Cisco Technology, Inc.

Samsung Electronics Co., Ltd.

Accenture Global Solutions Limited

Abhishek Mukherji

Santosh Ghanshyam Pandey

Rong Peng

Huy Phuong Tran

Vinay S Raghuram

Xu Zhang

Matthew Aaron Silverman

Abhishek Bhattacharyya

Paul Jeffrey Stager

Emmanuel Munguia Tapia

Balaji Gurumurthy

Prasad Walawalkar

Evan Welbourne

Santosh Ramrao Patil

Hugo Latapie

Enzo Fenoglio

Anwitha Paruchuri

Shalinder Singh Sidhu

Yoshiya Hirase

Oscar Bejarano Chavez

Anshuma Chandak

Satheesh Sudarsan

Yannick Pellet

Liu Huang

Enamul Hoque

Jacob Earl Fussell

Oscar Berjarano Chavez

Edmund Cameron Duhaime

Chayan Bhaisare

Jenny Marie Yoshihara

In some examples, structure-based multi-intent email classification may include receiving an email thread that includes a plurality of emails including an email that is to be classified, and identifying, for the email thread, a process associated with the email thread, Based on the process associated with the email thread and for each sentence of the email that includes a plurality of sentences, a corresponding classifier may be determined from a plurality of classifiers, and applied to each sentence of the email to generate classified sentences. At least one entity may be extracted from each of the classified sentences of the email. Based on the at least one extracted entity, the email may be classified.

Structure-based multi-intent email classification

Systems and methods provide for improving the accuracy of a location system. The location system can capture partial phase vector data from one or more access points (APs). The location system can capture associated data associated with the partial phase vector data across multiple dimensions, such as identity data of the APs and client devices generating the partial phase vector data and frequency band data, location data, a time and date, and other data associated with the partial phase vector data. The location system can determine correlation data across the multiple dimensions using the first partial phase vector data and the associated data. The location system can a cause of the partial phase vector data based on the correlation data. The location system can perform one or more remediation actions based on the cause of the partial phase vector data.

Partial phase vectors as network sensors

Offloading of location computation from a location server to an access point through the use of projections on base phase vectors may be provided. First, an Access Point (AP) may receive a set of two or more base phase vectors from a location server. Next, the AP may measure a measured phase vector for a first signal from a user device. Then, the AP can determine projection values based on a comparison of the measured phase vector to each base phase vector. From these comparisons, the AP can determine a subset of base phase vectors with the highest projection values. The AP can then send the projection values and the subset of base phase vectors to the location server, wherein the location server determines the device location from these projection values and subset of base phase vectors.

Offloading location computation from cloud to access point (AP) with projection on base phase vectors

Location-based, context-aware challenge-response authentication may be provided. First, a challenge may be provided to a user. The challenge may be based on a context corresponding to the user. The context corresponding to the user may comprise a location of a device associated with the user within an environment. Next, in response to providing the challenge, a response to the challenge may be received from the user. Then, in response to receiving the response to the challenge, it may be determined that the response is a correct answer to the challenge. In response to determining the response is the correct answer, a privilege may be provided to the user.

Location-based, context-aware challenge-response authentication

Determining a device's location in a space in real time is computing intensive. To offload some of the workload in conducting this hyperlocation, the access points in the network conduct some of process in determining the location of a device. The cloud determines a restricted AoA search area based on previous client locations. After this determination, a three-dimensional (3D) AoA search is conducted by each AP in the restricted area (restricted by a range of azimuth directions) for a device. Finally, each AP reports a location(s) for the device, which comprises weights for selected angular sectors. The cloud can then construct a probability heat map for location computation from the weights provided from each AP for the device.

Scalability of location in the cloud with angle of arrival (AOA) search on the edge

In one embodiment, a service obtains spatial information regarding a physical area. The service estimates locations of a device within the physical area over time, based on wireless signals sent by the device. The service generates a set of images based on the spatial information regarding the physical area and on the estimated locations of the device within the physical area over time. The service updates an estimated location of the device by inputting the generated set of images to a machine learning model trained to minimize a location estimation error.

