Unfused pose-based drift correction of a fused pose of a totem in a user interaction system

Information

  • Patent Grant
  • 11960661
  • Patent Number
    11,960,661
  • Date Filed
    Tuesday, February 7, 2023
    a year ago
  • Date Issued
    Tuesday, April 16, 2024
    26 days ago
  • Inventors
  • Original Assignees
  • Examiners
    • Michaud; Robert J
    Agents
    • De Klerk; Stephen M.
Abstract
The invention relates generally to a user interaction system having a head unit for a user to wear and a totem that the user holds in their hand and determines the location of a virtual object that is seen by the user. A fusion routine generates a fused location of the totem in a world frame based on a combination of an EM wave and a totem IMU data. The fused pose may drift over time due to the sensor's model mismatch. An unfused pose determination modeler routinely establishes an unfused pose of the totem relative to the world frame. A drift is declared when a difference between the fused pose and the unfused pose is more than a predetermined maximum distance.
Description
BACKGROUND OF THE INVENTION
1). Field of the Invention

This invention relates to a user interaction system having a totem that defines a six degree of freedom (“6dof”) pose, or pose, of a virtual object that is perceived by a user.


2). Discussion of Related Art

Modern computing and display technology has facilitated the development of user interaction systems that include “augmented reality” viewing devices. Such a viewing device usually has a head unit with a head unit body that is mountable to a head of a user and frequently includes two waveguides, one in front of each eye of the user. The waveguides are transparent so that ambient light from real-world objects can transmit through the waveguides and the user can see the real-world objects. Each waveguide also serves to transmit projected light from a projector to a respective eye of the user. The projected light forms an image on the retina of the eye. The retina of the eye thus receives the ambient light and the projected light. The user simultaneously sees real-world objects and one or more virtual objects that are created by the projected light.


Such a user interaction system often includes a totem. The user may, for example, hold the totem in their right hand and move the totem with six degrees of freedom in three-dimensional space. A virtual object may be perceived by the user to be attached to the totem and move with the totem in three-dimensional space, or the virtual object may be the perception of a light beam that hits the wall or another object that the user moves across the wall.


It is important for the virtual object to remain in its realistic pose relative to the totem. For example, if the totem represents the handle of a racket and the virtual object represents a head of the racket, the head of the racket has to remain “attached” to the handle of the racket over time.


SUMMARY OF THE INVENTION

The invention provides a user interaction system including a totem having a totem body, an electromagnetic (EM) transmitter on the totem body and a totem inertial measurement unit (IMU) located on the totem, to generate a totem IMU signal due to movement of the totem; a head unit having a head unit body and an EM receiver on the head unit body to receive an EM wave transmitted by the EM transmitter, the EM wave being indicative of a location of the totem; a processor; a storage device connected to the processor and a set of instructions on the storage device and executable by the processor. The set of instructions includes a world frame, a fusion routine connected to the EM receiver and the totem IMU to generate a fused pose of the totem in the world frame based on a combination of the EM wave, the head unit pose, and the totem IMU data, an unfused pose determination modeler that determines a pose of the totem relative to the head unit and a pose of the head unit relative to the world frame to establish an unfused pose of the totem relative to the world frame, a comparator connected to the fused pose determination modeler and the unfused pose determination modeler to compare the fused pose with the unfused pose, a drift declarer connected to the comparator to declare a drift only if the fused pose is more than a predetermined distance from the unfused pose, a location correction routine connected to the drift declarer to reset a pose of the totem IMU to match the unfused location only if the drift is declared, a data source to carry image data and a display system connected to the data source to display a virtual object using the image data to a user, a location of the virtual object being based on the fused location of the totem.


