US-12619674-B2 - System and method for topological representation of commentary

US12619674B2US 12619674 B2US12619674 B2US 12619674B2US-12619674-B2

Abstract

Systems, methods, and computer-readable storage media for aggregating media (and commentary on that media) into a topology. To do so, the system receives first content (and associated metadata) as well as second content (and associated metadata). The system then generates a topology based on a relationship between the first content and the second content, where the topology has a number of dimensions based on the metadata of the different pieces of content. The system then compares the topology and the metadata to previously stored topologies and/or metadata and, based on that comparison, executes a machine learning algorithm. The output of that machine learning algorithm includes predicted future changes to the topology, which the system uses to reduce the number of dimensions within the topology.

Inventors

Peter Robert Williams
Scott Lester Minneman
Stephan John Fitch

Assignees

Intelling Media Corp.

Dates

Publication Date: 20260505
Application Date: 20231113

Claims (20)

1 . A method comprising: receiving, at a computer system, a plurality of comments associated with an event, wherein each comment in the plurality of comments is received while the event is ongoing; training, via at least one processor of the computer system, an artificial intelligence algorithm using commentary on a topic; executing, via the at least one processor, the artificial intelligence algorithm on the plurality of comments, resulting in at least one comment commonality; calculating, via the at least one processor, at least one velocity at which the plurality of comments are being received; calculating, via the at least one processor, at least one volatility of the plurality of comments; generating, via the at least one processor, a conversation space where additional comments regarding the event can be represented, the conversation space allocated storage space in computer-readable memory, wherein the conversation space is generated based on attributes of the plurality of comments comprising: the at least one comment commonality, the at least one velocity, and the at least one volatility; and presenting the conversation space to at least one user while the event is ongoing.
2 . The method of claim 1 , wherein the event is a video event.
3 . The method of claim 1 , wherein the at least one comment commonality comprises a topic about the event.
4 . The method of claim 1 , wherein each comment in the plurality of comments comprises metadata, the metadata comprising a time of creation.
5 . The method of claim 1 , further comprising: executing, via the at least one processor, a sentiment analysis on the plurality of comments, resulting in sentiment analysis results, wherein the volatility is measured, at least in part, on a distribution of sentiment within the plurality of comments.
6 . The method of claim 1 , further comprising: receiving at least one existing taxonomy; executing, via the at least one processor, at least one additional machine learning algorithm, wherein input to the at least one additional machine learning algorithm comprises the plurality of comments and the at least one existing taxonomy, and output of the at least one additional machine learning algorithm comprises a taxonomy, wherein the generating of the conversation space is further based on the taxonomy.
7 . The method of claim 1 , wherein the conversation space is formed to the at least one user based upon at least one agreement between users.
8 . A system comprising: at least one processor; and a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving a plurality of comments associated with an event, wherein each comment in the plurality of comments is received while the event is ongoing; training an artificial intelligence algorithm using commentary on a topic; executing the artificial intelligence algorithm on the plurality of comments, resulting in at least one comment commonality; calculating at least one velocity at which the plurality of comments are being received; calculating at least one volatility of the plurality of comments; generating a conversation space where additional comments regarding the event can be viewed, the conversation space allocated storage space in computer-readable memory, wherein the conversation space is generated based on attributes of the plurality of comments comprising: the at least one comment commonality, the at least one velocity, and the at least one volatility; and presenting the conversation space to at least one user while the event is ongoing.
9 . The system of claim 8 , wherein the event is a video event.
10 . The system of claim 8 , wherein the at least one comment commonality comprises a topic about the event.
11 . The system of claim 8 , wherein each comment in the plurality of comments comprises metadata, the metadata comprising a time of creation.
12 . The system of claim 8 , the non-transitory computer-readable storage medium having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: executing a sentiment analysis on the plurality of comments, resulting in sentiment analysis results, wherein the volatility is measured, at least in part, on a distribution of sentiment within the plurality of comments.
13 . The system of claim 8 , the non-transitory computer-readable storage medium having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving at least one existing taxonomy; executing at least one additional machine learning algorithm, wherein input to the at least one additional machine learning algorithm comprises the plurality of comments and the at least one existing taxonomy, and output of the at least one additional machine learning algorithm comprises a taxonomy, wherein the generating of the conversation space is further based on the taxonomy.
14 . The system of claim 8 , wherein the conversation space is formed to the at least one user based upon at least one agreement between users.
15 . A non-transitory computer-readable storage medium having instructions stored which, when executed by at least one processor, cause the at least one processor to perform operations comprising: receiving a plurality of comments associated with an event, wherein each comment in the plurality of comments is received while the event is ongoing; training an artificial intelligence algorithm using commentary on a topic; executing the artificial intelligence algorithm on the plurality of comments, resulting in at least one comment commonality; calculating at least one velocity at which the plurality of comments are being received; calculating at least one volatility of the plurality of comments; generating a conversation space where additional comments regarding the event can be viewed, the conversation space allocated storage space in computer-readable memory, wherein the conversation space is generated based on attributes of the plurality of comments comprising: the at least one comment commonality, the at least one velocity, and the at least one volatility; and presenting the conversation space to at least one user while the event is ongoing.
16 . The non-transitory computer-readable storage medium of claim 15 , wherein the event is a video event.
17 . The non-transitory computer-readable storage medium of claim 15 , wherein the at least one comment commonality comprises a topic about the event.
18 . The non-transitory computer-readable storage medium of claim 15 , wherein each comment in the plurality of comments comprises metadata, the metadata comprising a time of creation.
19 . The non-transitory computer-readable storage medium of claim 15 , having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: executing a sentiment analysis on the plurality of comments, resulting in sentiment analysis results, wherein the volatility is measured, at least in part, on a distribution of sentiment within the plurality of comments.
20 . The non-transitory computer-readable storage medium of claim 15 , having additional instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving at least one existing taxonomy; executing at least one machine learning algorithm, wherein input to the at least one machine learning algorithm comprises the plurality of comments and the at least one existing taxonomy, and output of the at least one machine learning algorithm comprises a taxonomy, wherein the generating of the conversation space is further based on the taxonomy.