Image-based framework for improving location estimate accuracy in indoor positioning systems

In one embodiment, a traffic analysis service receives payload data from packets sent by a sensor tag in a network. The service forms a payload signature for the sensor tag, based on the payload data. The payload signature is indicative of one or more bytes in the payload that vary across the packets. The service identifies a portion of the payload data as potentially including a sensor measurement, based on the payload signature. The service uses a machine learning classifier to assign a sensor measurement type to the identified portion of the payload data.

Vendor agnostic sensor telemetry detection, processing, and identification

An enterprise system configures access point devices at an enterprise location to communicate with a location determination system. The location determination system receives wireless signal attributes of user computing devices broadcasting Wi-Fi signal data at the enterprise location from one or more access point devices. For a particular time window, the location determination system determines aggregated features of received wireless signal data across all access point devices, and classifies each of the user computing devices as moving or stationary by applying the wireless signal data to a model. For each of the user computing devices determined to be moving, the location determination system calculates a respective position of the user computing device based on the wireless signal data. For each of the user computing devices determined to be stationary, the location determination system does not calculate a respective position of the respective user computing device.

Motion detection for passive indoor positioning system

A plurality of access points located within a building structure may provide various location-based services, such as navigation, to mobile devices within the building structure. To more accurately determine the location of the plurality of access points, a floorplan of the interior of the building structure may be obtained. A plurality of images of the interior of the building structure is then obtained. At least one of the images includes an image of at least one of the plurality of access points. A plurality of reference points may also be located within the building structure and obtained. Based on the plurality of images, reference points, and the floorplan, a composite image of the interior of the structure is generated. Based on the composite image, the location of each of the plurality of access points is determined.

Precise access point placement and location measurement

Techniques for generating a prediction for a location of a mobile device includes a network controller obtaining signal strength information and angle-of-arrival information for mobile devices from a plurality of access points disposed at a location that includes two or more zones. The network controller extracts features from the obtained information. The features indicate whether the mobile devices are more likely in a first zone than in one or more of second zones of the two or more zones. The network controller generates a software model for classifying a mobile device into one of the two or more zones by applying a computer learning process to the extracted features. The network controller uses the software model to generate a prediction indicating that a first mobile device is in one of the two or more zones, and performs an action at the location based on the prediction.

Device location classification using data collected by wireless access points

In one embodiment, a device determines that location accuracy performance of an indoor positioning system deployment is below a predefined threshold. The device obtains characteristic data for the indoor positioning system deployment. The device identifies, by using the characteristic data as input to a machine learning model, one or more contributing factors from the characteristic data for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold. The device initiates a remediation action based on the identified one or more contributing factors for the location accuracy performance of the indoor positioning system deployment being below the predefined threshold.

Location accuracy assessment and remediation for indoor positioning system deployments

In one embodiment, a device receives location estimates for a wireless node in a network, each location estimate having an associated timestamp. The device applies hierarchical clustering to the received location estimates and their associated timestamps, to identify locations and points in time in which the wireless node was stationary. The device performs sequence modeling on the identified locations and points in time in which the wireless node was stationary, to form a sequence of locations and associated time periods in which the wireless node was stationary. The device associates the wireless node with a behavioral profile based on the sequence of locations and associated time periods in which the wireless node. The device generates, based in part on the behavioral profile for the wireless node, a predictive model that predicts a location of the wireless node at a particular point in time.

Characterizing movement behaviors of wireless nodes in a network

It may be determined that each of a first plurality of devices was stationary at a location based on received first location information. Next, a first estimated ground truth may be determined for each of the first plurality of devices at the location based on the first location information corresponding to each of the respective first plurality of devices at the location. A first plurality of location estimation errors may then be determined for each of the first plurality of devices at the location respectively based on the first location information corresponding to each of the respective first plurality of devices at the location and the first estimated ground truth for each of the respective first plurality of devices at the location. Next, each of the determined first plurality of location estimation errors for each of the first plurality of devices at the location may be aggregated to provide a first location accuracy measurement for the location.

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Fremont, CA, United States of America

Abhishek Mukherji