The invention also provides a user interaction system including transmitting an electromagnetic (EM) wave with an EM transmitter on a totem body, generating a totem inertial measurement unit (IMU) signal with a totem IMU on the totem body due to movement of the totem, locating a head unit body on a head of a user, receiving the EM wave transmitted by the EM transmitter by an EM receiver on the head unit body, the EM wave being indicative of a pose of the totem, storing a world frame, executing, with a processor, a fusion routine to generate a fused pose of the totem in the world frame based on a combination of the EM wave, head unit pose, and the totem IMU data, executing, with the processor, an unfused pose determination modeler that determines a pose of the totem relative to the head unit and a location of the head unit relative to the world frame to establish an unfused pose of the totem relative to the world frame, executing, with the processor, a comparator to compare the fused pose with the unfused pose, executing, with the processor, a drift declarer to declare a drift only if the fused pose is more than a predetermined pose from the unfused pose, executing, with the processor, a pose correction routine to reset pose of the totem IMU to match the unfused pose only if the drift is declared, receiving image data from a data source; and displaying, with a display system connected to the data source, a virtual object using the image data to a user, a location of the virtual object being based on the fused location of the totem.





BRIEF DESCRIPTION OF THE DRAWINGS

The invention is further described by way of example with reference to the accompanying drawings, wherein:



FIG. 1 is a perspective view illustrating a user interaction system, according to an embodiment of the invention;



FIG. 2 is a block diagram illustrating components of the user interaction system as it relates to a head unit and vision algorithms for the head unit;



FIG. 3 is block diagram of the user interaction system as it relates to a totem and vision algorithms for the totem;



FIG. 4 is a front view illustrating how the user sees and perceives real and virtual objects;



FIG. 5 is a view similar to FIG. 4 after a virtual object has drifted within the view of the user;



FIG. 6 is a perspective view illustrating drift of a fused location over time;



FIG. 7 is a graph illustrating how drift can be corrected using a distance calculation;



FIG. 8 is a graph illustrating how drift is corrected by detecting a difference between the fused location and an unfused location;



FIG. 9 is a perspective view illustrating how drift is corrected; and



FIG. 10 is a block diagram of a machine in the form of a computer that can find application in the present invention system, in accordance with one embodiment of the invention.





DETAILED DESCRIPTION OF THE INVENTION


FIG. 1 of the accompanying drawings illustrates a user 10, a user interaction system 12, according to an embodiment of the invention, a real-world object 14 in the form of a table, and a virtual object 16, which is not visible from the perspective of the figure but is visible to the user 10.


The user interaction system 12 includes a head unit 18, a belt pack 20, a network 22 and a server 24.


The head unit 18 includes a head unit body 26 and a display system 28. The head unit body 26 has a shape that fits over a head of the user 10. The display system 28 is secured to the head unit body 26.


The belt pack 20 has a processor and a storage device connected to the processor. Vision algorithms are stored on the storage device and are executable by the processor. The belt pack 20 is communicatively connected to the display system 28 with a cable connection 30. The belt pack 20 further includes a network interface device that permits the belt pack 20 to connect wirelessly over a link 32 with the network 22. The server 24 is connected to the network 22.


In use, the user 10 secures the head unit body 26 to their head. The display system 28 includes an optical waveguide (not shown) that is transparent so that the user 10 can see the real-world object 14 through the waveguide.


The belt pack 20 may download image data from the server 24 over the network 22 and the link 32. The belt pack 20 provides the image data through the cable connection 30 to the display system 28. The display system 28 has one or more projectors that create light based on the image data. The light propagates through the one or more optical waveguides to eyes of the user 10. Each waveguide creates light at a particular focal length on a retina of a respective eye so that the eye sees the virtual object 16 at some distance behind the display system 28. The eye thus sees the virtual object 16 in three-dimensional space. Additionally, slightly different images are created for each eye so that a brain of the user 10 perceives the virtual object 16 in three-dimensional space. The user 10 thus sees the real-world object 14 augmented with the virtual object 16 in three-dimensional space.


The user interaction system 12 further includes a totem 34. In use, the user 10 holds the totem 34 in one of their hands. The virtual object 16 is positioned in three-dimensional space based on the positioning of the totem 34. By way of example, the totem 34 may be a handle of a racket and the virtual object 16 may include the head of the racket. The user 10 can move the totem 34 in six degrees of freedom in three-dimensional space. The totem 34 thus moves in three-dimensional space relative to the real-world object 14 and the head unit body 26. Various components within the head unit 18 and the belt pack 20 track movement of the totem 34 and move the virtual object 16 together with the totem 34. The head of the racket thus remains attached to the handle in the view of the user 10.



