Fuji Xerox Co., Ltd.

Fujifilm Business Innovation Corp.

Fuji Xerox Corporation

Jacob Biehl

Andreas Girgensohn

Miteshkumar Patel

Matthew L Cooper

William J Van Melle

Anthony Dunnigan

Althea Turner

Gerald Filby

Gene Golovchinsky

Daniel Avrahami

Eleanor Rieffel

Pernilla Quarfordt

Scott Carter

Adam Joseph Lee

Maani Ghaffari Jadidi

Lynn D Wilcox

Laurent Denoue

Maribeth Joy Back

Matthew L Lee

Kandha Sankarapandian

Stephen H Carman

Krishna Chaitanya Pavuluri

Jason Wiese

Gene Golovchinsky Deceased

Anbang Xu

Lucas Peter William Carrigan

Raphael Guenot-Falque

To prevent the spread of hospital-acquired infections, embodiments described herein monitor mobile device usage in clinical settings (by a physician, nurse, etc.) to characterize potential pathogenic contamination of the mobile device, the user's hands, and the surfaces in contact with the device. Our method considers the physical properties of how the device is handled and held during use, as well as the location and setting before, during, and after use to estimate potential contamination and transmission of pathogens. A system can use the estimation, along with knowledge (e.g., of the user's upcoming schedule) to alert them of potential contamination hazards and suggest when to avoid mobile device use as well as when to sanitize the device and user's hands.

System and method for estimating pathogen transfer from mobile interaction in clinical environments and a warning system and method for reducing cross-contamination risks

A computer-implemented method performed in a computerized system incorporating a central processing unit, a localization signal receiver, a plurality of sensors, separate and distinct from the localization signal receiver, and a memory, the computer-implemented method involving: using the central processing unit to initialize a plurality of particles based on an information on a map graph; using the central processing unit to repeatedly execute a particle filter loop, wherein the particle filter loop includes: using the central processing unit to perform a motion update of the plurality of particles; using the central processing unit to perform a measurement update of the plurality of particles; and using the central processing unit to perform a resampling of the plurality of particles based on particle importance weights and the map graph information. The location of the computerized system is subsequently determined based on the plurality of particles.

Systems and methods for utilizing graph based map information as priors for localization using particle filter

A system and method that extend the protections provided by the existing state-of-the-art to provide location-aware two-factor authentication for authenticating users of computer systems. There are many potential use cases where location-aware two-factor authentication could be of value. For instance, for purposes of access to critical business documentation, such as intellectual property, financial data, sales data for publicly traded companies, and personal medical information are all heavily protected information artifacts in most organizations. Providing controls to insure this information is only accessed in secure, trusted locations could greatly reduce the potential of inappropriate information access.

Systems and methods for location-aware two-factor authentication

A system and method for automating scheduling and coordination activities associated with the organizational responsibilities of hosting visitors (whether from the same company or an external organization). System leverages location technologies and other services to track the location of visitors, hosts, and other key participants related to the agenda items. The system performs the matching tracking information of visitor(s), participant(s), and the location of agenda event(s). The design of the described system rests on the assumption that visitors are a special type of person in an organization whose movements are actively tracked and managed through organizational policies and/or practices.

Systems and methods for automatic awareness and management of corporate visitor scheduling and coordination

This disclosure focuses on providing a technology framework for creating secure, location-based applications. The disclosure details a technology solution that can allow users to access secure information on commodity computation devices (e.g., tablets and smartphones) without a heavy infrastructure burden. The technology consists of a collection of software services that run on end-user devices, services on network accessible servers (e.g., cloud), and a specialized device that provides high-assurance location services atop a tamper resistant, trusted computation platform.

Systems and methods for high-assurance indoor proximity and location

System is built using the particle filter (PF) framework which utilizes data from round trip time (RTT) ranging or BLE signal strength to perform both (1) sensing the environment by using the scan data during measurement phase and (2) detect motion state of the user which it utilized during the motion update phase of PF. To detect the motion state of the user the temporal difference of the received ranging scans is used over which is utilized within the PF framework. The primary advantage of the described technique is that the velocity parameter of the PF can dynamically be updated based on the motion state as estimated using ranging scans.

Radiofrequency based virtual motion model for localization using particle filter

A probabilistic motion model to calculate the motion parameters of a user's hand held device using a noisy low cost Inertial Measurement Unit (IMU) sensor. Also described is a novel technique to reduce the bias noise present in the aforesaid IMU sensor signal, which results in a better performance of the motion model. The system utilizes a Particle Filter (PF) loop, which fuses radio signal data accumulated from Bluetooth Low Energy (BLE) beacons using BLE receiver with the signal from IMU sensor to perform localization and tracking. The Particle Filter loop operates based on a Sequential Monte Carlo technique well known to persons of ordinary skill in the art. The described approach provides a solution for both the noise problem in the IMU sensor and a motion model utilized in the Particle Filter loop, which provides better performance despite the noisy IMU sensor.

