Hewlett-packard Development Company, L.P.

David Murphy

Madhu Sudan Athreya

Wagston Staehler

Juliano Vacaro

Alexandre Santos Da Silva, Jr

Thomas Da Silva Paula

Gabriel Rodrigo De Lima Paz

Jonathan Salfity

Qian Lin

Sunil Bharitkar

Will Allen

Joao Edwardo Carrion

Caio Northfleet

Juliano Cardoso Vacaro

Joao Edurado Carrion

Thiago Jose Da Silva

Joao Eduardo Carrion

Wagston Tassoni Staehler

A method includes receiving, with a computing device, a first client request from a first client that identifies a machine learning model and a sensor. The method includes sending, with the computing device, a call to a server to apply the identified machine learning model to a set of data from the identified sensor, in response to the first client request. The method includes receiving, with the computing device, a second client request from a second client that identifies a same machine learning model and sensor as the first client request. The method includes sending, with the computing device, response data from the identified machine learning model to both the first client and the second client without sending an additional call to the server in response to the second client request.

Responding to machine learning requests from multiple clients

An example robot performs a scan to obtain image data of a given region. The robot performs image analysis on the image data to detect a set of undesirable objects, and generates a reference map that excludes the set of undesirable objects, where the reference map is associated with the location of the robot at the time of the scan.

Mobile robots to generate reference maps for localization

An example control system includes a memory and at least one processor to obtain image data from a given region and perform image analysis on the image data to detect a set of objects in the given region. For each object of the set, the example control system may classify each object as being one of multiple predefined classifications of object permanency, including (i) a fixed classification, (ii) a static and fixed classification, and/or (iii) a dynamic classification. The control system may generate at least a first layer of a occupancy map for the given region that depicts each detected object that is of the static and fixed classification and excluding each detected object that is either of the static and unfixed classification or of the dynamic classification.

Mobile robots to generate occupancy maps

A method includes receiving, with a computing device, a configuration file and a client request to apply a machine learning model to a set of data from a sensor. The method includes performing, with the computing device, preprocessing on the set of data from the sensor based on the configuration file to generate preprocessed data. The method includes sending, with the computing device, a call to a model server to apply the machine learning model to the preprocessed data.

Preprocessing sensor data for machine learning

One example of a video monitoring system includes a frame acquisition subsystem, a stage gate motion detection subsystem, a person detection subsystem, a face recognition subsystem, and an alert emission subsystem. The frame acquisition subsystem extracts frames from an input video. The stage gate motion detection subsystem separates background motion from foreground motion within frames. The person detection subsystem detects people including faces and bodies within the foreground motion. The face recognition subsystem matches detected faces to previously registered users. The alert emission subsystem provides alerts based on events detected by the stage gate motion subsystem, the person detection subsystem, and the face recognition subsystem.

Video monitoring

According to examples, an apparatus may include a processor and a memory on which is stored machine readable instructions that are to cause the processor to determine a reference frame from a plurality of frames received at multiple different times, in which each of the plurality of frames includes audio signal data, and in which the reference frame includes audio signal data that identifies a highest audio signal level among audio signals identified in the plurality of frames. The reference frame may be time-aligned with each of the plurality of frames other than the reference frame to obtain respective time-aligned frames. The audio signals identified in each of the respective time-aligned frames may be added together to generate respective added audio signals. The respective added audio signals may be combined together to obtain a combined audio signal and the combined audio signal may be outputted.

Combined audio signal output

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Palo Alto, CA, United States of America

David Murphy