FIG. 2 illustrates the display system 28 in more detail together with vision algorithms 38. The vision algorithms 38 primarily reside within the belt pack 20 in FIG. 1. In other embodiments, the vision algorithms 38 may reside entirely within a head unit or may be split between a head unit and a belt pack. FIG. 2 further includes a data source 40. In the present example, the data source 40 includes image data that is stored on a storage device of the belt pack 20. The image data may, for example, be three-dimensional image data that can be used to render the virtual object 16. In alternate embodiments, the image data may be time sequenced image data that allows for the creation of a video that moves in two- or three-dimensions, and may have as its purpose attachment to a totem, be located on a real-world object, or be in a fixed position in front of a user when the user moves their head.


The vision algorithms 38 include a render engine 42, a stereoscopic analyzer 44, a display adjustment algorithm 46 and a simultaneous localization and mapping (SLAM) system 48.


The render engine 42 is connected to the data source 40 and the display adjustment algorithm 46. The render engine 42 is capable of receiving inputs from various systems, in the present example the display adjustment algorithm 46, and positions the image data within a frame that is to be viewed by the user 10 based on the display adjustment algorithm 46. The display adjustment algorithm 46 is connected to the SLAM system 48. The SLAM system 48 is capable of receiving image data, analyzing the image data for purposes of determining objects within images of the image data, and recording the locations of the objects within the image data.


The stereoscopic analyzer 44 is connected to the render engine 42. The stereoscopic analyzer 44 is capable of determining left and right image data sets from a data stream that is provided by the render engine 42.


The display system 28 includes left and right projectors 48A and 48B, left and right waveguides 50A and 50B, and detection devices 52. The left and right projectors 48A and 48B are connected to power supplies. Each projector 48A or 48B has a respective input for image data to be provided to the respective projector 48A or 48B. The respective projector 48A or 48B, when powered, generates light in a two-dimensional pattern and emanates the light therefrom. The left and right waveguides 50A and 50B are positioned to receive the light from the left and right projectors 48A and 48B, respectively. The left and right waveguides 50A and 50B are transparent waveguides.


The detection devices 52 include a head unit inertial motion unit (IMU) 60 and one or more head unit cameras 62. The head unit IMU 60 includes one or more gyroscopes and one or more accelerometers. The gyroscopes and accelerometers are typically formed in a semiconductor chip and are capable of detecting movement of the head unit IMU 60 and the head unit body 26, including movement along three orthogonal axes and rotation about three orthogonal axes.


The head unit cameras 62 continually capture images from an environment around the head unit body 26. The images can be compared to one another to detect movement of the head unit body 26 and the head of the user 10.


The SLAM system 48 is connected to the head unit cameras 62. The display adjustment algorithm 46 is connected to the head unit IMU 60. One of ordinary skill in the art will appreciate that the connections between the detection devices 52 and the vision algorithms 38 are accomplished through a combination of hardware, firmware and software. The components of the vision algorithms 38 are linked to one another through subroutines or calls.


In use, the user 10 mounts the head unit body 26 to their head. Components of the head unit body 26 may, for example, include a strap (not shown) that wraps around the back of a head of the user 10. The left and right waveguides 50A and 50B are then located in front of left and right eyes 120A and 120B of the user 10.


The render engine 42 receives image data from the data source 40. The render engine 42 enters the image data into the stereoscopic analyzer 44. The image data is three-dimensional image data of the virtual object 16 in FIG. 1. The stereoscopic analyzer 44 analyzes the image data to determine left and right image data sets based on the image data. The left and right image data sets are data sets that represent two-dimensional images that differ slightly from one another for purposes of giving the user 10 a perception of a three-dimensional rendering. In the present embodiment, the image data is a static data set that does not change over time.


The stereoscopic analyzer 44 enters the left and right image data sets into the left and right projectors 48A and 48B. The left and right projectors 48A and 48B then create left and right light patterns. The components of the display system 28 are shown in plan view, although it should be understood that the left and right patters are two-dimensional patterns when shown in front elevation view. Each light pattern includes a plurality of pixels. For purposes of illustration, light rays 124A and 126A from two of the pixels are shown leaving the left projector 48A and entering the left waveguide 50A. The light rays 124A and 126A reflect from sides of the left waveguide 50A. It is shown that the light rays 124A and 126A propagate through internal reflection from left to right within the left waveguide 50A, although it should be understood that the light rays 124A and 126A also propagate in a direction into the paper using refractory and reflective systems.


