Implementing cognitive modeling techniques to provide bidding support

An article of manufacture comprising a computer-readable storage medium for storing computer-readable program code which, when executed, causes a computer to perform the steps of:

• providing, by a cognitive bidding engine associated with a user, bidding support to the user during an auction using cognitive modeling, wherein providing bidding support using cognitive modeling comprises:
• obtaining an initial bidding strategy for the user;
• generating sensor data in real-time or near real-time by at least one sensing device located in physical proximity to the auction;
• the sensor data comprising at least one of audio data, video data and physiological data;
• determining, based at least in part on the sensor data, a physiological state of at least one bidder at the auction, the at least one bidder being different than the user;
• obtaining external factor data associated with the at least one bidder from at least one source external to the auction;
• obtaining historical data comprising at least one of bid history data, relevant weather data, and relevant historical event data;
• determining, based at least in part on the obtained external factor data, whether or not the determined physiological state of the at least one bidder is a physiological response of the at least one bidder to the auction;
• analyzing at least a portion of the generated sensor data, obtained external factor data and obtained historical data to determine a bid price threshold for the at least one bidder, wherein determining the bid price threshold for the given bidder comprises predicting the bid price threshold, and assigning a confidence score to the prediction;
• adjusting the bid price threshold for the at least one bidder based on a success of the prediction via at least one machine learning algorithm;
• in response to determining that the determined physiological state of the at least one bidder is a physiological response of the at least one bidder to the auction, updating the initial bidding strategy based at least in part on the determined physiological state of the at least one bidder and the adjusted bid price threshold to generate an updated bidding strategy;
• generating at least one output based at least in part on the updated bidding strategy;
• and
• providing the at least one generated output to at least one device associated with the user, wherein the at least one device is configured to generate at least one alert based at least in part on the at least one output, and wherein the at least one alert provides decision support to the user with respect to bidding strategy.

An apparatus comprising:

• at least one processor operatively coupled to at least one memory;
• and
• a cognitive bidding engine in communication with the processor, wherein the cognitive bidding engine is associated with a user and configured to provide bidding support to the user during an auction using cognitive modeling;
• at least one sensing device located in physical proximity to the auction and configured to generate sensor data comprising at least one of audio data, video data and physiological data in real-time or near real-time;
• wherein the cognitive bidding engine is configured to:
• determine, based at least in part on the sensor data, a physiological state of at least one bidder at the auction, the at least one bidder being different than the user;
• obtain external factor data associated with the at least one bidder from at least one source external to the auction;
• obtain historical data comprising at least one of bid history data, relevant weather data, and relevant historical event data;
• determine, based at least in part on the obtained external factor data, whether or not the determined physiological state of the at least one bidder is a physiological response of the at least one bidder to the auction;
• analyze at least a portion of the generated sensor data, obtained external factor data and obtained historical data to determine a bid price threshold for the at least one bidder, wherein determining the bid price threshold for the given bidder comprises predicting the bid price threshold, and assigning a confidence score to the prediction;
• adjust the bid price threshold for the at least one bidder based on a success of the prediction via at least one machine learning algorithm;
• in response to determining that the determined physiological state of the at least one bidder is a physiological response of the at least one bidder to the auction, updating the initial bidding strategy based at least in part on the determined physiological state of the at least one bidder and the adjusted bid price threshold to generate an updated bidding strategy;
• generate at least one output based at least in part on the updated bidding strategy;
• and
• provide the at least one generated output to at least one device associated with the user, wherein the at least one device is configured to generate at least one alert based at least in part on the at least one output, and wherein the at least one alert provides decision support to the user with respect to bidding strategy.