Abstract: A method of preparing an antibody therapeutic is provided comprising: (a) providing a dissociated cell sample from at least one solid tumor sample obtained from a patient; (b) loading the dissociated cell sample into a microfluidic device having a flow region and at least one isolation region fluidically connected to the flow region; (c) moving at least one B cell from the dissociated cell sample into at least one isolation region in the microfluidic device, thereby obtaining at least one isolated B cell; and (d) using the microfluidic device to identify at least one B cell that produces antibodies capable of binding to cancer cells. The cancer cells can be the patient's own cancer cells. Also provided are methods of treating patients, methods of labeling or detecting cancer, engineered T or NK cells comprising antibodies or fragments thereof, and engineered antibody constructs.

Abstract: Systems and methods for generating real-time location-based notifications are described. In some aspects, the system receives a recurring user activity that is recurring at a previous location during a recurring time period. Based on detecting that a current location of the user is in proximity of the previous location during an instance of the recurring time period, the system determines whether any user activity was executed while the user is present at the current location. Based on determining that a current user activity was executed by the user different from the recurring user activity, the system determines whether the current user activity is a substitute for an instance of the recurring user activity. The system updates a measure of elasticity of the recurring user activity based on determining that the current user activity is a substitute for the instance of the recurring user activity.

Abstract: Systems as described herein generate an outstanding check alert. An alert generating server may receive transaction records associated with a plurality of checking accounts. The alert generating server may user a first machine learning classifier to determine a transaction pattern indicating a merchant has failed to process outstanding checks for a period of time. The alert generating server may receive sequential check information comprising at least one missing check number associated with a particular checking account. The alert generating server may user a second machine learning classifier to determine at least one outstanding check associated with the particular checking account. The alert generating server may send an alert indicating the at least one outstanding check to a user device.

Abstract: In some implementations, a system may receive interaction data associated with an interaction between a user and an interaction party. The system may determine, from the interaction data, one or more interaction characteristics associated with the interaction or one or more interaction party characteristics associated with the interaction party, which the system may provide as inputs to a machine learning model trained using supervised learning and historical interactions of the user or one or more other users with the interaction party or one or more other interaction parties. The system may receive an output, based on applying the machine learning model to the interaction data, indicating one or more categories for categorizing the interaction, herein the one or more categories correspond to a subsequent action or recommendation related to the interaction. The system may store a record that includes the interaction data and information identifying the one or more categories.

Abstract: Methods and systems use an additional determination to generate predictions of future data processing load predictions. Specifically, the methods and systems generate additional predictions based on other data sets and data calculation methodologies (e.g., data with non-homogenous time dependencies). The methods and systems then use these additional predictions to determine whether or not the prediction of the real-time average of data processing loads is an outlier. Thus, the methods and systems generate a time-specific data processing prediction.

Abstract: In some implementations, a system may receive exchange data that includes a credential for an exchange and an indication of an entity associated with the exchange. The system may determine that an entity location is within a threshold distance of a target location included in a set of target locations. The system may determine a baseline location associated with a user. The system may determine that the baseline location is outside of a threshold proximity of all target locations included in the set of target locations. The system may transmit, to a mobile device associated with the user, a message that includes an indication of the target location based on determining that the entity location is within the threshold distance of the target location and based on determining that the baseline location is outside of the threshold proximity of all target locations included in the set of target locations.

Abstract: In some implementations, a system may receive exchange data that includes a credential for an exchange and an indication of an entity associated with the exchange. The system may determine that an entity location is within a threshold distance of a target location included in a set of target locations. The system may determine a baseline location associated with a user. The system may determine that the baseline location is outside of a threshold proximity of all target locations included in the set of target locations. The system may transmit, to a mobile device associated with the user, a message that includes an indication of the target location based on determining that the entity location is within the threshold distance of the target location and based on determining that the baseline location is outside of the threshold proximity of all target locations included in the set of target locations.

Abstract: In some implementations, a system may receive exchange data that includes a credential for an exchange and an indication of an entity associated with the exchange. The system may determine that an entity location is within a threshold distance of a target location included in a set of target locations. The system may determine a baseline location associated with a user. The system may determine that the baseline location is outside of a threshold proximity of all target locations included in the set of target locations. The system may transmit, to a mobile device associated with the user, a message that includes an indication of the target location based on determining that the entity location is within the threshold distance of the target location and based on determining that the baseline location is outside of the threshold proximity of all target locations included in the set of target locations.

Abstract: Systems as described herein generate an outstanding check alert. An alert generating server may receive transaction records associated with a plurality of checking accounts. The alert generating server may user a first machine learning classifier to determine a transaction pattern indicating a merchant has failed to process outstanding checks for a period of time. The alert generating server may receive sequential check information comprising at least one missing check number associated with a particular checking account. The alert generating server may user a second machine learning classifier to determine at least one outstanding check associated with the particular checking account. The alert generating server may send an alert indicating the at least one outstanding check to a user device.