The light rays 124A and 126A exit the left light waveguide 50A through a pupil 128A and then enter a left eye 120A through a pupil 130A of the left eye 120A. The light rays 124A and 126A then fall on a retina 132A of the left eye 120A. In this manner, the left light pattern falls on the retina 132A of the left eye 120A. The user 10 is given the perception that the pixels that are formed on the retina 132A are pixels 134A and 136A that the user 10 perceives to be at some distance on a side of the left waveguide 50A opposing the left eye 120A. Depth perception is created by manipulating the focal length of the light.


In a similar manner, the stereoscopic analyzer 44 enters the right image data set into the right projector 48B. The right projector 48B transmits the right light pattern, which is represented by pixels in the form of light rays 124B and 126B. The light rays 124B and 126B reflect within the right waveguide 50B and exit through a pupil 128B. The light rays 124B and 126B then enter through a pupil 130B of the right eye 120B and fall on a retina 132B of a right eye 120B. The pixels of the light rays 124B and 126B are perceived as pixels 134B and 136B behind the right waveguide 50B.


The patterns that are created on the retinas 132A and 132B are individually perceived as left and right images. The left and right images differ slightly from one another due to the functioning of the stereoscopic analyzer 44. The left and right images are perceived in a mind of the user 10 as a three-dimensional rendering.


As mentioned, the left and right waveguides 50A and 50B are transparent. Light from a real-life object on a side of the left and right waveguides 50A and 50B opposing the eyes 120A and 120B can project through the left and right waveguides 50A and 50B and fall on the retinas 132A and 132B. In particular, light from the real-world object 14 in FIG. 1 falls on the retinas 132A and 132B so that the user 10 can see the real-world object 14. Additionally, the user 10 can see the totem 34 and augmented reality is created wherein the real-world object 14 and the totem 34 are augmented with a three-dimensional rendering of the virtual object 16 that is perceived by the user 10 due to the left and right images that are, in combination, perceived by the user 10.


The head unit IMU 60 detects every movement of the head of the user 10. Should the user 10, for example, move their head counterclockwise and simultaneously move their body together with their head towards the right, such movement will be detected by the gyroscopes and accelerometers in the head unit IMU 60. The head unit IMU 60 provides the measurements from the gyroscopes and the accelerometers to the display adjustment algorithm 46. The display adjustment algorithm 46 calculates a placement value and provides the placement value to the render engine 42. The render engine 42 modifies the image data received from the data source 40 to compensate for the movement of the head of the user 10. The render engine 42 provides the modified image data to the stereoscopic analyzer 44 for display to the user 10.


The head unit cameras 62 continually capture images as the user 10 moves their head. The SLAM system 48 analyzes the images and identifies images of objects within the image. The SLAM system 48 analyzes movement of the objects to determine a pose position of the head unit body 26. The SLAM system 48 provides the pose position to the display adjustment algorithm 46. The display adjustment algorithm 46 uses the pose position to further refine the placement value that the display adjustment algorithm 46 provides to the render engine 42. The render engine 42 thus modifies the image data received from the data source 40 based on a combination of the motion sensors in the head unit IMU 60 and images taken by the head unit cameras 62. By way of a practical example, if the user 10 rotates their head to the right, a location of the virtual object 16 rotates to the left within the view of the user 10 thus giving the user 10 the impression that the location of the virtual object 16 remains stationary relative to the real-world object 14 and the totem 34.



FIG. 3 illustrates further details of the head unit 18, the totem 34 and the vision algorithms 38. The head unit 18 further includes an electromagnetic (EM) receiver 150 secured to the head unit body 26. The display system 28, head unit cameras 62 and EM receiver 150 are mounted in fixed positions relative to the head unit body 26. If the user 10 moves their head, the head unit body 26 moves together with the head of the user 10 and the display system 28, head unit cameras 62 and EM receiver 150 move together with the head unit body 26.


