The system may perform a method for controlling access to a secure data object. The secure data object may be a tax return. The method may implement a distributed ledger and tokens. A tax preparer or an owner of a tax return may put the tax return into a distributed ledger and securely associate the tax return with a token. By sharing the token with authorized third parties, the tax preparer or owner of the tax return may allow others to access the tax return. By retrieving the tax return from the distributed ledger with the token, rather than receiving the tax return directly from an owner of the tax return, the authorized third parties may be assured of the authenticity of the tax return that they are accessing with the token.

The system may automatically filter data according to multiple criteria. The method may be applied for the selection of a certified public accountant according to multiple criteria relevant to a particular client. The method may include receiving various criteria, requesting a plurality of target data structures, receiving various weights associated with various criteria, and then identifying which of the target data structures reflect greater or lesser correspondence to the criteria in view of the criteria weighting. The method may filter target data structures to reject target data structures lacking sufficient correspondence to the criteria and may transmit the remaining data structures to a remote selection device.

The system automatically selects an output data structure from among more than one output data structure generated by separate processors. For instance, the output data structures may have different performance scores, and the system and method may compute difference scores to compare the independently determined performance scores of the output data structures. Depending on the magnitude of the difference score, the output data structures can be determined to be valid or to be invalid so that further processing is performed to generate a valid output data structure. In some instances, the data structures are tax returns, and the system automatically computes a tax return, and the comparing process advantageously yields a tax return that optimizes a tax filing strategy.

A method that predicts business income from user transaction data. A multinomial classifier is trained, using a vector of features from data related to a historical transaction and a label associated with the historical transaction, to generate a probability that the historical transaction belongs to a specific classification with respect to income. Data related to a new transaction is split into a set of unigrams. A new vector of features is generated from the data related to the new transaction. The new vector includes a set of values that correspond and are assigned to the set of unigrams. A classification with respect to income is determined for the new transaction by applying the multinomial classifier to the new vector. The new transaction is labeled with the classification. One or more fields of a form that is maintained by an online service is populated using the classification.

Relative to a gaming system, a jackpot processing server is configured to generate at least one gaming win reporting form, such as a W2G, in response to a gaming win by the player. The jackpot processing server is configured to store that form and provide access to the form. A code may be provided to the player which is used to generate a communication link to a webserver for accessing the form. Alternatively, the form may be emailed to the player, such as in response to a request to an email server. The email server may be a public email server that communicates to the jackpot processing server via a secure communication module.

A computer gaming system receives a specification of rules for revenue recognition, reporting, or allocation of proceeds of gaming activities among entities having claims to proceeds in conjunction with bettors of the gaming activity. The specification may describe and reflect changes in inter-jurisdiction tax treaties or compacts, or agreements among commercial operators. During or at conclusion of gaming activities by players through the gaming system, amounts of revenue, tax payable, or tax withholding, relating to the gaming activities are computed from the specification, for a plurality of horizontally-related tax jurisdictions, players and operators of the gaming activity having taxable contacts with the horizontally-related tax jurisdictions. The computation may reflect a tax compact or treaty among the horizontally-related jurisdictions to specify interjurisdictional treatment of the revenue, tax payable, or withholding. Proceeds of the gaming activities of the players are reported and/or allocated to operators and/or jurisdictions as specified by the computation.

In various embodiments a player of a gaming device or mobile gaming device is presented with an indication of a payout amount less any taxes that would be owed for the payout.

A computer system includes a container deployment and management server for executing a container builder. The container builder is configured to generate and deploy a transaction tax engine container. The container builder is configured to extract from client configuration settings a subset of multiple products and a subset of multiple geographic regions applicable to transactions processed by a client, identify a subset of the tax rate and rule data applicable to each of the subset of products in each of the subset of geographic regions, and create a local edge database including the subset of tax rate and rule data and excluding a reminder of the tax rate and rule data. The container builder is further configured to create a transaction tax engine container image and transmit the tax engine container image to an edge computing device.

The computerized staffing platform includes a staffing server with a processor configured to store data indicating available shifts from third-party businesses and data indicating employees of the platform registered to fulfill the available shifts. The processor is configured to receive a request from a particular business to fulfill a target available shift, and determine candidate employees to fulfill the target available shift. The processor is configured to compute a predicted labor burden associated with hiring each candidate employee for the target available shift, including whether the candidate employee has exceeded an income threshold after which a labor burden factor no longer applies. The processor is further configured to send, to the particular business, a list of the candidate employees and the predicted labor burden for each candidate employee, receive a selection of a candidate employee, and send, to the selected candidate employee, an offer to fulfill the available target shift.

Aspects extract, from payroll data of employees of an organization, data historically associated to previous instances of certified tax credit eligibility; normalize the extracted data with respect to data type and data value; generate from the normalized extracted data via a neural network classifier multi-class outputs for each employee that indicate strengths of likelihood that each employee is currently eligible for each of a plurality of different tax credits; filter the normalized extracted data by removing portions associated to employees indicated within the multi-class outputs as having no currently eligible likelihood for the different tax credits, thereby generating a remainder set of normalized extracted data associated to remainder eligible ones of the employees; and prioritize application for the tax credits for the remainder eligible employees as a function of respective values and likelihoods of eligibility within the remainder set of normalized extracted data.