Abstract: A building system including one or more storage devices storing instructions thereon that, when executed by one or more processors, cause the one or more processors to ingest information associated with a physical asset of a building, the physical asset being an indoor air quality device. The instructions further cause the one or more processors to cause a graphical model of the building to include a heat map of one of infection risk or particulate concentration overlaid onto a floor of the graphical model based on the information. The instructions further cause the one or more processors to generate a building layout recommendation based on the heat map of the one of the infection risk or the particulate concentration. The instructions further cause the one or more processors to cause a display device of a user device to display the graphical model including the heat map and the building layout recommendation within a user interface.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: Systems and methods of the present disclosure enable intelligent dynamic caching of data by accessing an activity history of historical electronic activity data entries associated with a user account, and utilizing a trained entity relevancy machine learning model to predict a degree of relevance of each entity associated with the historical electronic activity data entries in the activity history based at least in part on model parameters and activity attributes of each electronic activity data entry. A set of relevant entities are determined based at least in part on the degree of relevance of each entity. Pre-cached entities are identified based on pre-cached entity data records cached on the user device, and un-cached relevant entities from the set of relevant entities are identified based on the pre-cached entities. The cache on the user device is updated to cache the un-cached entity data records associated with the un-cached relevant entities.

Abstract: An accessory device for use with a medical device includes a first cuff secured to a distal end of the medical device, a second cuff secured to the medical device proximal of the first cuff, an actuator, and at least one actuation wire extending from the first cuff, through the second cuff, to the actuator.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a trained spam upsurge detection machine learning model that determines when a current frequency associated with spam communications received by a current user exceeds a baseline frequency associated with the current user; receiving a permission indicator identifying a permission by the user to detect communications being received by the computing device; receiving an indication of at least one communication being received; determining the at least one communication as a particular spam communication; updating a frequency at which spam communications have been received by the user based at least in part on the particular spam communication; utilizing the trained spam upsurge detection machine learning model to determine that the frequency exceeds a baseline frequency associated with the user; and initiating a scan of one or more dark web resources.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from the user to monitor a plurality of activities executed within the computing device; continually monitoring the plurality of activities executed within the computing device for a predetermined period of time; identifying an indication of an incoming interaction session within the predetermined period of time; automatically verifying a session interaction parameter associated with the incoming interaction session; determining a plurality of inquires associated with the research interaction session based on the at least one session interaction parameter; identifying the plurality of demographic informational items associated with the user; and automatically updating the programmable GUI element to display the plurality of inquires associated with the interaction session and the plurality of demographic informational items associated with the user.

Abstract: A computer-implemented method comprising: instructing a computing device to obtain a permission from the user to monitor a plurality of activities executed within the computing device; receiving a call session request from a computing device associated with a second user; utilizing a trained machine learning algorithm to confirm an identity of the user based on the call session interaction parameter to confirm the call interaction session as a verified call interaction session; generating a fictitious call session interaction parameter that differs from the at least one call session interaction parameter; modifying the call session request into a verified call session request by replacing the call session interaction parameter with the fictitious call session interaction parameter; utilizing a telecommunication network to transmit the verified call session request with the at least one fictitious session interaction parameter to the first calling-enabled computing device; automatically instructing the first c

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.

Abstract: The present disclosure provides an exemplary method, system, and computing device that may include the steps of receiving a first indication that information of a user has been detected at one or more dark web resources; classifying the item of the compromised information into an information type category; receiving a permission indicator to detect communications by the computing device; receiving a second indication of a communication; receiving a third indication that the user engages an interaction with the communication; instructing the computing device to execute a technique to obtain data for the communication; receiving the data for the communication; determining the communication is a spam communication; determining a current information type category being discussed during the spam communication; making a determination that the current information type category corresponds to the information type category; and instructing a graphical user interface to display an alert to the user.

Abstract: Systems and methods of location-aware caller identification via machine learning techniques are disclosed. In one embodiment, an exemplary computer-implemented method may include: utilizing a trained call annotation machine learning model to determine one or both of an annotating condition and annotating location granularity, and associate one location of a user with one phone number of the user based at least on one or both of the annotating condition and the annotating location granularity; receiving second transactional information of one transaction associated with a first user; extracting second location information from the second transactional information of the one transaction; utilizing the trained call annotation machine learning model to automatically annotate one phone number record of one phone number of the first user, with the second location information at the annotating location granularity to form at least one user-specific location-specific annotated phone number record.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from the user to monitor a plurality of activities executed within the computing device; receiving monitoring data of the activities executed within the plurality of computing devices for a predetermined period of time; identifying incoming interaction sessions across the plurality of computing devices; verifying one common session parameter associated with the incoming interaction sessions to identify the incoming interaction sessions as suspect interaction sessions; determining a frequency metric for the suspect interaction sessions; determining a threshold value for the frequency metric; receiving new monitoring data; determining that the new incoming interaction session has at least one common session interaction parameter with the suspect interaction sessions; automatically generating an interaction notification for transmission to the computing device; receiving a response to the int

Abstract: Methods and systems are described herein for manipulating a communication acceptance screen, manipulating an interactive communication acceptance icon, and restricting access to accounts based on voice communication parameters. In particular, when a communication is detected from one device to another device, that communication may be risky. Thus, the risk is mitigated by giving a user information about the source of the communication to give a user a chance to reject the communication. In addition, in instances where the user accepts the communication, the system enables monitoring the communication and restricting any accounts that are disclosed within the communication.

