Analyzing event-associated connections

• US 10,643,224 B2
• Filed: 07/24/2017
• Issued: 05/05/2020
• Est. Priority Date: 07/24/2017
• Status: Active Grant

• Current Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Original Assignee: Accenture Global Solutions Limited (Accenture PLC)
• Inventors: Ram Bhuyan, Mukunda, Roy, Subhasish, Thottikkara, Bibin George, Mukhopadyaya, Sudipta
• Primary Examiner(s): Reagan, James A
• Application Number:
  US15/657,884
• Publication Number:
  US 20190026758A1
• Time in Patent Office: 1,016 Days
• Field of Search: None
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06Q 30/0201 Market modelling; Market analysis; Collecting market data
• CPC Class Codes: G06Q 50/01 Social networking

Abstract

A device may receive information related to connections between two or more attendees of an event or subsequent interactions between the two or more attendees. The device may process the information using a set of techniques to permit an analysis of the connections and the subsequent interactions. The device may determine a score related to the event based on a result of analyzing the connections and the subsequent interactions. The score may indicate a level of success of the event with respect to the connections or the subsequent interactions. The device may perform an action based on the score.

Claims

1. A device, comprising:

one or more processors to;

receive, from at least one of a client device or a server device, information related to connections between two or more attendees of an event or subsequent interactions between the two or more attendees after the event,

the information including contact information associated with the two or more attendees;

process the information using a set of computer processing techniques to identify the connections or the subsequent interactions,

the set of computer processing techniques including;

a text processing technique, or

an audio data processing technique;

determine, based on analyzing the information using a machine learning model and based on the connections or the subsequent interactions associated with the two or more attendees of the event, a score related to the event,

the score indicating a level of success of the event, and

the machine learning model being trained on data identifying other scores indicating other levels of success of other events; and

perform an action related to the event based on the score,

where the one or more processors, when performing the action, are to;

generate, based on the score, a prediction regarding a second score indicating a second level of success of a second event, and

generate, based on the second score satisfying a threshold score, a recommendation regarding participation in the second event.

2. The device of claim 1, where the one or more processors are further to:

determine whether the two or more attendees have previously exchanged the contact information; and

where the one or more processors, when determining the score, are to;

determine the score based on whether the two or more attendees have previously exchanged the contact information.

3. The device of claim 1, where the one or more processors are further to:

identify a set of events associated with the threshold score after determining the score for the event; and

where the one or more processors, when performing the action, are to;

generate a report that identifies the set of events; and

output the report for display after generating the report.

4. The device of claim 1, where the one or more processors are further to:

determine whether a first type of subsequent interaction, included in the subsequent interactions, is related to a second type of subsequent interaction included in the subsequent interactions; and

where the one or more processors, when determining the score, are to;

determine the score based on whether the first type of subsequent interaction is related to the second type of subsequent interaction.

5. The device of claim 1, where the set of computer processing techniques are related to processing text data, and

where the set of computer processing techniques includes:

a natural language processing technique,

a text analysis technique, or

a computational linguistics technique.

6. The device of claim 1, where the set of computer processing techniques are related to processing audio data, and

where the set of computer processing techniques includes:

a speech-to-text technique,

a computer speech recognition technique, or

an automatic speech recognition technique.

7. The device of claim 1, where the one or more processors, when processing the information, are to:

determine that the information includes at least one term; and

identify the connections or the subsequent interactions based on determining that the information includes the at least one term.

8. A method, comprising:

receiving, by a device and from at least one of a client device or a server device, information related to an event,

the information relating to;

connections between two or more attendees of the event that exchanged contact information at the event, or

subsequent interactions between the two or more attendees after exchanging the contact information;

processing, by the device, the information using a set of computer processing techniques to identify the connections or the subsequent interactions;

determining, by the device and based on analyzing the information using a machine learning model, a score related to the event that indicates a level of success of the event with respect to the connections or the subsequent interactions,

the score being determined using the information related to the connections or the subsequent interactions, and

the machine learning model being trained on data identifying other scores indicating other levels of success of other events; and

performing, by the device, an action related to the event based on the score,

wherein performing the action comprises;

generating, based on the score, a prediction regarding a second score indicating a second level of success of a second event, and

generating, based on the second score satisfying a threshold score, a recommendation regarding participation in the second event.

9. The method of claim 8, further comprising:

determining whether the two or more attendees have previously exchanged the contact information or a timing of a prior exchange of the contact information; and

where determining the score comprises;

determining the score based on whether the two or more attendees have previously exchanged the contact information or the timing of the prior exchange of the contact information.

10. The method of claim 8, where performing the action comprises:

generating another recommendation related to a set of future events to which to send an attendee, of the two or more attendees, or another individual based on the score.

11. The method of claim 8, further comprising:

determining a content or a nature of the subsequent interactions of the two or more attendees; and

where determining the score comprises;

determining the score based on the content or the nature of the subsequent interactions.

12. The method of claim 8, where determining the score comprises:

determining the score based on whether the two or more attendees have previously exchanged the contact information.

13. The method of claim 8, further comprising:

determining whether a connection, of the connections, is related to another connection; and

where determining the score comprises;

determining the score based on whether the connection is related to another connection.

14. The method of claim 8, where performing the action comprises:

scheduling a meeting related to the event or an attendee, of the two or more attendees, after determining the score.

15. A non-transitory computer-readable medium storing instructions, the instructions comprising:

one or more instructions that, when executed by one or more processors, cause the one or more processors to;

receive information related to connections between two or more attendees of an event or subsequent interactions between the two or more attendees,

the information being received from;

a client device associated with one of the two or more attendees, or

a server device;

process the information using a set of computer processing techniques to permit an analysis of the connections and the subsequent interactions;

determine, based on analyzing the information using a machine learning model and based on the connections and the subsequent interactions, a score related to the event,

the score indicating a level of success of the event with respect to the connections or the subsequent interactions, and

the machine learning model being trained on data identifying other scores indicating other levels of success of other events; and

perform an action based on the score,

where the one or more instructions, that cause the one or more processors to perform the action, cause the one or more processors to;

generate, based on the score, a prediction regarding a second score indicating a second level of success of a second event, and

generate, based on the second score satisfying a threshold score, a recommendation regarding participation in the second event.

16. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine whether a subsequent interaction, of the subsequent interactions, is a particular type of subsequent interaction; and

where the one or more instructions, that cause the one or more processors to determine the score, cause the one or more processors to;

determine the score based on whether the subsequent interaction is the particular type of subsequent interaction.

17. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

determine whether the two or more attendees had a subsequent interaction;

determine a timing of the subsequent interaction after determining that the two or more attendees had the subsequent interaction; and

where the one or more instructions, that cause the one or more processors to determine the score, cause the one or more processors to;

determine the score based on whether the two or more attendees had the subsequent interaction or the timing of the subsequent interaction.

18. The non-transitory computer-readable medium of claim 15, where the one or more instructions, that cause the one or more processors to process the information, cause the one or more processors to:

process the information to identify a term or phrase included in the information; and

where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to;

identify the connections or the subsequent interactions after identifying the term or phrase included in the information.

19. The non-transitory computer-readable medium of claim 15, where the one or more instructions, when executed by the one or more processors, further cause the one or more processors to:

generate another recommendation related to whether to have an attendee, of the two or more attendees, attend the second event; and

where the one or more instructions, that cause the one or more processors to perform the action, cause the one or more processors to;

provide information identifying the recommendation for display after generating the recommendation.

20. The non-transitory computer-readable medium of claim 15, where the information comprises:

social media network information indicating that the connections between the two or more attendees are on a social media network.