The totem 34 has a totem body 152, an EM transmitter 154 and a totem IMU 156. The EM transmitter 154 and the totem IMU 156 are mounted in fixed positions relative to the totem body 152. The user 10 holds on to the totem body 152 and when the user 10 moves the totem body 152, the EM transmitter 154 and the totem IMU 156 move together with the totem body 152. The EM transmitter 154 is capable of transmitting an EM wave and the EM receiver 150 is capable of receiving the EM wave. The totem IMU 156 has one or more gyroscopes and one or more accelerometers. The gyroscopes and accelerometers are typically formed in a semiconductor chip and are capable of detecting movement of the totem IMU 156 and the totem body 152, including movement along three orthogonal axes and rotation about three orthogonal axes.


The vision algorithms 38, in addition to the data source 40, render engine 42, stereoscopic analyzer 44 and SLAM system 48 described with reference to FIG. 2 further include a fusion routine 160, an unfused pose determination modeler 162, a comparator 164, a drift declarer 166, a pose correction routine 168, and a sequencer 170.


The head unit cameras 62 capture images of the real-world object 14. The images of the real-world object 14 are processed by the SLAM system 48 to establish a world frame 172 as described with reference to FIG. 2. Details of how the SLAM system 48 establishes the world frame 172 are not shown in FIG. 3 so as not obscure the drawing.


The EM transmitter 154 transmits an EM wave that is received by the EM receiver 150. The EM wave that is received by the EM receiver 150 indicates a pose or a change of a pose of the EM transmitter 154. The EM receiver 150 enters data of the EM wave into the fusion routine 160.


The totem IMU 156 continually monitors movement of the totem body 152. Data from the totem IMU 156 is entered into the fusion routine 160.


The sequencer 170 executes the fusion routine 160 at a frequency of 250 Hz. The fusion routine 160 combines the data from the EM receiver 150 with the data from the totem IMU 156 and from the SLAM system 48. The EM wave that is received by the EM receiver 150 includes data that represents relatively accurately the pose of the EM transmitter 154 relative to the EM receiver 150 in six degrees of freedom (“6dof”). However, due to EM measurement noise, the measured EM wave may not accurately represent the pose of the EM transmitter 154 relative to the EM receiver 150. The EM measurement noise may result in jitter of the virtual object 16 in FIG. 1. The purpose of combining the data from the totem IMU 156 is to reduce jitter. The fusion routine 160 provides a fused pose 174 within the world frame 172. The fused pose 174 is used by the render engine 42 for purposes of determining the pose of the virtual object 16 in FIG. 1 using the image data from the data source 40.


As shown in FIG. 4, the virtual object 16 is shown in a correct pose relative to the totem 34. Furthermore, if the user 10 moves the totem 34, the virtual object 16 moves together with the totem 34 with a minimal amount of jitter.


The totem IMU 156 essentially measures acceleration and angular rate in six degrees of freedom. The acceleration and angular rate are integrated to determine a location and orientation of the totem IMU 156. Due to integration errors, the fused pose 174 may drift over time.



FIG. 5 illustrates that the virtual object 16 has drifted from its correct pose relative to the totem 34. The drift could be caused by the so-called “model mismatch”, i.e., the imperfect mathematical models that describe the relationship between the physical quantities (e.g., 6dof, acceleration, and angular rate) and the actually measured signal (such as the EM wave measurement, and the IMU signals). And such drift could be amplified for high dynamic motion that can even lead to the fusion algorithm to diverge (i.e., the virtual object would like to be “blown away” from the actual object). In the present example, the virtual object 16 has drifted to the right relative to the totem 34. The fused pose 174 in FIG. 3 is based on the belief by the system that the totem 34 is located further to the right than where it actually is located. The fused data thus has to be corrected so that virtual object 16 is again placed in its correct location relative to the totem 34 as shown in FIG. 4.


In FIG. 3, the sequencer 170 executes the unfused pose determination modeler 162 at a frequency of 240 Hz. The unfused pose determination modeler 162 thus executes asynchronously relative to the fusion routine 160. In the present example, the unfused pose determination modeler 162 makes use of the SLAM system 48 to determine the location of the totem 34. Other systems my use other techniques to determine the location of the totem 34.