Abstract: A surface cleaning apparatus including a body defining an agitation chamber, an agitator partially disposed within the agitation chamber and configured to rotate about a pivot axis, and a debrider at least partially disposed within the agitation chamber. The agitator includes an elongated body having a first and a second end, a sidewall extending radially outward from the elongated body extending between the first and the second ends, and a plurality of bristles extending radially outward from the elongated body. The plurality of bristles are arranged in at least one row adjacent to the sidewall. The debrider includes a plurality of teeth configured to contact a portion of the sidewall as the agitator rotates about the pivot axis.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of receiving input data from at least one external data aggregator; utilizing a trained machine learning algorithm to generate a database of known queries; receiving subsequent input data from the at least one external aggregator; automatically updating the database of known queries associated with the plurality of users; utilizing the trained machine learning algorithm to perform a cross-reference analysis to determine a presence of a data record within the database of known queries; dynamically removing the data record from the database of known queries; utilizing the trained machine learning algorithm to predict a trigger associated with the presence of the at least one data record; and instructing a computing device to initiate a verification of the presence of the at least one data record.

Abstract: Systems and methods for controlling a display system. The methods comprise: detecting an occurrence of an event; initiating a display session based on the event being of an event type to trigger a display session; and selecting media content (MC) for display by computing devices (ICDs) (wherein ICDs are located in areas or sections (SA/S) of a venue); selecting a first SA/S at which display of MC is to begin; identifying first CDs (FCDs) that are located in the first SA/S; causing FCDs to notify users that FCDs are to be moved into positions in which first pixel panels are visible by persons located outside of the first SA/S; and causing each FCD to display a respective pixel portion of a first electronic graphic (FEI) contained in MC, whereby an entirety of FEI is displayed collectively by FCDs.

Abstract: Telling a human apart from a computer using a capacitance module may include receiving a request for human verification; determining a human-verification prompt; communicating the human-verification prompt using a prompting device in the capacitance module; detecting a response to the human-verification prompt using a user input sensor in the capacitance module; determining whether a human is operating the capacitance module based, in part, on the response detected with the user input detector; and returning a human verification result.

Abstract: In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission to monitor a plurality of activities executed within the second computing device; continuously monitoring the plurality of activities executed within the second computing device for a predetermined period of time; receiving an indication of an incoming interaction session within the predetermined period of time; automatically verifying at least one session interaction parameter associated with the incoming interaction session to identify the incoming interaction session as a suspect interaction session; determining when a duration of time associated with the suspect interaction session meets or exceeds a predetermined duration threshold; utilizing a trained detection machine learning model to model a confidence value associated with the suspect interaction session; identifying at least one future activity subsequent to the suspect interaction session; and automatically instructing the firs

Abstract: A capacitance sensing module may include a capacitance sensor; a pressure sensor in mechanical communication with the capacitance sensor; a processor and memory; programmed instructions stored in the memory to cause the processor, when executed, to determine, with the capacitance sensor, a location of a user input; determine, with the pressure sensor, a pressure value of the user input applied to the capacitance sensor; and determine whether the pressure value exceeds a non-uniform actuation threshold value where the non-uniform actuation threshold value is based, at least in part, on the location of the user input on the capacitance sensor.

Abstract: One embodiment provides an apparatus for fluorescence lifetime imaging (FLI). The apparatus includes a deep neural network (DNN). The DNN includes a first convolutional layer, a plurality of intermediate layers and an output layer. The first convolutional layer is configured to receive FLI input data. Each intermediate layer is configured to receive a respective intermediate input corresponding to an output of a respective prior layer. Each intermediate layer is further configured to provide a respective intermediate output related to the received respective intermediate input. The output layer is configured to provide estimated FLI output data corresponding to the received FLI input data. The DNN is trained using synthetic data.

Abstract: An apparatus may include a stack of layers, the stack including a shield layer with a first surface, at least one capacitive sensor layer having at least one set of electrodes, which capacitive sensor layer is disposed within a first portion of the stack of layers, where the first portion of the stack of layers is located adjacent to the first surface of the shield layer, and an antenna incorporated in the first portion of the stack of layers.