The present invention relates to augmented reality (AR) and more specifically, to a system for monitoring environmental conditions within a building using data from one or more AR devices and determining optimal control settings for reducing energy consumption.
The use of AR devices in the workplace is becoming more prevalent. AR devices are often designed to be worn on a user's head and display information that augments the user's own visual experience. In a workplace setting, a typical AR experience may include being presented with certain visual information about their environment to help them understand and perform their job duties more productively and efficiently.
The AR experience is created by presenting generated content (e.g., text, graphics, images, etc.) that overlay the user's field of view (FOV). This content is typically positioned so that it lends context to items (e.g., objects, people, etc.) within the user's immediate environment. AR devices have embedded sensors to detect and track user orientation, object locations, and environmental conditions such as light intensity and air temperature. Each AR device is coupled to a processor configured by software to utilize data from the sensors to generate the AR content.
Most buildings have control systems to operate lighting and climate-control systems (e.g., heating, ventilation, and cooling, or HVAC) based on user-determined set-points and measured environmental conditions. Control systems may be limited to an on/off switch for lighting systems and a thermostat for climate-control systems. More advanced controllers may have multiple sensors providing location-based temperature and/or lighting inputs. Smart controllers may include energy reduction strategies such as minimizing light from artificial sources to take advantage of natural lighting sources and adjusting the lighting and/or temperature settings when certain areas of buildings are not occupied. Operating and maintaining these systems, however, may include expenses that reduce the expected cost savings. Further, workplaces are often remodeled or rearranged and installing additional sensors to update the building control systems may be cost-prohibitive.
In the United States, workplace conditions are regulated by the Occupational Safety and Health Administration (OSHA). For example, OSHA sets minimum lighting levels (e.g., 5 foot-candles for general construction areas, 20 foot-candles for offices, etc.). OSHA also offers advice on optimizing workplace conditions such as keeping glare from overhead lighting to a minimum to reduce eyestrain, headaches, and even awkward postures on workers. Other workplace conditions (e.g., ambient air temperatures above or below standard comfort levels) affect worker comfort and productivity. In addition to regulatory compliance and worker comfort, many workplaces consider energy reduction goals when designing, setting, and adjusting workplace environmental conditions.
Minimum lighting levels, however, may be difficult to maintain as light bulbs burn out or malfunction. Consistent and comfortable temperatures within a building may be difficult to achieve in drafty or poorly insulated buildings having only one temperature zone and thermostat. Energy costs also affect workplace environmental conditions. Energy control efforts, (e.g., dimming lights in response to building occupancy or natural light levels, using more or less outside air for heating or cooling) are effective but become cost-prohibitive to implement if new controllers, equipment or sensors are needed.
Therefore, a need exists for inexpensive, yet reliable, sensors providing environmental condition data within a building that can be measured and tracked. Sensors embedded in AR devices currently used in workplace settings may be utilized to monitor environmental conditions such as lighting and temperature levels. Algorithms running on processors connected to the sensors determine optimal control settings that may be used by environmental control systems to adjust the lighting and temperature to reduce energy usage. Optimal control settings are based on, at a minimum, considerations of regulatory compliance, worker comfort, and/or energy reduction.
Accordingly, in one aspect, the present invention embraces an augmented reality (AR) system for monitoring and controlling environmental conditions within a building to reduce energy usage. The system includes a display to present AR content. The system also includes at least one sensor that detects and characterizes environmental condition data in the building. The system also includes a location detector for providing location data of the user relative to the building. Environmental condition and location data is stored in a computing device that is communicatively coupled to the display, one or more sensors, and the location detector. The computing device includes a processor that is configured by software to store data gathered by the sensor over time to develop a time- and location-based data profile for one or more monitored environmental conditions within the building. The processor is further configured to measure one or more current environmental conditions with the sensor at a known location and compare the current condition with an expected condition based on the data profile. The processor is also configured to determine an optimal control setting for at least one environmental conditions based on the comparison between the one or more current and expected environmental conditions.
In an exemplary embodiment of the AR system, the processor is further configured to generate AR content pertaining to the one or more environmental conditions within the building and transmit the AR content to the display.
In another exemplary embodiment of the AR system, the processor is further configured by the software to update the profile with the one or more current environmental conditions for the known location. The processor, in another embodiment, is configured to generate a notification message when at least one current environmental condition is outside a range of a set-point by a predetermined amount.
In another exemplary embodiment of the AR system, the at least one sensor includes a light sensor for gathering light data from light sources in the building. One of the measured environmental conditions is light intensity or frequency.
In another exemplary embodiment of the AR system, the light sensor is a charge-coupled device (CCD). In still another embodiment, the data profile includes lighting intensity and the processor is further configured to detect and characterize the light sources in the building using the light data.
In another exemplary embodiment of the AR system, the notification message includes an indication that at least one of the light sources may be burnt out or malfunctioning when the measured lighting intensity deviates from a known lighting intensity level by an amount indicative of such a condition. In another embodiment of the AR system, the generated AR content includes measured lighting intensity, lighting set-point, and an indicator of a malfunctioning or burnt-out light source.
In another embodiment, the AR system further includes one or more depth sensors to gather mapping data of physical objects in the building. The processor is further configured to detect and track the number of people in the building using the mapping data, adjust a control setting to reduce energy usage for at least one of the environmental conditions when no people are detected within one or more zones within the building.
In another exemplary embodiment of the AR system, the depth sensors includes at least one optical 3D scanner, and wherein the processor further constructs a three-dimensional (3D) model of the building using the mapping data.
In yet another embodiment of the AR system, the control setting for the intensity of artificial light sources is calculated to supplement natural light sources he lighting set-point.
In another exemplary embodiment of the AR system, a temperature sensor for gathering temperature data from the building is provided. Further, at least one measured environmental conditions is the ambient temperature of the building. In another embodiment of the AR system, the control settings for the climate-control system are calculated to maintain the ambient temperature inside the building within a range of a temperature set-point.
In another aspect, the present invention embraces a method for monitoring and controlling one or more environmental conditions within a building using an AR system. The method includes the step of providing an AR device configured to be worn by a user and operatively connected to a computing device. The AR device of the method includes one or more sensors and a display system for presenting AR content generated by the computing device. The method also includes the steps of identifying a location of the AR device relative to the building and measuring an environmental condition of the building using data gathered by the one or more sensors at the identified location. The method further includes the steps of determining the number and/or location of the workers in the building and creating and updating a time- and location-based data profile of the environmental condition. The method also includes the steps of comparing the measured environmental condition with an expected value that is based on a time- and location-based data map of the environmental condition within the building. A last step of the method is adjusting a control setting if the measured environmental condition is outside of a range of a set-point.
In another exemplary embodiment of the method for monitoring and controlling one or more environmental conditions within a building using an AR system, the generated AR content comprises indicators of the measured environmental condition and set-point. In another embodiment of the method, the monitored environmental condition is lighting intensity and/or ambient temperature.
In yet another exemplary embodiment of the method, an additional step includes displaying AR content instructing the user to move to a location different from the current location.
