Derry, Paul Stefan

A computing system can facilitate discovery and one-on-one communication among multiple software applications of a multi-application execution environment. The system can enable software applications to broadcast and/or detect advertisement messages indicative of discoverable software applications and supported capabilities, such as for targeted messaging, sharing state, and/or the like. The system can enable software applications to detect and invoke capabilities of other software applications from outside said software applications. The system can enable software applications to share their states such that states are synchronized among multiple software applications. In some embodiments, the system can cause generation and display of interactive graphical user interfaces (GUIs) and/or of various GUI elements configured to indicate and/or facilitate discovery and/or one-on-one communication among multiple software applications, such as for detecting and invoking functionalities of software applications from outside said software applications, and/or such as for sharing states among multiple software applications.

The technology relates to cargo vehicles. National, regional and/or local regulations set requirements for operating cargo vehicles, including how to distribute and secure cargo, and how often the cargo should be inspected during a trip. However, such regulations have been focused on traditional human-driven vehicles. Aspects of the technology address various issues involved with securement and inspection of cargo before a trip, as well as monitoring during the trip so that corrective action may be taken as warranted. For instance, imagery and other sensor information may be used to enable proper securement of cargo before starting a trip. Onboard sensors along the vehicle monitor the cargo and securement devices/systems during the trip to identify issues as they arise. Such information is used by the onboard autonomous driving system (or a human driver) to take corrective action depending on the nature of the issue.

A system and methodology is provided in which inductive liveness techniques are employed to conduct identity verification operations. These techniques may include a wide variety of signals generated by an identity verification application and which are caused to be implemented on a user device such that a live person is stimulated and/or encouraged to respond in a way that is detectable via the device and which may be processed by the identity verification application to assess the likelihood of the user actually being a live person. These signals may include various non-overt prompts stimulating the user to respond in ways such as predictable eye movements, predictable blinking, predictable head or body movements in the context of selfie alignments as well as many other signals.

Implementations are directed to generating voice-based chatbot policy override(s) and/or utilizing voice-based chatbot policy override(s) in conjunction with existing voice-based chatbot(s). The voice-based chatbot policy override(s) can correspond to, for example, machine learning (ML) model(s) that supplement functionality of the existing voice-based chatbot(s). Notably, the voice-based chatbot policy override(s) are associated with rule(s) (e.g., by virtue of training the ML model(s) that correspond to the voice-based chatbot policy override(s)) for when the voice-based chatbot policy override(s) should be utilized in lieu of the existing voice-based chatbot(s) in responding to spoken utterance(s) of human user(s) engaged in corresponding conversation(s) with the voice-based chatbot policy override(s). Nonetheless, from a perspective of the human user(s), it appears as if they are still engaging in the corresponding conversations with the existing voice-based chatbot(s). Thus, the functionality of the existing voice-based chatbot(s) can be supplemented without having to re-train the existing voice-based chatbot(s).

An end to end system and methodology is employed to obtain key-value pairs from original documents and images. The system and methodology of the present invention may generate synthetic training data from a single source document image or a relatively small number of source document images and use the aforesaid synthetic training data to train a model which is capable of extracting key value pairs from documents even when the source documents do not contain a machine readable source of ground truth data and/or when there is a limited number of source documents available for model training.

Provided are a method and system for digital user identification in use of corresponding personally identifiable information (PII) as an instrument of verifying the identification. For one or more iterations of execution of the method and system, aggregated user data obtained by a server and corresponding to a digital user defines a digital profile confirmed for PII transacted at the time of the aggregation. Accordingly, and when one or more components of the profile are inspected for deviation thereof relative to the profile, as confirmed, legitimacy of the digital user identification is determined.

Aspects of the disclosure relate to a method of managing a fleet of autonomous vehicles providing trip services. The method includes receiving information identifying an intermediate destination and a final destination for a trip 900. In this example, the intermediate destination 920 is a destination where an autonomous vehicle 100 will drop off and wait for a passenger in order to continue the trip, and the final destination 930 is a destination where the trip ends. The method also includes determining an amount of waiting time the vehicle is likely to be waiting for the passenger at the intermediate destination, determining how a vehicle of the fleet of autonomous vehicles should spend the amount of waiting time, and sending an instruction to the vehicle, based on the determination of how the vehicle should spend the amount of waiting time.

Implementations relate to determination and visual display of spoken menus for calls. In some implementations, a computer-implemented method includes receiving audio data output in a call between a call device and a device associated with a target entity. The audio data includes speech indicating one or more selection options for a user of the call device to navigate through a call menu provided by the target entity in the call. Text is determined by programmatically analyzing the audio data, the text representing the speech. The selection options are determined based on programmatically analyzing at least one of the text or the audio data. At least a portion of the text is displayed by the call device during the call, as one or more visual options that correspond to the selection options. The visual options are each selectable via user input to cause corresponding navigation through the call menu.

A liquid cooling system comprising a printed circuit board (130), a gasket (115) seated on a top side of the printed circuit board, the gasket encircling one or more components (105) on the printed circuit board (130), an enclosure (220, 260) on the gasket (115) and fastened against the printed circuit board (130) to form a sealed chamber, and a transfer liquid (838) within the sealed chamber contacting one or more components (105) mounted on the printed circuit board (130) to provide cooling for the one or more components (105).

In an aspect, a data processing system includes a computer-readable memory comprising computer-executable instructions, and at least one processor configured to execute executable logic including at least one artificial neural network trained to predict one or more responses to a chemical substance by identifying one or more discrete biological tissue components in a biological image. When the at least one processor is executing the computer-executable instructions, the at least one processor is configured to carry out operations including: receiving spatially arranged image data representing a biological image of a patient; generating spatially arranged image tile data representing a plurality of image tiles; processing the spatially arranged image tile data through one or more data structures storing one or more portions of executable logic included in the artificial neural network to predict one or more responses of a patient.