Machine learning methods and systems are provided. A machine learning system receives item-descriptive data corresponding to a plurality of uncategorized items and programmatically associates, based on the item-descriptive data, each of the uncategorized items with a user account. The system compares, by a machine learning algorithm, the item-descriptive data with existing item-descriptive data corresponding to a number of previously categorized items and automatically decides to which of one or more item categories the uncategorized data should be assigned based on dynamically learned behavior, the one or more item categories being defined in the user account. The system automatically assigns, based on the comparison and decision, each of the plurality of uncategorized items to the one or more item categories to generate a plurality of newly categorized items and adds the automatic item category assignments and corresponding newly categorized items to the number of previously categorized items.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A machine learning system comprising: a data reader configured to acquire and transmit item-descriptive data corresponding to each of a plurality of uncategorized items; and a machine learning module in electronic communication with the data reader and including instructions stored in a memory that when executed by a processor cause the machine learning module to: receive, by a communications device of the machine learning module, the electronic item-descriptive data transmitted by the data reader, programmatically associate, based on user-identifying information included in the item-descriptive data, each of the plurality of uncategorized items with a user account in a user account database stored in the memory of the machine learning module, verify a presence of a threshold number of previously categorized items associated with the user account, compare, by a machine learning algorithm executed by the processor, the item-descriptive data corresponding to each of the plurality of uncategorized items with existing item-descriptive data corresponding to each of the previously categorized items, automatically decide to which of one or more item categories the uncategorized data should be assigned based on dynamically learned behavior, the one or more item categories being defined in the user account; automatically assign, based on the comparison and decision, each of the plurality of uncategorized items to the one or more item categories to generate a plurality of newly categorized items in the user account, and add the automatic item category assignments and corresponding newly categorized items to the number of previously categorized items associated with the user account. 2. The system of claim 1, further comprising a user device configured to: display the automatic item category assignments for each of the newly categorized items within a user interface of the user device; and permit a user to modify one or more of the automatic item category assignments within the user interface. 3. The system of claim 2, wherein the instructions, when executed by the processor, further cause the machine learning module to: receive, from the user via the user interface, instructions to modify one or more of the automatic item category assignments corresponding to at least one of the newly categorized items; and modify the automatic item category assignment in response to the user instructions; and add the modified item category assignment and corresponding newly categorized item to the number of previously categorized items associated with the user account. 4. The system of claim 1, wherein the instructions, when executed by the processor, further cause the machine learning module to: display previous item category assignments for each of the previously categorized items within a user interface of a user device; and permit a user to modify one or more of the previous item category assignments within the user interface. 5. The system of claim 4, wherein the instructions, when executed by the processor, further cause the machine learning module to: receive, from the user via the user interface, instructions to modify one or more of the previous item category assignments corresponding to at least one of the previously categorized items; modify the previous item category assignment in response to the user instructions; and add the modified item category assignment and corresponding previously categorized item to the number of previously categorized items associated with the user account. 6. The system of claim 1, wherein the instructions, when executed by the processor, further cause the machine learning module to permit the user, via a user interface of a user device, to at least one of add, remove, or modify the one or more item categories defined in the user account. 7. The system of claim 1, wherein the machine learning algorithm includes at least one of a Naïve Bayes classifier, a support vector machine, a decision tree, a linear regression, a neural network, a logistic regression, a perceptron, a relevance vector machine, a Bayes optimal classifier, a bootstrap aggregating ensemble, a random forest, a boosting ensemble, a Bayesian model combination, a bucket of models ensemble, a stacking ensemble, or a supervised learning algorithm. 8. A method performed by a machine learning system, the method comprising: receiving, by a communications device of the machine learning system, item-descriptive data corresponding to each of a plurality of uncategorized items; programmatically associating, by a processor of the machine learning system and based on user-identifying information included in the item-descriptive data, each of the plurality of uncategorized items with a user account in a user account database stored in a memory of the machine learning system; verifying, by the processor of the machine learning system, a presence of a threshold number of previously categorized items associated with the user account; comparing, by a machine learning algorithm executed by the processor, the item-descriptive data corresponding to each of the plurality of uncategorized items with existing item-descriptive data corresponding to each of the previously categorized items; automatically deciding to which of one or more item categories the uncategorized data should be assigned based on dynamically learned behavior, the one or more item categories being defined in the user account, at least one of the item categories corresponding to a user-defined item category; automatically assigning, based on the comparison and decision, each of the plurality of uncategorized items to the one or more item categories to generate a plurality of newly categorized items in the user account; and adding the automatic item category assignments and corresponding newly categorized items to the number of previously categorized items associated with the user account. 