In another aspect, the present invention embraces an AR device with a depth sensor to gather mapping data of (i) physical objects in an environment and (ii) light sources in the environment, a light sensor to gather light data from the light sources in the environment, and a display for displaying AR content overlapping a user's perspective view of the environment. The AR device further includes a processor communicatively coupled to the depth, the light sensor, and the display. The processor is configured by software to construct a map of the environment using the mapping data and measure the lighting intensity at a known location using the light data gathered by the light sensor. The processor is further configured to determine an optimal control setting for one or more artificial light sources after comparing the measured lighting intensity to a user-selected lighting intensity set-point and generate a notification message if the measured light intensity at the known location deviates from an expected light intensity by an amount indicative of a malfunctioning or burnt-out light source.
In yet another embodiment of the AR device, the AR device includes a temperature sensor to gather temperature data from the environment. The processor is also further configured to measure the temperature at the known location using the temperature data gathered by the temperature sensor, determine an optimal control setting for a climate-control system based on the difference between a comparison of the measured temperature against a temperature set-point, and create AR content corresponding to the temperature measurement and corresponding set-point and the measured lighting intensity and corresponding set-point.
The foregoing illustrative summary, as well as other exemplary objectives and/or advantages of the invention, and the manner in which the same are accomplished, are further explained within the following detailed description and its accompanying drawings.
The present invention embraces an augmented reality (AR) system, method, and device for monitoring environmental conditions (e.g., light levels, temperature, etc.) and occupancy levels of a building and determining control settings to reduce energy usage by lighting and climate-control systems. Optimal control settings are determined after taking energy reduction, regulatory compliance, and worker comfort into consideration.
AR systems allow a user to view and (in some cases) interact with an enhanced version of the physical world. AR devices include head-mounted displays (HMDs) or hand-held displays (e.g., smartphones) with a number of embedded sensors connected to a computing device. The AR device combines a user's perspective view of the surrounding environment with virtual objects such as graphics and text messages. Data from the sensors is used to detect and track the user's perspective view and physical conditions of the surrounding environment. The computing device updates and adjust the virtual objects (i.e., AR content) in real-time.
An exemplary AR device is shown in
The AR system 20 further includes a display 32 to facilitate the user's view of the environment 16 and display AR content 18. In the illustrated AR device 10, the sensors 22, 26, 28 and display 32 are integrated into the smart glasses-type HMD. An inertial measurement sensor (e.g., gyroscope, accelerometer, magnetometer, etc.) (not shown) is provided to track the orientation (i.e., position) of the AR device 10 within the environment 16. A computing device 34 receives inputs, executes various software applications, and produces outputs related to each AR device 10.
The computing device 34 may be integrated into the AR device 10 or may be physically separate but be communicatively coupled thereto. The computing device 34 has a processor enabled by software to execute algorithms to determine energy-reducing settings for environmental control systems for the building. Exemplary processors suitable for the present invention include (but are not limited to) microprocessors, application-specific integrated circuits (ASIC), graphics processing units (GPU), digital signal processors (DSP), image processors, and multi-core processors. It is possible that the AR system 20 uses one or more of these processors types.
A camera (not shown) on the AR device 10 may also be used to facilitate tracking and mapping functions. The camera (e.g., CCD camera, CMOS camera, etc.) is typically aligned with the user's perspective view 14. Images captured by the camera may be sent to a processor running algorithms 36 such as simultaneous localization and mapping (SLAM) to track the orientation of the AR device 10 and create a map of the user's perspective view 14. SLAM algorithms may aid in the creation of maps (i.e., models) of the environment 16, including the locations of physical objects (e.g., equipment, people, etc.), light sources 24, and overall physical dimensions.
The computing device 34 is configured to receive mapping data from the depth sensors 22. In one embodiment, the processor uses the mapping data to construct or update a 3D model of the environment 16. The resulting 3D model includes the orientation of the AR device 10 relative to physical objects (e.g., walls, people, etc.) 23. Alternatively, the location detector system 30 may provide location data for measurements taken with the AR device 10. The mapping data may also be used to determine the presence, location, and movement of workers within the building.
The computing device 34 is further configured to receive light data from the light sensors 26. A light source detection algorithm 38 running on the processor uses the light data to detect and characterize the light sources 24 illuminating the environment 16. The results of this algorithm 38 may include the number, location (e.g., relative to the AR device 10 and/or the 3D model), and characteristics (e.g., color, intensity, directionality, etc.) of the detected light sources 24.
The computing device 34 may also be configured to detect and locate light sources 24 using the camera or by using data from one of a variety of possible photo sensor types (e.g., photodiodes, phototransistors, etc.). Light source detection algorithm 38 may include repeatedly measuring and recording light levels within the environment 16 at various locations using the light sensor (e.g., camera, photo sensor, etc.). The measurements may be executed at regular periodic intervals for a length of time sufficient to detect and differentiate between artificial lighting sources within the building and natural light sources emanating from outside of the building. Alternatively, information about the locations, illumination levels, and control settings for artificial light sources in the building may be provided to the AR system 20 by a smart lighting controller.
The computing device 34 is further configured by an algorithm 40 to create and maintain a time- and location-based data profile of various environmental conditions within the building. The data profile is populated with measurements of one or more environmental conditions taken at multiple times and locations throughout the building. Changes and trends in the environment 16 (e.g., light and temperature fluctuations throughout a day or between seasons, etc.) may be detected and included with the data profile. The data profile includes empirical and extrapolated data that can be utilized for various forecasting models including anticipated energy usage and associated costs for the building.
In an exemplary embodiment, the AR system 20 logically divides the building into one or more 2D or 3D zones. Properties such as the location, shape, and size, as well as the number of zones may be determined by the processor based on user-entered or automatically detected parameters. Optimal control settings for each zone are based, in part, on zone-specific characteristics such as the number and location of workspaces/equipment and workers. Other factors that may be considered include worker movements and zone designation (e.g., warehouse, shipping/receiving, administrative offices, etc.). AR devices 10 may continuously or periodically detect, measure, and transmit environmental condition data obtained from within a specific zone.
Environmental condition measurements are associated with time and location information and may be included in the time- and location-based data profile. Measurements from two or more AR devices 10 in close proximity or time may be averaged together by zone and the data profile updated accordingly. The processor develops trends based on values from the data profile and uses them to extrapolate expected environmental condition levels. A data profile may be provided for each zone or the building as a whole.
In an alternate exemplary embodiment, environmental condition measurements are obtained from AR devices 10 at or near identified, spaced-apart, and fixed locations. The locations may be distributed evenly and uniformly (e.g., a grid pattern) or distributed based on the building layout, zone profiles, and worker movements. In such an embodiment, the data profile includes actual measurements made at (or in very close proximity to) each of the fixed locations. The processor may develop a trend the values in the data profile and extrapolate expected environmental conditions in between the fixed locations using measured data from one or more surrounding locations, the time, and proximity to the data points.
Optimal, energy-reducing control settings for the building lighting system may include desired intensity levels for both individual and groups of lighting sources. Likewise, optimal control settings for the HVAC system may be zone- or building-based depending on number of workers, tasks being performed, and the time.
The AR device 10 is automatically triggered to detect and record measurements of one or more environmental conditions when a worker enters a zone or passes by a known location. Some workers wearing AR devices 10 may be directed from time-to-time to areas of the building with low foot travel, and thus typically less frequent measurements. The AR system 20 may generate AR content 18 with instructions to travel to specific identified areas of the building where environmental conditions can be detected and measured by the AR device 10. The computing device 34 may also receive and track occupancy levels and worker movements within the building using one or more of the aforementioned devices or methods.
