A system and method for generating a cue sheet for submission to a performing rights organization. The system comprising a processor for executing the steps of: receiving an electronic file associated with a broadcast production comprising a list of cues used in the broadcast production and associated use data; extracting irrelevant data from the electronic file by determining and deleting irrelevant cues, determining and deleting unnecessary and unrelated use data from each cue, and determining and deleting duplicative cues, so that relevant cues and associated use data remain after extracting; searching a music catalog database to retrieve identification data for each relevant cue; storing each relevant cue with its associated use data and identification data in a memory; and generating a cue sheet comprising relevant cues with associated use data and identification data for each relevant cue.

The present disclosure relates to management of inventories and supply chains and, more particularly, to a predictive inventory management system and methods of use. The method of inventory management, includes: obtaining information associated with an item; predicting demand of the item based on the obtained information; determining whether additional inventory is required based on the predicted demand of the item; and obtaining additional inventory of the item when the current inventory level does not match the predicted demand.

Provided are systems and methods for reconciling transactions from different financial accounts via machine learning. In one example, a method may include storing a data file comprising a plurality of electronic transactions and a plurality of transaction records corresponding to the plurality of electronic transactions in memory, inputting the data file into a first machine learning model and identifying respective transaction attributes of the plurality of transaction records based on the input data file, determining, via execution of a second machine learning model, that a first transaction and a second transaction in the data file correspond to a common transaction based on attributes of the first and second transactions which are identified from respective transactions records of the first and second transactions, and modifying the data file to indicate that the first and second transactions correspond to the common transaction and storing the modified data file in the memory.

A system includes memory operable to store instructions and processing circuitry operable to execute the instructions. The system accesses previous data processing transactions associated with a first account of a user, wherein at least one of the previous data processing transactions are transacted using a first routing between a first computing system and a second computing system. The system further determines, using machine learning, a pattern from the previous data processing transactions and determines an upcoming data processing transaction corresponding to the pattern. The system further compares the upcoming data processing transaction to a data item associated with the first account and determines a second routing between the second computing system and a third computing system. The system further transacts, based on the comparison of the upcoming data processing transaction to the data item, the upcoming data processing transaction using either the first routing or the second routing.

Systems, methods, and apparatuses for generating a budget and scope associated therewith include a provider computing system including a network interface structured to facilitate data communication via a network and a processing circuit comprising a processor and memory. The processing circuit is structured to receive a scope and a budget. The processing circuit includes a management circuit structured to determine a scope merchant category for items offered for sale by a merchant that match the scope and wherein the management circuit is structured to interpret the budget to determine a maximum amount of funds for the purchase. The processing circuit receives a selected item. The management circuit determines a category in comparison to the scope and a cost item in comparison to the budget to determine if the cost of the item is less than the maximum amount of funds.

An automated payment delivery system is provided that combines electronic investigative data methods with fintech payment systems to deliver payments to payees. The system can be configured to receive payee account information, optionally enrich the payee account information through data discovered in public and private databases, automatically generate unique communications to each payee, verify the identity of the payee, and initiate electronic payment of the payment to the payee.

A system and method for advanced inventory management using deep neural networks. The system may be disposed in a business establishment or it may be a cloud-based network comprising a one or more databases to store and retrieve including patron data, recipe data, business data, and inventory data, mobile and compute devices, staff and suppliers, one or more gateways for vendors and staff to interface with other third-party business, and an inventory analysis server. Taken together or in part, optimize organizational operations by predicting and optimizing key inventory decisions using artificial intelligence or other computerized methods around inventory management based upon a large amount of variables associated with the enterprise.

A computerized system for verifiable pairing physical assets with digital representations comprising a set of non-transitory computer readable instructions for retrieving a design record having a material list, retrieving a supplier record according to the design record and according to a first verification, retrieving a shipping record according to the supplier record and according to a second verification, and creating a material receipt record including a third verification representing that the material is received at the location and is the same material that was designated in the designer, provided by the supplier, received by the shipper and delivered to the location.

The preceding disclosure concerns a novel solution to income inequality and wealth inequality (the “Solution”). The Solution is comprised of, among other elements, the following embodiments: an income equality generator; a wealth equality generator; future income swaps; future wealth swaps; income verification for future income swaps; and wealth verification for future wealth swaps. The embodiments are useful, novel, and non-obvious individually. Likewise, the embodiments are useful, novel, and non-obvious when taken collectively. Income inequality and/or wealth inequality—the problem(s) solved by the income equality generator and/or wealth equality generator alone or in combination with future income swaps and/or future wealth swaps and income verification for future income swaps and/or wealth verification for future wealth swaps—do not occur in nature, are a relatively new problem, and are problems that have increased in magnitude in recent years. The Solution does not constitute a software implementation of a process that is already in use. Nor do the embodiments, either individually or collectively. Importantly, we do not seek patent protection for all means by which income equality and/or wealth equality may be generated; for all swap products (i.e., those not related to income or wealth); or other income verification processes for future income swaps and/or future wealth swaps that may be similar to those that are detailed in this application.

A method, apparatus, system, and computer program code for digitally presenting a comparison of labor resource allocations between organizations. A computer system identifies employee data for a set of employees. The employee data includes job titles, and job descriptions. Using a set of machine learning models, the computer system extracts skills data from the job descriptions. Based on the skills data extracted from the job descriptions and using the set of machine learning models, the computer system maps the job titles to normalized titles within a job taxonomy that governs relationships between business functions, subfunctions, and the normalized titles. The computer system determines a resource allocation for an organization over each of the normalized titles. The computer system generates a granular comparison of the granular resource allocation to a set of benchmark allocations. The computer system digitally presents the granular comparison in a graphical user interface.