Abstract: Disclosed herein are embodiments for automated intelligent price guidance of listings for a for sale object (FSO) being offered by a seller. Some embodiments may operate by: receiving information relating to the FSO, including specifications for selling the FSO, wherein the specifications include an original offer price and a time window for selling the FSO; determining a category of the FSO; generating an optimal offer price for the FSO based on one or more of: (a) past listings of previously sold FSOs that have a same or similar category of the FSO; (b) the specifications, including the time window; (c) a category decay curve applicable to the category; and (d) seller flexibility curve of the seller; and through the use of a machine learning neural networking analysis suggesting the optimal offer price to the seller as an offer price for a listing corresponding to the FSO, wherein this price is evaluated over time and suggestions are made accordingly.

Abstract: Disclosed herein are embodiments for intelligent listing creation for a for sale object (FSO). Some embodiments operate by determining a numerical identifier corresponding to a category of the FSO. A binarization of the numerical identifier using hot encoding is performed and using a neural network regression model, an optimal offer price is generated based on a category of the FSO. Information about the FSO is provided to the neural network regression model that tokenizes the textual input, and a unique binary vector representing the category is provided instead of the numerical identifier to the neural network regression model. An optimal price, generated by the neural network regression model, based on the unique binary vector representing the category.

Abstract: Disclosed herein are embodiments for intelligent listing creation for a for sale object (FSO). Some embodiments operate by determining a numerical identifier corresponding to a category of the FSO. A binarization of the numerical identifier using hot encoding is performed and using a neural network regression model, an optimal offer price is generated based on a category of the FSO. Information about the FSO is provided to the neural network regression model that tokenizes the textual input, and a unique binary vector representing the category is provided instead of the numerical identifier to the neural network regression model. An optimal price, generated by the neural network regression model, based on the unique binary vector representing the category.

Abstract: Disclosed herein are system, computer-program product (non-transitory computer-readable medium), and method embodiments for machine-learning prediction or suggestion based on object identification. A system including at least one processor may be configured to cross-reference an identifier of a selected object with a list of known unique identifiers. The selected object may be selected via received selection. The at least one processor may further retrieve a set of values associated with the identifier of the selected object, upon determining that the list of known unique identifiers includes the identifier of the selected object, and perform machine-learning to derive a predicted-value set based at least in part on the set of values associated with the identifier of the selected object and a category applicable to the selected object. The at least one processor may determine that the predicted-value set satisfies a predetermined confidence condition, and output at least part of the predicted-value set.

Abstract: Disclosed herein are embodiments for automated intelligent price guidance of listings for a for sale object (FSO) being offered by a seller. Some embodiments may operate by: receiving information relating to the FSO, including specifications for selling the FSO, wherein the specifications include an original offer price and a time window for selling the FSO; determining a category of the FSO; generating an optimal offer price for the FSO based on one or more of: (a) past listings of previously sold FSOs that have a same or similar category of the FSO; (b) the specifications, including the time window; (c) a category decay curve applicable to the category; and (d) seller flexibility curve of the seller; and through the use of a machine learning neural networking analysis suggesting the optimal offer price to the seller as an offer price for a listing corresponding to the FSO, wherein this price is evaluated over time and suggestions are made accordingly.