Optimal control settings may be determined with the AR system 20 by executing algorithms 42 using various energy-reduction control strategies such as turning off or reducing power to light sources on occupancy levels. An initial control setting for each zone may be based on expected occupancy levels and adjusted based on actual or detected occupancy levels. Another control strategy includes setting the temperature control setting for each zone to a reduced-power mode based on occupancy levels therein. Also, a larger target temperature range may be utilized to reduce the amount of on/off cycling by the HVAC equipment.
The computing device 34 is configured by an algorithm 44 to communicate calculated control settings to one or more building environmental control systems, such as a lighting system 46 and a climate-control system 48. Communication may occur wirelessly between one or more components utilizing a known and secure communication protocol. A webserver (not shown) may be connected to the AR system 20 to enable off-site monitoring of environmental conditions, control settings, and energy usage associated with the building.
Initial control settings, including lighting levels and temperature set-points, may be determined using factors such as workplace-specific tasks, expected occupancy levels, worker comfort, and regulatory compliance. Initial control settings may be based on user-provided set-points for the building control systems or determined using expected values extrapolated from the time-and location-based data profile. Control settings may include a desired set-point and allowable deviation (i.e., error) from the set-point. Environmental conditions resulting from Low- and reduced-power settings may be measured to ensure the levels remain in compliance with OSHA recommendations (e.g., five candle-feet for occupied warehouses and construction areas, ten candle-feet for workspaces with machining and operating equipment, and no less than thirty candle-feet for office areas).
In one exemplary embodiment, expected and actual occupancy levels are utilized to determine optimal reduced energy-usage settings for the artificial lighting sources. In another embodiment, building light levels may be at a very low-power setting with visual effects such as augmented lighting or night-vision provided by generated AR content 18. In another exemplary embodiment, artificial light sources 24 may be controlled by the inertial sensor on a user's AR device 10. In such an embodiment, the inertial sensor detects the orientation of the AR device 10 (and the user 12 by extension) and adjusts lighting control settings accordingly. Each artificial light source 24 may be operated in a low-power mode, or turned off when no workers are detected near or adjacent to the light source 24. Individual light sources 24 determined to be within the perspective view 16 of a worker using an AR device 10 are turned on or operated at a higher intensity. As the worker 12 shifts their gaze to another area, control settings for the lighting system are adjusted accordingly. Power levels for artificial light sources 24 within the line of sight of the perspective view 16 are turned on and/or up. As the worker 12 continues to shift their gaze, light sources 24 are turned off/down as they exit from the perspective view 16.
The presence and location of workers using an AR device 10 may be determined with device-specific location data provided to the AR system 20. Workers or other people in the building without an AR device 10 are recognized and tracked through analysis of the 3D map from the embedded depth sensors 22.
The processor is configured to cross-check the locations of all workers detected in the building with the depth sensor 22 against the locations of workers identified through location data of their own AR device 10. Workers 12 counted twice or more (i.e., detected by the depth sensor of one or more other AR devices 10 and by the location data from their own AR device 10) are correctly counted as one worker to ensure an accurate count. Thus, occupancy levels and worker movements within a building can be monitored, recorded, and trended by the AR system 20.
In another embodiment, optimal control settings for the building lighting system are adjusted to account for the intensity and other characteristics of natural light sources. Daylight may be measured, tracked, and used to supplement the light generated by the artificial light sources 26 to reduce energy usage. Lighting control settings for artificial light sources 24 near exterior windows and skylights may include adjustments to the color and intensity to account for undesirable characteristics (e.g., glare, shade, color, etc.) of natural light.
In one embodiment, the initial temperature setting includes a target set-point and a deviation range including the set-point. The target set-point may be set within OSHA-recommended ranges for ambient indoor air temperature (i.e., 68°-74° F./20°-23.5° C. during the heating season and 73°-78° F./23°-26° C. during the cooling season). Climate control systems typically control the ambient air temperature and humidity within a pre-determined range (e.g., ±10%) of a set-point to minimize the on/off cycling of HVAC systems.
Optimal, energy-reducing control settings may include instructions to a ventilation system to pull warm or cool outside air into the building and vent building air out when cost savings can be realized based on the outside temperature and humidity level.
Baseline data of building lighting and temperature characteristics includes measurements taken at different times of the day and through changes in the environment. Data used to create and update the data profile may be obtained through an initialization process or by regularly obtaining data from workers over a period of time.
The software 50 may create relevant AR content 18 including the expected and actual lighting and temperature levels. The AR content 18 may then be transmitted from the computing device 10 to the display 13.
In another embodiment, the processor is configured to determine if a light intensity level in a particular area differs by more than an amount indicative of a burnt-out or malfunctioning light source. If so, appropriate notifications may be automatically generated and sent to maintenance personnel. The notification message may provide a location or identify a light source 24 believed to be burnt out or malfunctioning to assist maintenance personnel. Such a process may be one of multiple ancillary services provided by the AR system 20 automatically without worker input or knowledge.
In addition to improving energy efficiency, the AR system 20 may improve employee efficiency by instructing workers with downtime to perform one or more tasks in addition to their regular job duties. For example the algorithms 50 may generate AR content 18 directing a worker to an area of the building to enable the AR device 10 to measure environmental conditions in areas of the building that have not been inspected for a certain period of time.
At 102, the method includes receiving building-related data from one or more of the sensors. The data may include information about environmental conditions within or surrounding the building such as the lighting intensity, light color, and inside and outside temperatures. The data may include information about the number or location of people within the building along with date and time information.
At 104, the method includes establishing a time- and location-based data profile by correlating measured environmental conditions within identified zones or at specified locations against the time and date information. The measurement locations may be predetermined or selected based on the size of the building. The measurements may be taken during an initialization process or over a period of time while workings are performing their routine job tasks inside and in close proximity to the building.
At 106, the method includes determining whether the measured environmental condition is within a range of an operating set-point. The range may be calculated as a percentage of the set-point or be from a set value above and below the set-point.
At 108, the method includes adjusting a control setting if the measured environmental condition is outside the range. At 110, the method includes generating a notification message that the environmental system is malfunctioning or not operating if the measured condition is outside the range of an expected amount by a predetermined value. For example, a measured lighting level at a certain location and time that is significantly less than an expected value stored in the data profile may indicate that a light bulb is burnt out or malfunctioning.
At 112, the method may include adjusting a control setting based on the occupancy level of the building. The method 100 may also include determining a control setting based on an averaged environmental condition (e.g., natural light, outside air temperature, etc.) stored in the environmental profile.
At 114, the method includes transmitting the control settings to operate one or more of the building environmental systems in a low-power mode. As discussed, some or all of the artificial light sources may be turned off when it is determined that there are no people present in the building. Likewise, the lights may be turned back on when a worker is detected to be within or in close proximity to the building. The internal ambient temperature may also have multiple settings based on whether people are present in the building and their location.
