Transaction modification based on modeled profiles is described. In an example, transaction data can be received from merchant computing devices associated with merchants associated with a payment processing system. A model can be trained to generate profiles using, as training data, one or more of merchant data, buyer data, or the transaction data. Upon receiving an indication of a particular transaction between a buyer and a merchant, it can be determined that a characteristic of the transaction corresponds to a profile of the generated profiles. Based on the determination that the characteristic corresponds to the profile and the transaction data, a recommendation can be generated for a modification of the transaction to add an item or replace an item.

Techniques are disclosed utilizing cognitive computing to improve commercial communications from vendors to users. A user's financial account(s) and location may be monitored to determine when a user is within a threshold distance of a vendor. If the user is within the threshold distance the methods and systems disclosed may determine which targeted commercial communications to transmit to the user based upon a shopping profile for the user. The shopping profile may include a dataset indicative of the shopping habits of the user.

Embodiments of sales generation, including sales generation employing reverse lead generation using vehicle data systems and methods, are presented herein. In particular, in certain embodiments a user may utilize the vehicle data system to obtain pricing data corresponding to a desired vehicle configuration. When the user is presented with the pricing data associated with the specified vehicle configuration the user may additionally be presented with an upfront price offered by a dealer, where by providing their personal information the user may obtain the name of the dealer offering the upfront price and may additionally be offered the opportunity to purchase the desired, or similar, vehicle at the upfront price.

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for semiconductor fabrication processes are described. A method may include accessing a distributed ledger including an instruction set for a semiconductor fabrication process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and, in response to the access request, providing a provable access to the instruction set. The method may further include providing commands to a production tool of the semiconductor fabrication process in response to the instruction set access request, and recording the transaction on the distributed ledger.

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for biological production processes are described. A method may include accessing a distributed ledger comprising an instruction set for a biological production process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and, in response to the access request, providing a provable access to the instruction set. The method may further include providing commands to a production tool of the biological production process in response to the instruction set access request and recording the transaction on the distributed ledger.

Transaction-enabled methods for providing provable access to a distributed ledger with a tokenized instruction set for polymer production processes are described. A method may include accessing a distributed ledger comprising an instruction set for a polymer production process and tokenizing the instruction set. The method may further include interpreting an instruction set access request and providing a provable access to the instruction set. The method may further include providing commands to a production tool of the polymer production process and recording the transaction on the distributed ledger.

A gaze initiated interaction technique for facilitating social networking in augmented reality. Methods are shown which allow users to interact with each other following an event of interlocking gaze. Methods are also shown which allows tagging using augmented reality filters following a gaze interlock. Methods are also introduced to allow an augmented reality target to determine themselves how they would like to be enhanced in augmented reality following gaze interlocks. This disclosure also introduces a device for social networking in augmented reality which comprises a gaze-interlock event detection circuit and a gaze-interlock event processing unit.

A new product development system may provide, to one or more client devices and server devices, one or more tools to enable development of a new product. The new product development system may receive current project data identifying a current project for developing the new product, historical project data identifying historical projects for developing historical products, and industry data associated with the new product. The new product development system may process the current project data, the historical project data, and the industry data, with a machine learning model, to identify one or more variables for a predictive model and may process the one or more variables, with the predictive model, to predict a predicted success rate of the current project. The new product development system may perform one or more actions based on the predicted success rate of the current project.

A system for training a bidding model comprising: a plurality of tactics stored on at least one database; a plurality of hyperparameters; in response to an available inventory from a publisher relayed through a real time bid server, computing a bid on the available inventory; sending the bid to the real time bid server; receiving an auction result in response to the bid; calculating a plurality of rewards based on the auction result and the tactics; calculate a plurality of q values based on the rewards; calculate a plurality of losses; backpropogating the losses through the bidding model.

In an example embodiment, multiple machine learned models are used to continually learn from data to update various prediction models. Prediction of item prices, at the production and distribution point level, and utilizing this information along with other item information, such as crop yield, operational cost, and storage cost in the case of arming, may be used to solve the long- and short-term planning problems of farmers and help in decision making based on daily and the most up-to-date forecasts.