9. The method of claim 8, further comprising: displaying the automatic item category assignments for each of the newly categorized items within a user interface of a user device; and permitting a user to modify one or more of the automatic item category assignments within the user interface. 10. The method of claim 9, further comprising: receiving, from the user via the user interface, instructions to modify one or more of the automatic item category assignments corresponding to at least one of the newly categorized items; modifying, by the processor of the machine learning system, the automatic item category assignment in response to the user instructions; and adding the modified item category assignment and corresponding newly categorized item to the number of previously categorized items associated with the user account. 11. The method of claim 8, further comprising: displaying previous item category assignments for each of the previously categorized items within a user interface of a user device; and permitting a user to modify one or more of the previous item category assignments within the user interface. 12. The method of claim 11, further comprising: receiving, from the user via the user interface, instructions to modify one or more of the previous item category assignments corresponding to at least one of the previously categorized items; modifying, by the processor of the machine learning system, the previous item category assignment in response to the user instructions; and adding the modified item category assignment and corresponding previously categorized item to the number of previously categorized items associated with the user account. 13. The method of claim 8, further comprising permitting the user, via a user interface of a user device, to at least one of add, remove, or modify the one or more item categories defined in the user account. 14. The method of claim 8, wherein the machine learning algorithm includes at least one of a Naïve Bayes classifier, a support vector machine, a decision tree, a linear regression, a neural network, a logistic regression, a perceptron, a relevance vector machine, a Bayes optimal classifier, a bootstrap aggregating ensemble, a random forest, a boosting ensemble, a Bayesian model combination, a bucket of models ensemble, a stacking ensemble, or a supervised learning algorithm. 15. A non-transitory computer-readable medium having instructions stored thereon that, when executed by a processor, cause a machine learning system to: receive, by a communications device of the machine learning system, item-descriptive data corresponding to each of a plurality of uncategorized items; programmatically associate, based on user-identifying information included in the item-descriptive data, each of the plurality of uncategorized items with a user account in a user account database stored in a memory of the machine learning system; verify a presence of a threshold number of previously categorized items associated with the user account; compare, by a self-learning categorization algorithm executed by the processor, the item-descriptive data corresponding to each of the plurality of uncategorized items with existing item-descriptive data corresponding to each of the previously categorized items; automatically decide to which of one or more item categories the uncategorized data should be assigned based on dynamically learned behavior, the one or more categories being defined in the user account; automatically assign, based on the comparison, each of the plurality of uncategorized items to the one or more item categories to generate a plurality of newly categorized items in the user account; and add the automatic item category assignments and corresponding newly categorized items to the number of previously categorized items associated with the user account. 16. The non-transitory computer-readable medium of claim 15, wherein the instructions further cause the machine learning system to: display the automatic item category assignments for each of the newly categorized items within a user interface of a user device; permit a user to modify one or more of the automatic item category assignments within the user interface; receive, from the user via the user interface, instructions to modify one or more of the automatic item category assignments corresponding to at least one of the newly categorized items; modify the automatic item category assignment in response to the user instructions; and add the modified item category assignment and corresponding newly categorized item to the number of previously categorized items associated with the user account. 17. The non-transitory computer-readable medium of claim 15, wherein the instructions further cause the machine learning system to: display previous item category assignments for each of the previously categorized items within a user interface of a user device; and permit a user to modify one or more of the previous item category assignments within the user interface; receive, from the user via the user interface, instructions to modify one or more of the previous item category assignments corresponding to at least one of the previously categorized items; modify the previous item category assignment in response to the user instructions; and add the modified item category assignment and corresponding previously categorized item to the number of previously categorized items associated with the user account. 18. The non-transitory computer-readable medium of claim 15, wherein the instructions further cause the machine learning system to permit the user, via a user interface of a user device, to at least one of add, remove, or modify the one or more item categories defined in the user account.