At 116, the method updates the data profile with current environmental conditions, providing a more detailed picture of environmental conditions, both internal and external to the building. Control settings determined and provided by the AR system 20 are able to realize a significant reduction in energy consumption with minimal installation or operational costs while also ensuring that regulatory compliance and worker comfort is maintained.
To supplement the present disclosure, this application incorporates entirely by reference the following commonly assigned patents, patent application publications, and patent applications:
In the specification and/or figures, typical embodiments of the invention have been disclosed. The present invention is not limited to such exemplary embodiments. The use of the term “and/or” includes any and all combinations of one or more of the associated listed items. The figures are schematic representations and so are not necessarily drawn to scale. Unless otherwise noted, specific terms have been used in a generic and descriptive sense and not for purposes of limitation.
| Number | Name | Date | Kind |
|---|---|---|---|
| 6094625 | Ralston | Jul 2000 | A |
| 6832725 | Gardiner et al. | Dec 2004 | B2 |
| 7128266 | Marlton et al. | Oct 2006 | B2 |
| 7159783 | Walczyk et al. | Jan 2007 | B2 |
| 7413127 | Ehrhart et al. | Aug 2008 | B2 |
| 7714895 | Pretlove | May 2010 | B2 |
| 7726575 | Wang et al. | Jun 2010 | B2 |
| 8294969 | Plesko | Oct 2012 | B2 |
| 8317105 | Kotlarsky et al. | Nov 2012 | B2 |
| 8322622 | Suzhou et al. | Dec 2012 | B2 |
| 8366005 | Kotlarsky et al. | Feb 2013 | B2 |
| 8371507 | Haggerty et al. | Feb 2013 | B2 |
| 8376233 | Van Horn et al. | Feb 2013 | B2 |
| 8381979 | Franz | Feb 2013 | B2 |
| 8390909 | Plesko | Mar 2013 | B2 |
| 8408464 | Zhu et al. | Apr 2013 | B2 |
| 8408468 | Horn et al. | Apr 2013 | B2 |
| 8408469 | Good | Apr 2013 | B2 |
| 8424768 | Rueblinger et al. | Apr 2013 | B2 |
| 8448863 | Xian et al. | May 2013 | B2 |
| 8457013 | Essinger et al. | Jun 2013 | B2 |
| 8459557 | Havens et al. | Jun 2013 | B2 |
| 8469272 | Kearney | Jun 2013 | B2 |
| 8474712 | Kearney et al. | Jul 2013 | B2 |
| 8479992 | Kotlarsky et al. | Jul 2013 | B2 |
| 8490877 | Kearney | Jul 2013 | B2 |
| 8517271 | Kotlarsky et al. | Aug 2013 | B2 |
| 8523076 | Good | Sep 2013 | B2 |
| 8528818 | Ehrhart et al. | Sep 2013 | B2 |
| 8544737 | Gomez et al. | Oct 2013 | B2 |
| 8548420 | Grunow et al. | Oct 2013 | B2 |
| 8550335 | Samek et al. | Oct 2013 | B2 |
| 8550354 | Gannon et al. | Oct 2013 | B2 |
| 8550357 | Kearney | Oct 2013 | B2 |
| 8556174 | Kosecki et al. | Oct 2013 | B2 |
| 8556176 | Van Horn et al. | Oct 2013 | B2 |
| 8556177 | Hussey et al. | Oct 2013 | B2 |
| 8559767 | Barber et al. | Oct 2013 | B2 |
| 8561895 | Gomez et al. | Oct 2013 | B2 |
| 8561903 | Sauerwein | Oct 2013 | B2 |
| 8561905 | Edmonds et al. | Oct 2013 | B2 |
| 8565107 | Pease et al. | Oct 2013 | B2 |
| 8571307 | Li et al. | Oct 2013 | B2 |
| 8579200 | Samek et al. | Nov 2013 | B2 |
| 8583924 | Caballero et al. | Nov 2013 | B2 |
| 8584945 | Wang et al. | Nov 2013 | B2 |
| 8587595 | Wang | Nov 2013 | B2 |
| 8587697 | Hussey et al. | Nov 2013 | B2 |
| 8588869 | Sauerwein et al. | Nov 2013 | B2 |
| 8590789 | Nahill et al. | Nov 2013 | B2 |
| 8596539 | Havens et al. | Dec 2013 | B2 |
| 8596542 | Havens et al. | Dec 2013 | B2 |
| 8596543 | Havens et al. | Dec 2013 | B2 |
| 8599271 | Havens et al. | Dec 2013 | B2 |
| 8599957 | Peake et al. | Dec 2013 | B2 |
| 8600158 | Li et al. | Dec 2013 | B2 |
| 8600167 | Showering | Dec 2013 | B2 |
| 8602309 | Longacre et al. | Dec 2013 | B2 |
| 8608053 | Meier et al. | Dec 2013 | B2 |
| 8608071 | Liu et al. | Dec 2013 | B2 |
| 8611309 | Wang et al. | Dec 2013 | B2 |
| 8615374 | Discenzo | Dec 2013 | B1 |
| 8615487 | Gomez et al. | Dec 2013 | B2 |
| 8621123 | Caballero | Dec 2013 | B2 |
| 8622303 | Meier et al. | Jan 2014 | B2 |
| 8628013 | Ding | Jan 2014 | B2 |
| 8628015 | Wang et al. | Jan 2014 | B2 |
| 8628016 | Winegar | Jan 2014 | B2 |
| 8629926 | Wang | Jan 2014 | B2 |
| 8630491 | Longacre et al. | Jan 2014 | B2 |
| 8635309 | Berthiaume et al. | Jan 2014 | B2 |
| 8636200 | Kearney | Jan 2014 | B2 |
| 8636212 | Nahill et al. | Jan 2014 | B2 |
| 8636215 | Ding et al. | Jan 2014 | B2 |
| 8636224 | Wang | Jan 2014 | B2 |
| 8638806 | Wang et al. | Jan 2014 | B2 |
| 8640958 | Lu et al. | Feb 2014 | B2 |
| 8640960 | Wang et al. | Feb 2014 | B2 |
| 8643717 | Li et al. | Feb 2014 | B2 |
| 8646692 | Meier et al. | Feb 2014 | B2 |
| 8646694 | Wang et al. | Feb 2014 | B2 |
| 8657200 | Ren et al. | Feb 2014 | B2 |
| 8659397 | Vargo et al. | Feb 2014 | B2 |
| 8668149 | Good | Mar 2014 | B2 |
| 8678285 | Kearney | Mar 2014 | B2 |
| 8678286 | Smith et al. | Mar 2014 | B2 |
| 8682077 | Longacre | Mar 2014 | B1 |
| D702237 | Oberpriller et al. | Apr 2014 | S |
| 8687282 | Feng et al. | Apr 2014 | B2 |
| 8692927 | Pease et al. | Apr 2014 | B2 |
