Systems and methods for automatically soliciting the purchase of a first or second machine-related resource in a forward market, wherein the first resource and the second resource are distinct instances of the same type of resource, are described. A sample system may include a fleet of machines, each having a resource requirement comprising at least two of: a compute resource, a spectrum resource, or a network bandwidth resource. The system may include an circuits to aggregate data corresponding to the machine-related resources from at least a behavioral data source, to determine a substitution cost of a second resource; to determine a machine-related resource acquisition value; and to automatically solicit a purchase, in a forward market, of one of the first resource or the second resource in response to the determined substitution cost of the second resource.

Systems, apparatuses and methods may provide for technology to receive first payment details of a first transaction, where the first transaction is an online purchase by a user from a first merchant. The technology identifies a multiple-transaction selection associated with the first transaction. The technology receives second payment details of a second transaction, where the second transaction is an online purchase by the user from a second merchant. The technology performs a single security validation to validate the first payment details and the second payment details.

A computer system monitors one or more communication applications on a device of a user. The computer system identifies a communication that corresponds to a payment from a plurality of communications on the one or more communication applications. In response to the identifying the communication that corresponds to the payment, the computer system automatically identifying one or more other users associated with the payment without prompting the user for information.

Systems and tools for sharing medical costs between prospective patients seeking treatment, and existing patients of a medical network. Prospective patients may actively query medical costs for specific conditions and treatments for the specific conditions previously experienced by existing patients. Existing patients contributing to the documents accessible by the medical network may upload anonymous versions of EOBs, medical bills and medical documents to the medical network in exchange for rewards. The prospective patients may communicate and query the desired information using natural language inputs to instruct a client computing system to search the medical network for relevant treatment information. The prospective patient may use natural language commands or other inputs to filter the query results by condition, location, cost, quality of service, service provider, insurance provider, or any other parameter that may be detailed by the medical documents accessible through the medical network.

Notifying customers of available efficiencies via updates to electronic menus, personal notifications, etc. is described herein. As an example, a computing system can receive a first request indicating that an item that is offered for sale by a merchant that has been requested by a customer and can determine an efficiency associated with combining the first request with a second request from another customer. Such an efficiency, for example, can comprise sharing an item on a menu or preparing multiple items together instead of individually. The computing system can notify, in near real-time and based at least partly on determining the efficiency associated with combining the first request and the second request, at least the other customer about the efficiency. In some examples, the other customer can be notified via an update to the electronic menu, a personal notification to a user computing device, etc.

Disclosed herein are system, method, and computer program product embodiments for providing recommendations for splitting bills. The approaches disclosed include the ability to obtain information about a bill to be split (such as a photo of the bill), and then use several machine learning models to determine the ‘who,’ ‘what,’ and ‘where’ of the underlying transaction. In particular, machine learning models described herein are used to perform facial recognition of a ‘selfie’ taken when a transaction was made against social media accounts to determine participants of the transaction. The machine learning models may also identify expected pricing from data about a merchant associated with the transaction, and expected amounts for each participant based on the expected pricing.

An online platform generates a playlist of clips of a lecture accessed by a plurality of users of the online platform. The online platform receives a recording of the lecture, and receives a plurality of events captured during a time period corresponding to the lecture. Each captured event is associated with a time stamp corresponding to a time at which a user performed an activity while listening to the lecture. The online platform clusters the captured events based on the time stamps, and generates one or more clips of the recording of the lecture from the clustered events. The online platform generates a playlist including the clips of the lecture.

Replacing discrete digital coin payment with a continuous payment flow to be used between devices on the Internet of Things. This pay-as-you-go, real-time payment regimen accommodates continuous, variable rate payment with no subsequent invoicing burden.

Methods for providing provable access to a distributed ledger with a tokenized instruction set are disclosed. A method may include accessing a distributed ledger including an instruction set, tokenizing the instruction set, interpreting an instruction set access request, and in response to the instruction set access request, providing a provable access to the instruction set.

Disclosed are methods, systems, and non-transitory computer-readable medium for managing transactions by consolidating associated transactions. For instance, a method may include receiving transaction information for a transaction. The transaction information may include an identifier and a transaction amount associated with the transaction. The method may further include comparing the transaction amount to a threshold to determine whether the transaction amount is equal to or above the threshold, or whether the transaction amount is below the threshold. The method may include, in response to determining that the transaction amount is below the threshold, obtaining pending transaction information, analyzing the pending transaction information to determine whether there is at least one associated transaction associated with the transaction, and performing at least one processing action to resolve the transaction.