Certain aspects of the present disclosure provide techniques for intelligent grouping of travel data for review through a user interface. In one example, a method for providing grouped travel data to a user interface of an application includes receiving travel data from an application running on a remote device; generating one or more travel data-based features from the travel data thereby creating featurized travel data; applying a pattern mining technique to the featurized travel data to detect a plurality of patterns in the featurized travel data; for each trip record in the featurized travel data: determining a plurality of trip record groups in which the trip record falls based on the plurality of patterns; and adding the trip record to a trip record group of the plurality of trip record groups according to a prioritization scheme; and transmitting the trip record group to the application running on the remote device.

Described herein are systems and techniques for providing automatic distribution of transaction-related benefits to an account used to conduct a transaction. In certain embodiments, the system receives transaction details from a transaction processing network as transactions are conducted. The system determines an appropriate benefit provider for a transaction conducted from an account and provides a subset of transaction details to that benefit provider. The benefit provider compares the subset of transaction details to a set of limited transaction details received from a resource provider to identify the transaction and determine a benefit amount, if any, for which the transaction qualifies. The benefit provider then provides the benefit amount to the system, which then causes the transaction processing network to credit the account the benefit amount.

This disclosure generally relates to systems and methods to receiving onboard diagnostic data associated with an operational state of a vehicle of a user and pushing a message to the user, the message including information associated with the onboard diagnostic data.

Disclosed is a taxpayer industry classification method based on label-noise learning, which comprises the following steps: extracting text information to be mined from taxpayer industry information for text embedding, and performing feature processing on the embedded information; extracting, non-text information from the taxpayer industry information for encoding; constructing a BERT-CNN deep network structure, a number of neurons and the dimensionality of input and output in each layer and the number of target categories; pre-training the network constructed in the previous step through contrastive learning, nearest neighbor semantic clustering and self-labeling learning in turn; adding, a noise modeling layer on the basis of the constructed deep network, modeling label noise distribution through network self-trust and noisy label information, and performing model training; taking the deep network before the noise modeling layer as a classification model, and classifying taxpayer industries.

The present disclosure is directed to conducting electronic transactions via a computer network. A server receives data packets carrying data identifying a merchant category, an electronic account maintained and configured on the server, and a first monetary amount of a first electronic transaction. A first device generates the data packets at the merchant to conduct the electronic transaction. A policy engine selects a first purse of a plurality of purses allocated to the electronic account based on a policy applied to the data. The first purse may be configured as a benefits purse. The server obtains the first monetary amount of the electronic transaction from the first purse. The policy engine applies a second policy to the data to determine a reimbursement amount based on the first monetary amount of the transaction, and electronically provides the reimbursement amount to a different second purse of the electronic account.

The present disclosure is directed to conducting electronic transactions via a computer network. A server receives data packets carrying data identifying a merchant category, an electronic account maintained and configured on the server, and a first monetary amount of a first electronic transaction. A first device generates the data packets at the merchant to conduct the electronic transaction. A policy engine selects a first purse of a plurality of purses allocated to the electronic account based on a policy applied to the data. The first purse may be configured as a benefits purse. The server obtains the first monetary amount of the electronic transaction from the first purse. The policy engine applies a second policy to the data to determine a reimbursement amount based on the first monetary amount of the transaction, and electronically provides the reimbursement amount to a different second purse of the electronic account.

Systems and methods of taxation analysis for asset transactions are disclosed. A system may be configured to obtain data associated with a transaction to be executed, obtain a request to determine a tax implication of the transaction, generate a first tax return (with the transaction being excluded from use in generating the first tax return), and generate a second tax return based on the request (with the transaction being included for use in generating the second tax return). The system may also be configured to compare the first tax return and the second tax return, generate one or more results associated with the transaction based on the comparison, and provide the one or more results. The one or more results may indicate an impact on total taxes owed, deductions, loss of tax credits, or other impacts caused by the proposed transaction.

A technique, comprising: identifying, at a node forming part of a group of nodes operating a distributed ledger comprising a block chain, a decision to retrospectively change a first smart contract prescribing one or more changes in the ledger already recorded in one or more existing blocks of the blockchain; and, without changing any existing blocks of the blockchain, updating the ledger by adding to the current end of the blockchain a new block indicating a new state of the ledger determined by a re-execution of all operations, according to the first smart contract and one or more other smart contracts, recorded in one or more blocks of the blockchain after a block recording an operation of the first smart contract, while taking into account the change to the first smart contract.

A technique, comprising: identifying, at a node forming part of a group of nodes operating a distributed ledger comprising a block chain, a decision to retrospectively change a first smart contract prescribing one or more changes in the ledger already recorded in one or more existing blocks of the blockchain; and, without changing any existing blocks of the blockchain, updating the ledger by adding to the current end of the blockchain a new block indicating a new state of the ledger determined by a re-execution of all operations, according to the first smart contract and one or more other smart contracts, recorded in one or more blocks of the blockchain after a block recording an operation of the first smart contract, while taking into account the change to the first smart contract.

Systems and methods related to resource distribution for a fleet of machines are disclosed. A system may include a fleet of machines each having an associated resource capacity and a resource requirement to perform a task. The system may further include a controller having a resource requirement circuit to determine an aggregated amount of the resource requirement and an aggregated amount of the resource capacity. A resource distribution circuit may adaptively improve, in response to an aggregated amount of the resource capacity, an aggregated resource delivery of the resource.