| 8695880 | Bremer et al. | Apr 2014 | B2 |
| 8698949 | Grunow et al. | Apr 2014 | B2 |
| 8702000 | Barber et al. | Apr 2014 | B2 |
| 8717494 | Gannon | May 2014 | B2 |
| 8720783 | Biss et al. | May 2014 | B2 |
| 8723804 | Fletcher et al. | May 2014 | B2 |
| 8723904 | Marty et al. | May 2014 | B2 |
| 8727223 | Wang | May 2014 | B2 |
| 8740082 | Wilz | Jun 2014 | B2 |
| 8740085 | Furlong et al. | Jun 2014 | B2 |
| 8746563 | Hennick et al. | Jun 2014 | B2 |
| 8750445 | Peake et al. | Jun 2014 | B2 |
| 8752766 | Xian et al. | Jun 2014 | B2 |
| 8756059 | Braho et al. | Jun 2014 | B2 |
| 8757495 | Qu et al. | Jun 2014 | B2 |
| 8760563 | Koziol et al. | Jun 2014 | B2 |
| 8736909 | Reed et al. | Jul 2014 | B2 |
| 8777108 | Coyle | Jul 2014 | B2 |
| 8777109 | Oberpriller et al. | Jul 2014 | B2 |
| 8779898 | Havens et al. | Jul 2014 | B2 |
| 8781520 | Payne et al. | Jul 2014 | B2 |
| 8783573 | Havens et al. | Jul 2014 | B2 |
| 8789757 | Barten | Jul 2014 | B2 |
| 8789758 | Hawley et al. | Jul 2014 | B2 |
| 8789759 | Xian et al. | Jul 2014 | B2 |
| 8794520 | Wang et al. | Aug 2014 | B2 |
| 8794522 | Ehrhart | Aug 2014 | B2 |
| 8794525 | Amundsen et al. | Aug 2014 | B2 |
| 8794526 | Wang et al. | Aug 2014 | B2 |
| 8798367 | Ellis | Aug 2014 | B2 |
| 8807431 | Wang et al. | Aug 2014 | B2 |
| 8807432 | Van Horn et al. | Aug 2014 | B2 |
| 8820630 | Qu et al. | Sep 2014 | B2 |
| 8822848 | Meagher | Sep 2014 | B2 |
| 8824692 | Sheerin et al. | Sep 2014 | B2 |
| 8824696 | Braho | Sep 2014 | B2 |
| 8842849 | Wahl et al. | Sep 2014 | B2 |
| 8844822 | Kotlarsky et al. | Sep 2014 | B2 |
| 8844823 | Fritz et al. | Sep 2014 | B2 |
| 8849019 | Li et al. | Sep 2014 | B2 |
| D716285 | Chaney et al. | Oct 2014 | S |
| 8851383 | Yeakley et al. | Oct 2014 | B2 |
| 8854633 | Laffargue | Oct 2014 | B2 |
| 8866963 | Grunow et al. | Oct 2014 | B2 |
| 8868421 | Braho et al. | Oct 2014 | B2 |
| 8868519 | Maloy et al. | Oct 2014 | B2 |
| 8868802 | Barten | Oct 2014 | B2 |
| 8868803 | Bremer et al. | Oct 2014 | B2 |
| 8870074 | Gannon | Oct 2014 | B1 |
| 8879639 | Sauerwein | Nov 2014 | B2 |
| 8880426 | Smith | Nov 2014 | B2 |
| 8881983 | Havens et al. | Nov 2014 | B2 |
| 8881987 | Wang | Nov 2014 | B2 |
| 8903172 | Smith | Dec 2014 | B2 |
| 8908995 | Benos et al. | Dec 2014 | B2 |
| 8910870 | Li et al. | Dec 2014 | B2 |
| 8910875 | Ren et al. | Dec 2014 | B2 |
| 8914290 | Hendrickson et al. | Dec 2014 | B2 |
| 8914788 | Pettinelli et al. | Dec 2014 | B2 |
| 8915439 | Feng et al. | Dec 2014 | B2 |
| 8915444 | Havens et al. | Dec 2014 | B2 |
| 8916789 | Woodburn | Dec 2014 | B2 |
| 8918250 | Hollifield | Dec 2014 | B2 |
| 8918564 | Caballero | Dec 2014 | B2 |
| 8925818 | Kosecki et al. | Jan 2015 | B2 |
| 8939374 | Jovanovski et al. | Jan 2015 | B2 |
| 8942480 | Ellis | Jan 2015 | B2 |
| 8944313 | Williams et al. | Feb 2015 | B2 |
| 8944327 | Meier et al. | Feb 2015 | B2 |
| 8944332 | Harding et al. | Feb 2015 | B2 |
| 8950678 | Germaine et al. | Feb 2015 | B2 |
| D723560 | Zhou et al. | Mar 2015 | S |
| 8967468 | Gomez et al. | Mar 2015 | B2 |
| 8971346 | Sevier | Mar 2015 | B2 |
| 8976030 | Cunningham | Mar 2015 | B2 |
| 8976368 | Akel et al. | Mar 2015 | B2 |
| 8978981 | Guan | Mar 2015 | B2 |
| 8978983 | Bremer et al. | Mar 2015 | B2 |
| 8978984 | Hennick et al. | Mar 2015 | B2 |
| 8985456 | Zhu et al. | Mar 2015 | B2 |
| 8985457 | Soule et al. | Mar 2015 | B2 |
| 8985459 | Kearney et al. | Mar 2015 | B2 |
| 8985461 | Gelay et al. | Mar 2015 | B2 |
| 8988578 | Showering | Mar 2015 | B2 |
| 8988590 | Gillet et al. | Mar 2015 | B2 |
| 8991704 | Hopper et al. | Mar 2015 | B2 |
| 8996194 | Davis et al. | Mar 2015 | B2 |
| 8996384 | Funyak et al. | Mar 2015 | B2 |
| 8998091 | Edmonds et al. | Apr 2015 | B2 |
| 9002641 | Showering | Apr 2015 | B2 |
| 9007368 | Laffargue et al. | Apr 2015 | B2 |
| 9010641 | Qu et al. | Apr 2015 | B2 |
| 9015513 | Murawski et al. | Apr 2015 | B2 |
| 9016576 | Brady et al. | Apr 2015 | B2 |
| D730357 | Fitch et al. | May 2015 | S |
| 9022288 | Nahill et al. | May 2015 | B2 |
| 9030964 | Essinger et al. | May 2015 | B2 |
| 9033240 | Smith et al. | May 2015 | B2 |
| 9033242 | Gillet et al. | May 2015 | B2 |
| 9036054 | Koziol et al. | May 2015 | B2 |
| 9037344 | Chamberlin | May 2015 | B2 |
| 9038911 | Xian et al. | May 2015 | B2 |
| 9038915 | Smith | May 2015 | B2 |
| D730901 | Oberpriller et al. | Jun 2015 | S |
| D730902 | Fitch et al. | Jun 2015 | S |
| D733112 | Chaney et al. | Jun 2015 | S |
| 9047098 | Barten | Jun 2015 | B2 |
| 9047359 | Caballero et al. | Jun 2015 | B2 |
| 9047420 | Caballero | Jun 2015 | B2 |
| 9047525 | Barber | Jun 2015 | B2 |
| 9047531 | Showering et al. | Jun 2015 | B2 |
| 9049640 | Wang et al. | Jun 2015 | B2 |
| 9053055 | Caballero | Jun 2015 | B2 |
| 9053378 | Hou et al. | Jun 2015 | B1 |
| 9053380 | Xian et al. | Jun 2015 | B2 |
| 9057641 | Amundsen et al. | Jun 2015 | B2 |
| 9058526 | Powilleit | Jun 2015 | B2 |
| 9064165 | Havens et al. | Jun 2015 | B2 |
| 9064167 | Xian et al. | Jun 2015 | B2 |
