a user account database, and one or more servers configured to execute a computer program to perform one or more of the following: receiving content items from content database that are associated with a topic selected by a user for posting on a social network, wherein at least one content item is associated with an URL; estimating a post to a reaction filter for a time interval for the social network for the user, calculating a reaction profile associated with reactions to posts on the social network by aggregating reaction time of a plurality of users on the social network for one or more content items posted on the social network; determining a schedule for posting the content items on the social network as a function of the post to reaction filter and reaction profile.

## Claims

### Claim 1

A method comprising:
receiving a request for an interval of time during which to post a content item on a social network in a plurality of social networks, the interval of time being divided into a first subset of units of time;
creating one or more transformation functions implementing a machine learning engine to profile data based on the social network as a constituent of a first social network against a second social network;
executing instructions at the one or more servers to redefine the interval of time to include a second subset of units of time;
calculating the schedule for posting a content item on the social network and the post-to-reaction filter based on the second subset of units of time;
modeling a decay function as a function of the post-to-reaction filter, the decay function representative of a level of interest of an audience of the user associated with one or more of the first social network and the second social network;
determining an optimal time to post the content item on the social network as a function of the decay function;
recomputing the schedule as a function of the decay function and received schedule for posting the content item on the social network; and
auto-scheduling the posting of the content item to automatically post the content item based on the decay function, wherein auto-scheduling includes one or more of estimating data values representing audience reaction and data values representing domain specific schedule personalization.

### Claim 2 (depends on 1)

The method of claim 1, wherein the units of time each is a bucket.

### Claim 3 (depends on 1)

The method of claim 1, wherein the executing instructions to redefine the interval of time to include the second subset of units of time comprises:
redefining the interval of time with greater granularity.

### Claim 4 (depends on 1)

The method of claim 1, wherein modeling the decay function comprises:
executing instructions to predict a weighted value of the post-to-reaction filter.

### Claim 5 (depends on 1)

The method of claim 1, further comprising:
executing instructions to use the post-to-reaction filter to calculate a delayed reaction profile.

### Claim 6 (depends on 1)

The method of claim 1, further comprising:
estimating the transformation function based on post-to-reaction filter data generated at the post-to-reaction filter to transform the schedule for posting the content item to the social network into another schedule for posting the content item to another social network.

### Claim 7 (depends on 1)

The method of claim 1, wherein receiving the request for the interval of time during which the content item is posted comprises:
receiving the request to post multiple content items.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
determining another optimal time to post a second content item on the social network as a function of the decay function; and
recomputing the schedule as a function of the decay function and received schedule for posting the second content item on the social network.

### Claim 9 (depends on 8)

The method of claim 8, further comprising:
repeating determining yet another optimal time to post a third content item on the social network as a function of the decay function; and
recomputing the schedule as a function of the decay function and received schedule for posting the content item and the second content item on the social network.

### Claim 10 (depends on 1)

The method of claim 1, further comprising:
providing a platform for managing a campaign for publishing multiple content items on the social network to increase reactions to published content.

### Claim 11 (depends on 1)

The method of claim 1, wherein identifying the post-to-reaction filter comprises:
implementing the post-to-reaction filter as a function of a domain.

### Claim 12 (depends on 11)

The method of claim 11, further comprising:
executing instructions to compute a domain reaction profile.

### Claim 13 (depends on 11)

The method of claim 11, wherein the domain includes one or more of a topic, a time zone, a geo-location based item, a city, a state, a country, and a language.

### Claim 14 (depends on 1)

The method of claim 1, further comprising:
estimating the post to a reaction filter for the time interval for the social network for the user, the post to a reaction filter including data representative of a probability distribution that a reaction to a posted content is predicted to occur within the time interval.

### Claim 15 (depends on 1)

The method of claim 1, further comprising:
analyzing data representing content associated with the social network to generate a graph; and
transmitting a signal to a content items database to retrieve a file.

### Claim 16

An apparatus comprising:
a processor; and
a memory that includes instructions which, when executed by the processor, cause the processor to:
receive a request for an interval of time during which to post a content item on a social network in a plurality of social networks, the interval of time being divided into a first subset of units of time;
create one or more transformation functions implementing a machine learning engine to profile data based on the social network as a constituent of a first social network against a second social network;
identify a schedule for posting a content item and a post-to-reaction filter for the interval of time;
execute instructions to redefine the interval of time to include a second subset of units of time;
calculate the schedule for posting a content item on the social network and the post-to-reaction filter based on the second subset of units of time;
model a decay function as a function of the post-to-reaction filter, the decay function representative of a level of interest of an audience of the user associated with one or more of the first social network and the second social network;
determine an optimal time to post the content item on the social network as a function of the decay function;
recompute the schedule as a function of the decay function and received schedule for posting the content item on the social network; and
auto-schedule the posting of the content item to automatically post the content item based on the decay function, wherein the processor configured to auto-schedule is further configured to perform one or more of estimating data values representing audience reaction and data values representing domain specific schedule personalization.

### Claim 17 (depends on 16)

The apparatus of claim 16, wherein the units of time each is a bucket.

### Claim 18 (depends on 16)

The apparatus of claim 16, wherein the processor is further configured to: redefine the interval of time with greater granularity.

### Claim 19 (depends on 16)

The apparatus of claim 16, wherein the processor is further configured to:
predict a weighted value of the post-to-reaction filter.

### Claim 20 (depends on 16)

The apparatus of claim 16, wherein the processor is further configured to:
use the post-to-reaction filter to calculate a delayed reaction profile.