System and method for calibration-lessly compensating bias of sensors for localization and tracking

A computer-implemented method, the method being performed in a computerized system incorporating a central processing unit, a localization signal receiver, a display and a memory, the computer-implemented method involving: receiving a request from a user for a content; receiving at least one localization signal using the localization signal receiver; determining a location based on the received localization signal; using a plurality of content access rules to determine whether the requested content is authorized to be accessed from the determined location; and providing content to the user only if the content is authorized to be accessed from the determined location.

Systems and methods for secure location-based document viewing

A novel solution to generate an optimal spatial placement map of Radio Frequency (RF) beacons, such as Bluetooth Low Energy (BLE) beacons, which can be used for indoor localization. A described embodiment solves for both generating optimal number of beacons required in a given environment and optimizing their location. The solution also incorporates behavior of RF signal due to various environmental factors such as signal reflection and absorption due to static and dynamic obstacles. In one embodiment, the framework is built upon Genetic Algorithm (GA) for optimization of beacon placement. The experimental results achieved using one implementation of the described method reduced the number of beacons by 50% as compared to beacons placed using expert knowledge. Furthermore, the overall localization error increased by &#x2dc;0.4 m when the results using the RF/BLE map generated by the described system were compared with the RF/BLE map generated using expert knowledge.

System and method for optimizing placement and number of radio-frequency beacons to achieve better indoor localization

A system and method, which utilizes incremental smoothing and mapping (iSAM) algorithm and automatically builds a beacon location map using various sensor and environmental information. The aforesaid iSAM algorithm fuses received signal strength indicator (RSSI) values available from different beacons in the environment and the information provided by the IMU sensor. The aforesaid iSAM algorithm is capable of simultaneously generating beacon and landmark map and localize the mobile computing device in the environment without having any prior information about any beacon locations. To accommodate for noisy sensor data and achieve better convergence for the iSAM algorithm, the system uses a prior beacon location map, which contains location information of some of the BLE beacons located in the environment. These known beacon locations provide cleaner environmental information to the iSAM algorithm and hence improve the overall estimation of beacon locations, which were not available apriori.

System and method for automating beacon location map generation using sensor fusion and simultaneous localization and mapping

A computer-implemented method, the method being performed in a system comprising an electronic lock and a client device comprising a central processing unit, a localization signal receiver and a memory, the computer-implemented method comprising: receiving at least one localization signal using the localization signal receiver; measuring a strength of the received localization signal; using the central processing unit to extract a identifier from the received localization signal; determining a location of the client device based at least on the measured strength of the received localization signal and the extracted identifier; and causing the electronic lock to unlock based on the determined location.

Systems and methods for location enabled electronic lock controls

Described are systems and methods for helping a participant, who is represented in a remote space by a remote device, to understand the physical state of the remote device in the remote space. This is done through physical and virtual changes in their local space that reflect their own remote physical representation. For example, if their action in the local space causes the remote telepresence robot to rotate 90 degrees, their local display through which the control the robot might rotate 9 degrees. The described methods will help participants have a greater sense of presence in a remote space.

Systems and methods for conveying physical state of a remote device

A computer-implemented method performed in a computerized system incorporating a central processing unit, a localization signal receiver and a memory, the computer-implemented method involving: receiving at least one localization signal using the localization signal receiver; measuring a strength of the received localization signal; using the central processing unit to extract a key from the received localization signal; determining a location based at least on the measured strength of the received localization signal; and validating the determined location using the extracted key. The localization signal may be provided by one or more beacons, such as iBeacons, which may be placed at various locations within a building. The key may be randomly generated and periodically transmitted to the beacons to prevent replay attacks. A ticket issued by a near field communication (NFC) device may be used for additional location validation to prevent tunneling and collusion.

Systems and methods for mobile device location verification using beacons

A method of identifying a location of a mobile device in a building includes identifying non-overlapping regions in a building. A server collects base station signal strength measurements at a plurality of distinct points in the building, with at least one point in each region. The server trains region classifiers for each region. Each region classifier is configured to compute a probability estimate that the test point is inside the region, using inputs that are signal strength differences. The server receives signal strength measurements from the base stations, taken by a mobile device at an unknown point. The server computes differences in signal strengths between pairs of base stations, and applies the region classifiers to the signal strength differences, thereby estimating the region where the mobile device is located. The server then transmits the estimated region to a user.

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San Jose, CA, United States of America

Jacob Biehl