| 9064168 | Todeschini et al. | Jun 2015 | B2 |
| 9064254 | Todeschini et al. | Jun 2015 | B2 |
| 9066032 | Wang | Jun 2015 | B2 |
| 9070032 | Corcoran | Jun 2015 | B2 |
| D734339 | Zhou et al. | Jul 2015 | S |
| D734751 | Oberpriller et al. | Jul 2015 | S |
| 9082023 | Feng et al. | Jul 2015 | B2 |
| 9224022 | Ackley et al. | Dec 2015 | B2 |
| 9224027 | Van Horn et al. | Dec 2015 | B2 |
| D747321 | London et al. | Jan 2016 | S |
| 9230140 | Ackley | Jan 2016 | B1 |
| 9250712 | Todeschini | Feb 2016 | B1 |
| 9258033 | Showering | Feb 2016 | B2 |
| 9262633 | Todeschini et al. | Feb 2016 | B1 |
| 9310609 | Rueblinger et al. | Apr 2016 | B2 |
| D757009 | Oberpriller et al. | May 2016 | S |
| 9342724 | McCloskey | May 2016 | B2 |
| 9375945 | Bowles | Jun 2016 | B1 |
| D760719 | Zhou et al. | Jul 2016 | S |
| 9390596 | Todeschini | Jul 2016 | B1 |
| D762604 | Fitch et al. | Aug 2016 | S |
| D762647 | Fitch et al. | Aug 2016 | S |
| 9412242 | Van Horn et al. | Aug 2016 | B2 |
| D766244 | Zhou et al. | Sep 2016 | S |
| 9443123 | Hejl | Sep 2016 | B2 |
| 9443222 | Singel et al. | Sep 2016 | B2 |
| 9478113 | Xie et al. | Oct 2016 | B2 |
| 20050034023 | Maturana | Feb 2005 | A1 |
| 20070063048 | Havens et al. | Mar 2007 | A1 |
| 20090134221 | Zhu et al. | May 2009 | A1 |
| 20100177076 | Essinger et al. | Jul 2010 | A1 |
| 20100177080 | Essinger et al. | Jul 2010 | A1 |
| 20100177707 | Essinger et al. | Jul 2010 | A1 |
| 20100177749 | Essinger et al. | Jul 2010 | A1 |
| 20110169999 | Grunow et al. | Jul 2011 | A1 |
| 20110202554 | Powilleit et al. | Aug 2011 | A1 |
| 20120111946 | Golant | May 2012 | A1 |
| 20120168512 | Kotlarsky et al. | Jul 2012 | A1 |
| 20120193423 | Samek | Aug 2012 | A1 |
| 20120203647 | Smith | Aug 2012 | A1 |
| 20120223141 | Good et al. | Sep 2012 | A1 |
| 20130043312 | Van Horn | Feb 2013 | A1 |
| 20130075168 | Amundsen et al. | Mar 2013 | A1 |
| 20130175341 | Kearney et al. | Jul 2013 | A1 |
| 20130175343 | Good | Jul 2013 | A1 |
| 20130257744 | Daghigh et al. | Oct 2013 | A1 |
| 20130257759 | Daghigh | Oct 2013 | A1 |
| 20130270346 | Xian et al. | Oct 2013 | A1 |
| 20130287258 | Kearney | Oct 2013 | A1 |
| 20130292475 | Kotlarsky et al. | Nov 2013 | A1 |
| 20130292477 | Hennick et al. | Nov 2013 | A1 |
| 20130293539 | Hunt et al. | Nov 2013 | A1 |
| 20130293540 | Laffargue et al. | Nov 2013 | A1 |
| 20130306728 | Thuries et al. | Nov 2013 | A1 |
| 20130306731 | Pedrao | Nov 2013 | A1 |
| 20130307964 | Bremer et al. | Nov 2013 | A1 |
| 20130308625 | Corcoran | Nov 2013 | A1 |
| 20130313324 | Koziol et al. | Nov 2013 | A1 |
| 20130313325 | Wilz et al. | Nov 2013 | A1 |
| 20130342717 | Havens et al. | Dec 2013 | A1 |
| 20140001267 | Giordano et al. | Jan 2014 | A1 |
| 20140002828 | Laffargue et al. | Jan 2014 | A1 |
| 20140008439 | Wang | Jan 2014 | A1 |
| 20140025584 | Liu et al. | Jan 2014 | A1 |
| 20140034734 | Sauerwein | Feb 2014 | A1 |
| 20140036848 | Pease et al. | Feb 2014 | A1 |
| 20140039693 | Havens et al. | Feb 2014 | A1 |
| 20140042814 | Kather et al. | Feb 2014 | A1 |
| 20140049120 | Kohtz et al. | Feb 2014 | A1 |
| 20140049635 | Laffargue et al. | Feb 2014 | A1 |
| 20140061306 | Wu et al. | Mar 2014 | A1 |
| 20140063289 | Hussey et al. | Mar 2014 | A1 |
| 20140066136 | Sauerwein et al. | Mar 2014 | A1 |
| 20140067692 | Ye et al. | Mar 2014 | A1 |
| 20140070005 | Nahill et al. | Mar 2014 | A1 |
| 20140071840 | Venancio | Mar 2014 | A1 |
| 20140074746 | Wang | Mar 2014 | A1 |
| 20140076974 | Havens et al. | Mar 2014 | A1 |
| 20140078341 | Havens et al. | Mar 2014 | A1 |
| 20140078342 | Li et al. | Mar 2014 | A1 |
| 20140078345 | Showering | Mar 2014 | A1 |
| 20140098792 | Wang et al. | Apr 2014 | A1 |
| 20140100774 | Showering | Apr 2014 | A1 |
| 20140100813 | Showering | Apr 2014 | A1 |
| 20140103115 | Meier et al. | Apr 2014 | A1 |
| 20140104413 | McCloskey et al. | Apr 2014 | A1 |
| 20140104414 | McCloskey et al. | Apr 2014 | A1 |
| 20140104416 | Li et al. | Apr 2014 | A1 |
| 20140104451 | Todeschini et al. | Apr 2014 | A1 |
| 20140106594 | Skvoretz | Apr 2014 | A1 |
| 20140106725 | Sauerwein | Apr 2014 | A1 |
| 20140108010 | Maltseff et al. | Apr 2014 | A1 |
| 20140108402 | Gomez et al. | Apr 2014 | A1 |
| 20140108682 | Caballero | Apr 2014 | A1 |
| 20140110485 | Toa et al. | Apr 2014 | A1 |
| 20140114530 | Fitch et al. | Apr 2014 | A1 |
| 20140121438 | Kearney | May 2014 | A1 |
| 20140121445 | Ding et al. | May 2014 | A1 |
| 20140124577 | Wang et al. | May 2014 | A1 |
| 20140124579 | Ding | May 2014 | A1 |
| 20140125842 | Winegar | May 2014 | A1 |
| 20140125853 | Wang | May 2014 | A1 |
| 20140125999 | Longacre et al. | May 2014 | A1 |
| 20140129378 | Richardson | May 2014 | A1 |
| 20140131441 | Nahill et al. | May 2014 | A1 |
| 20140131443 | Smith | May 2014 | A1 |
| 20140131444 | Wang | May 2014 | A1 |
| 20140131448 | Xian et al. | May 2014 | A1 |
| 20140133379 | Wang et al. | May 2014 | A1 |
| 20140136208 | Maltseff et al. | May 2014 | A1 |