Description

CROSS-REFERENCE The present is a continuation of U.S. patent application Ser. No. 18/047,813, filed on Oct. 19, 2022, which claims priority to U.S. Provisional Patent Application No. 63/257,360, filed Oct. 19, 2021, now U.S. Pat. No. 11,841,914, the contents of which are incorporated herein by reference in their entirety. BACKGROUND 1. Technical Field The present disclosure relates to a topological representation of commentary, and more specifically to aggregating media and commentary on that media into a topology. 2. Introduction Online content distribution systems typically have a piece of content for a user to view, along with user created comments about the content. For example, a newspaper may have an article with a number of comments from users, just as a video on a system such as YOUTUBE may have a number of comments from users. Such systems may also give users the ability to comment on the comments of other users. However, filtering through the many comments to find additional information about a given topic, removing non-related information, ignoring the “crazies,” and/or selecting only the top tier comments can quickly become computationally impossible for a human being to do, much less do so with an objective sense of quality. SUMMARY Additional features and advantages of the disclosure will be set forth in the description that follows, and in part will be understood from the description, or can be learned by practice of the herein disclosed principles. The features and advantages of the disclosure can be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the disclosure will become more fully apparent from the following description and appended claims, or can be learned by the practice of the principles set forth herein. Disclosed are systems, methods, and non-transitory computer-readable storage media which provide a technical solution to the technical problem described. A method for performing the concepts disclosed herein can include: receiving, at a computer system, first content; generating, via at least one processor of the computer system, first metadata associated with the first content; receiving, at the computer system, second content associated with the first content; generating, via the at least one processor, second metadata associated with the second content; generating, via the at least one processor, a topology representing a relationship between the first content and the second content, the topology having a number of dimensions based at least in part on the first metadata and the second metadata; comparing, via the at least one processor, the topology, the first metadata, and the second metadata, to previously instantiated topologies and previously instantiated metadata, resulting in a comparison; executing, via the at least one processor, at least one machine learning algorithm, wherein input to the at least one machine learning algorithm comprises the comparison, and output of the at least one machine learning algorithm comprises predicted changes to the topology; and reducing, via the at least one processor, the number of dimensions within the topology based on the predicted changes to the topology, resulting in a reduced dimensions topology. A system configured to perform the concepts disclosed herein can include: at least one processor; and a non-transitory computer-readable storage medium having instructions stored which, when executed by the at least one processor, cause the at least one processor to perform operations comprising: receiving first content; generating first metadata associated with the first content; receiving second content associated with the first content; generating second metadata associated with the second content; generating a topology representing a relationship between the first content and the second content, the topology having a number of dimensions based at least in part on the first metadata and the second metadata; comparing the topology, the first metadata, and the second metadata, to previously instantiated topologies and previously instantiated metadata, resulting in a comparison; executing a machine learning algorithm, wherein input to the machine learning algorithm comprises the comparison, and output of the machine learning algorithm comprises predicted changes to the topology; and reducing the number of dimensions within the topology based on the predicted changes to the topology, resulting in a reduced dimensions topology. A non-transitory computer-readable storage medium configured as disclosed herein can have instructions stored which, when executed by a computing device, cause the computing device to perform operations which include: receiving first content; generating first metadata associated with the first content; receiving second content associated with the first content; generating second metadata associated with the second content; g