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US 2019/0156252 A1Published application
System For Facilitating And Executing Travel-Related Transactions
OwnerMark C. Dawkinsinventor
Life of this patent
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Abstract
The present disclosure relates to methods and systems for facilitating travel preparation and reservations. More specifically, the present disclosure relates to a monitoring system that may monitor travel sites based on input travel parameters, wherein the monitoring may allow a traveler to optimize a trip. The optimization may be based on a plurality of factors, such as price, location, and dates, as non-limiting examples. The present disclosure further relates to an offer system that develops offer terms for travel providers to extend to travelers, wherein offer terms may vary based on a plurality of factors, such as trends, sales, or demand, as non-limiting examples.
Claims20
A system for monitoring travel parameters comprising:
- a traveler server configured to receive and store a plurality of traveler profiles wherein each traveler profile comprises one or more travel activity packets;
- a monitoring server configured to:
- access the traveler server;
- access a first traveler profile and a first travel activity packet;
- access a monitoring optimizer database, wherein the monitoring optimizer database comprises a machine learning mechanism and predictive data related to travel;
- associate a first travel activity packet with a first set of predictive data, wherein the first set of predictive data comprises a subset of the predictive data related to the first travel activity packet;
- access a plurality of remote servers hosted by one or more travel vendor;
- search the plurality of remote servers at a set of search intervals, wherein the set of search intervals are based at least in part on the first set of predictive data;
- identify a first pricing for at least a portion of the first travel activity packet, wherein the first pricing is provided by at least a portion of the plurality of remote servers;
- generate a first qualitative score for the first pricing based at least in part on the first set of predictive data, wherein the qualitative score indicates a relative quality of the first pricing based on trends identified by the machine learning mechanism utilizing the predictive data and the first travel activity packet;
- and
- generate a first likelihood of purchase score based at least in part on the first qualitative score, the first travel activity packet, the first set of predictive data, and the trends identified by the machine learning mechanism utilizing the predictive data, wherein the likelihood of purchase score indicates a relative likelihood of purchase for the at least a portion of the first travel activity packet at the first pricing.
9 more elements
A computer-implemented process for monitoring travel parameters comprising the process steps of:
- accessing a traveler server configured to receive and store a plurality of traveler profiles wherein each traveler profile comprises one or more travel activity packets;
- accessing a first traveler profile and a first travel activity packet;
- accessing a monitoring optimizer database, wherein the monitoring optimizer database comprises a machine learning mechanism and predictive data related to travel;
- associating a first travel activity packet with a first set of predictive data, wherein the first set of predictive data comprises a subset of the predictive data related to the first travel activity packet;
- accessing a plurality of remote servers hosted by one or more travel vendor;
- searching the plurality of remote servers at a set of search intervals, wherein the set of search intervals are based at least in part on the first set of predictive data;
- identifying a first pricing for at least a portion of the first travel activity packet, wherein the first pricing is provided by at least a portion of the plurality of remote servers;
- generating a first qualitative score for the first pricing based at least in part on the first set of predictive data, wherein the qualitative score indicates a relative quality of the first pricing based on pricing trends identified by the machine learning mechanism utilizing the predictive data and the first travel activity packet;
- and
- generating a first likelihood of purchase score based at least in part on the first qualitative score, the first travel activity packet, the first set of predictive data, and the pricing trends, wherein the likelihood of purchase score indicates a relative likelihood of purchase for the at least a portion of the first travel activity packet at the first pricing.
7 more elements
Dependencies are inferred from the claim text.