| 20140140585 | Wang | May 2014 | A1 |
| 20140151453 | Meier et al. | Jun 2014 | A1 |
| 20140152882 | Samek et al. | Jun 2014 | A1 |
| 20140158770 | Sevier et al. | Jun 2014 | A1 |
| 20140159869 | Zumsteg et al. | Jun 2014 | A1 |
| 20140166755 | Liu et al. | Jun 2014 | A1 |
| 20140166757 | Smith | Jun 2014 | A1 |
| 20140166759 | Liu et al. | Jun 2014 | A1 |
| 20140168787 | Wang et al. | Jun 2014 | A1 |
| 20140175165 | Havens et al. | Jun 2014 | A1 |
| 20140175172 | Jovanovski et al. | Jun 2014 | A1 |
| 20140191644 | Chaney | Jul 2014 | A1 |
| 20140191913 | Ge et al. | Jul 2014 | A1 |
| 20140197238 | Lui et al. | Jul 2014 | A1 |
| 20140197239 | Havens et al. | Jul 2014 | A1 |
| 20140197304 | Feng et al. | Jul 2014 | A1 |
| 20140203087 | Smith et al. | Jul 2014 | A1 |
| 20140204268 | Grunow et al. | Jul 2014 | A1 |
| 20140214631 | Hansen | Jul 2014 | A1 |
| 20140217166 | Berthiaume et al. | Aug 2014 | A1 |
| 20140217180 | Liu | Aug 2014 | A1 |
| 20140231500 | Ehrhart et al. | Aug 2014 | A1 |
| 20140232930 | Anderson | Aug 2014 | A1 |
| 20140247315 | Marty et al. | Sep 2014 | A1 |
| 20140263493 | Amurgis et al. | Sep 2014 | A1 |
| 20140263645 | Smith et al. | Sep 2014 | A1 |
| 20140270196 | Braho et al. | Sep 2014 | A1 |
| 20140270229 | Braho | Sep 2014 | A1 |
| 20140278387 | DiGregorio | Sep 2014 | A1 |
| 20140282210 | Bianconi | Sep 2014 | A1 |
| 20140284384 | Lu et al. | Sep 2014 | A1 |
| 20140288714 | Poivet | Sep 2014 | A1 |
| 20140288933 | Braho et al. | Sep 2014 | A1 |
| 20140297058 | Barker et al. | Oct 2014 | A1 |
| 20140299665 | Barber et al. | Oct 2014 | A1 |
| 20140312121 | Lu et al. | Oct 2014 | A1 |
| 20140319220 | Coyle | Oct 2014 | A1 |
| 20140319221 | Oberpriller et al. | Oct 2014 | A1 |
| 20140326787 | Barten | Nov 2014 | A1 |
| 20140332590 | Wang et al. | Nov 2014 | A1 |
| 20140344943 | Todeschini et al. | Nov 2014 | A1 |
| 20140346233 | Liu et al. | Nov 2014 | A1 |
| 20140351317 | Smith et al. | Nov 2014 | A1 |
| 20140353373 | Van Horn et al. | Dec 2014 | A1 |
| 20140361073 | Qu et al. | Dec 2014 | A1 |
| 20140361082 | Xian et al. | Dec 2014 | A1 |
| 20140362184 | Jovanovski et al. | Dec 2014 | A1 |
| 20140363015 | Braho | Dec 2014 | A1 |
| 20140369511 | Sheerin et al. | Dec 2014 | A1 |
| 20140374483 | Lu | Dec 2014 | A1 |
| 20140374485 | Xian et al. | Dec 2014 | A1 |
| 20150001301 | Ouyang | Jan 2015 | A1 |
| 20150001304 | Todeschini | Jan 2015 | A1 |
| 20150003673 | Fletcher | Jan 2015 | A1 |
| 20150009338 | Laffargue et al. | Jan 2015 | A1 |
| 20150009610 | London et al. | Jan 2015 | A1 |
| 20150014416 | Kotlarsky et al. | Jan 2015 | A1 |
| 20150021397 | Rueblinger et al. | Jan 2015 | A1 |
| 20150028102 | Ren et al. | Jan 2015 | A1 |
| 20150028103 | Jiang | Jan 2015 | A1 |
| 20150028104 | Ma et al. | Jan 2015 | A1 |
| 20150029002 | Yeakley et al. | Jan 2015 | A1 |
| 20150032709 | Maloy et al. | Jan 2015 | A1 |
| 20150039309 | Braho et al. | Feb 2015 | A1 |
| 20150040378 | Saber et al. | Feb 2015 | A1 |
| 20150048168 | Fritz et al. | Feb 2015 | A1 |
| 20150049347 | Laffargue et al. | Feb 2015 | A1 |
| 20150051992 | Smith | Feb 2015 | A1 |
| 20150053766 | Havens et al. | Feb 2015 | A1 |
| 20150053768 | Wang et al. | Feb 2015 | A1 |
| 20150053769 | Thuries et al. | Feb 2015 | A1 |
| 20150062366 | Liu et al. | Mar 2015 | A1 |
| 20150063215 | Wang | Mar 2015 | A1 |
| 20150063676 | Lloyd et al. | Mar 2015 | A1 |
| 20150069130 | Gannon | Mar 2015 | A1 |
| 20150071818 | Todeschini | Mar 2015 | A1 |
| 20150083800 | Li et al. | Mar 2015 | A1 |
| 20150086114 | Todeschini | Mar 2015 | A1 |
| 20150088522 | Hendrickson et al. | Mar 2015 | A1 |
| 20150096872 | Woodburn | Apr 2015 | A1 |
| 20150099557 | Pettinelli et al. | Apr 2015 | A1 |
| 20150100196 | Hollifield | Apr 2015 | A1 |
| 20150102109 | Huck | Apr 2015 | A1 |
| 20150115035 | Meier et al. | Apr 2015 | A1 |
| 20150127791 | Kosecki et al. | May 2015 | A1 |
| 20150128116 | Chen et al. | May 2015 | A1 |
| 20150129659 | Feng et al. | May 2015 | A1 |
| 20150133047 | Smith et al. | May 2015 | A1 |
| 20150134470 | Hejl et al. | May 2015 | A1 |
| 20150136851 | Harding et al. | May 2015 | A1 |
| 20150136854 | Lu et al. | May 2015 | A1 |
| 20150142492 | Kumar | May 2015 | A1 |
| 20150144692 | Hejl | May 2015 | A1 |
| 20150144698 | Teng et al. | May 2015 | A1 |
| 20150144701 | Xian et al. | May 2015 | A1 |
| 20150149946 | Benos et al. | May 2015 | A1 |
| 20150161429 | Xian | Jun 2015 | A1 |
| 20150169925 | Chang et al. | Jun 2015 | A1 |
| 20150169929 | Williams et al. | Jun 2015 | A1 |
| 20150186703 | Chen et al. | Jul 2015 | A1 |
| 20150193644 | Kearney et al. | Jul 2015 | A1 |
| 20150193645 | Colavito et al. | Jul 2015 | A1 |
| 20150199957 | Funyak et al. | Jul 2015 | A1 |
| 20150204671 | Showering | Jul 2015 | A1 |
| 20150210199 | Payne | Jul 2015 | A1 |
| 20150220753 | Zhu et al. | Aug 2015 | A1 |
| 20150254485 | Feng et al. | Sep 2015 | A1 |
| 20150327012 | Bian et al. | Nov 2015 | A1 |
| 20160014251 | Hejl | Jan 2016 | A1 |
| 20160040982 | Li et al. | Feb 2016 | A1 |
| 20160042241 | Todeschini | Feb 2016 | A1 |
| 20160057230 | Todeschini et al. | Feb 2016 | A1 |
| 20160109219 | Ackley et al. | Apr 2016 | A1 |
| 20160109220 | Laffargue et al. | Apr 2016 | A1 |
| 20160109224 | Thuries et al. | Apr 2016 | A1 |
| 20160112631 | Ackley et al. | Apr 2016 | A1 |
| 20160112643 | Laffargue et al. | Apr 2016 | A1 |
| 20160124516 | Schoon et al. | May 2016 | A1 |
| 20160125217 | Todeschini | May 2016 | A1 |
| 20160125342 | Miller et al. | May 2016 | A1 |
| 20160125873 | Braho et al. | May 2016 | A1 |
| 20160133253 | Braho et al. | May 2016 | A1 |
| 20160171720 | Todeschini | Jun 2016 | A1 |
| 20160178479 | Goldsmith | Jun 2016 | A1 |
| 20160180678 | Ackley et al. | Jun 2016 | A1 |
| 20160189087 | Morton et al. | Jun 2016 | A1 |
| 20160227912 | Oberpriller et al. | Aug 2016 | A1 |
| 20160232891 | Pecorari | Aug 2016 | A1 |
| 20160292477 | Bidwell | Oct 2016 | A1 |
| 20160294779 | Yeakley et al. | Oct 2016 | A1 |
| 20160306769 | Kohtz et al. | Oct 2016 | A1 |
| 20160314276 | Sewell et al. | Oct 2016 | A1 |
| 20160314294 | Kubler et al. | Oct 2016 | A1 |
| Number | Date | Country |
|---|---|---|
| 2013163789 | Nov 2013 | WO |
| 2013173985 | Nov 2013 | WO |
| 2014019130 | Feb 2014 | WO |
| 2014110495 | Jul 2014 | WO |
| Entry |
|---|
| Wang, Xiangyu, et al. “Integrating Augmented Reality with Building Information Modeling: Onsite construction process controlling for liquefied natural gas industry.” Automation in Construction 40 (2014): pp. 96-105. |
| Wang, Xiangyu, et al. “Augmented Reality in built environment: Classification and implications for future research.” Automation in Construction 32 (2013): pp. 1-13. |
| Wang, Xiangyu, et al. “A conceptual framework for integrating building information modeling with augmented reality.” Automation in Construction 34 (2013): pp. 37-44. |
| U.S. Appl. No. 13/367,978, filed Feb. 7, 2012, (Feng et al.); now abandoned. |
| U.S. Appl. No. 14/277,337 for Multipurpose Optical Reader, filed May 14, 2014 (Jovanovski et al.); 59 pages; now abandoned. |
| U.S. Appl. No. 14/446,391 for Multifunction Point of Sale Apparatus With Optical Signature Capture filed Jul. 30, 2014 (Good et al.); 37 pages; now abandoned. |
| U.S. Appl. No. 29/516,892 for Table Computer filed Feb. 6, 2015 (Bidwell et al.); 13 pages. |
| U.S. Appl. No. 29/523,098 for Handle for a Tablet Computer filed Apr. 7, 2015 (Bidwell et al.); 17 pages. |
| U.S. Appl. No. 29/528,890 for Mobile Computer Housing filed Jun. 2, 2015 (Fitch et al.); 61 pages. |
| U.S. Appl. No. 29/526,918 for Charging Base filed May 14, 2015 (Fitch et al.); 10 pages. |
| U.S. Appl. No. 14/715,916 for Evaluating Image Values filed May 19, 2015 (Ackley); 60 pages. |
| U.S. Appl. No. 29/525,068 for Tablet Computer With Removable Scanning Device filed Apr. 27, 2015 (Schulte et al.); 19 pages. |
| U.S. Appl. No. 29/468,118 for an Electronic Device Case, filed Sep. 26, 2013 (Oberpriller et al.); 44 pages. |
| U.S. Appl. No. 29/530,600 for Cyclone filed Jun. 18, 2015 (Vargo et al); 16 pages. |
| U.S. Appl. No. 14/707,123 for Application Independent DEX/UCS Interface filed May 8, 2015 (Pape); 47 pages. |
| U.S. Appl. No. 14/283,282 for Terminal Having Illumination and Focus Control filed May 21, 2014 (Liu et al.); 31 pages; now abandoned. |
| U.S. Appl. No. 14/705,407 for Method and System to Protect Software-Based Network-Connected Devices From Advanced Persistent Threat filed May 6, 2015 (Hussey et al.); 42 pages. |
| U.S. Appl. No. 14/704,050 for Intermediate Linear Positioning filed May 5, 2015 (Charpentier et al.); 60 pages. |
| U.S. Appl. No. 14/705,012 for Hands-Free Human Machine Interface Responsive to a Driver of a Vehicle filed May 6, (Fitch et al.); 44 pages. |
| U.S. Appl. No. 14/715,672 for Augumented Reality Enabled Hazard Display filed May 19, 2015 (Venkatesha et al.); 35 pages. |
| U.S. Appl. No. 14/735,717 for Indicia-Reading Systems Having an Interface With a User's Nervous System filed Jun. 10, 2015 (Todeschini); 39 pages. |
| U.S. Appl. No. 14/702,110 for System and Method for Regulating Barcode Data Injection Into a Running Application on a Smart Device filed May 1, 2015 (Todeschini et al.); 38 pages. |
| U.S. Appl. No. 14/747,197 for Optical Pattern Projector filed Jun. 23, 2015 (Thuries et al.); 33 pages. |
| U.S. Appl. No. 14/702,979 for Tracking Battery Conditions filed May 4, 2015 (Young et al.); 70 pages. |
| U.S. Appl. No. 29/529,441 for Indicia Reading Device filed Jun. 8, 2015 (Zhou et al.); 14 pages. |
| U.S. Appl. No. 14/747,490 for Dual-Projector Three-Dimensional Scanner filed Jun. 23, 2015 (Jovanovski et al; 40 pages. |
| U.S. Appl. No. 14/740,320 for Tactile Switch for a Mobile Electronic Device filed Jun. 16, 2015 (Bamdringa); 38 pages. |
| U.S. Appl. No. 14/740,373 for Calibrating a Volumn Dimensioner filed Jun. 16, 2015 (Ackley et al.);63 pages. |
| Number | Date | Country | |
|---|---|---|---|
| 20170108838 A1 | Apr 2017 | US |