The head unit cameras 62 routinely capture images of the totem 34 together with the images of the real-world objects such as the real-world object 14. The images that are captured by the head unit cameras 62 are entered into the SLAM system 48. The SLAM system 48, in addition to determining the locations of the real-world objects such as the real-world object 14, also determines the location of the totem 34. As such, the SLAM system 48 establishes a relationship 180 of the totem 34 relative to the head unit 18. The SLAM system 48 also relies on data from the EM receiver 150 for establishing the relationship 180.


The SLAM system 48 also establishes a relationship 182 of the head unit relative to the world frame 172. As mentioned earlier, the fusion routine 60 receives an input from the SLAM system 48. The fusion routine used the relationship 182 of the head unit to the world frame, i.e. the head pose, as part of the calculations of the fused model of the pose of the totem 34.


The relative pose of the totem 34 to the head unit 18 is established by solving the EM dipole model from the measurement by the EM Receiver 150. The two relationships 180 and 182 thus establish a pose of the totem 34 within the world frame 172. The relationship of the totem 34 and the world frame 172 is stored as an unfused pose 184 within the world frame 172.


The comparator 164 executes synchronously together with the unfused pose determination modeler 162. The comparator 164 compares the fused pose 174 with the unfused location 184. The comparator 164 then enters a difference between the fused pose 174 and the unfused pose 184 into the drift declarer 166. The drift declarer 166 declares a drift only if the difference between the fused pose 174 and unfused pose 184 is more than a predetermined maximum distance 188 that is stored within the vision algorithms 38. The predetermined maximum distance 188 is typically less than 100 mm, and is preferably on the order of 30 mm, 20 mm or more preferably 10 mm and are determined or tuned through data analysis of the sensor fusion system. The drift declarer 166 does not declare a drift if the difference between the fused pose 174 and unfused pose 184 is less than the predetermined maximum distance 188.


When the drift declarer 166 declares a drift, the drift declarer 166 enters the pose reset routine 168. The pose reset routine 168 uses the unfused pose 184 to reset the fused pose 174 in the fusion routine 160, so the drifting is stopped and fusion routine 160 re-starts a pose tracking with the drifting being eliminated.



FIG. 6 illustrates a relationship between a rig frame 196, the world frame 172 and the fused pose 174. The rig frame 196 is a mathematical object that represents a head frame of the head unit 18. The rig frame 196 is located between the waveguides 50A and 50B. In a high-dynamic motion scenario, the fused pose 174 may drift over time (T1; T2; T3; T4) due to imperfect modeling of the actual EM Receiver measurement. The fused pose 174 initially represents the actual pose of the totem 34, but in such a high-dynamic motion scenario it may progressively fail to represent the actual pose of the totem 34 as it drifts further from the actual pose of the totem 34 over time.



FIG. 7 illustrates one method of correcting for the drift. The method illustrated in FIG. 7 has a user drift-detection threshold that is distance-based. By way of example, if the totem 34 is more than 2 meters from the head unit 18, it is not possible for the user 10 to hold on to the totem 34 at such a distance and a drift is declared. If the user 10 can, for example, extend their arm by 0.5 meters, then the system will only declare a drift when the drift has reached an additional 1.5 meters. Such a large drift is undesirable. A system where drift is declared more quickly is more desirable.



FIG. 8 illustrates the manner that the drift is declared according to the embodiment in FIG. 3. As noted with reference to FIG. 3, the unfused pose determination modeler 162 calculates the unfused pose 184 at a frequency of 240 Hz. As noted above, a drift may be declared if a difference between the fused pose 174 and the unfused location 184 is 100 mm or less as described above. At t1, the system-error detection threshold of, for example 100 mm, is reached and a drift is declared. At t2, the drift is immediately corrected. The drift can thus be corrected for smaller distance errors in the system in FIG. 8 than in the system of FIG. 7. Additionally, the drift may again be corrected at t3. Drift can thus be corrected more frequently in the system of FIG. 8 than in the system of FIG. 7.



FIG. 9 shows how the drift is corrected. At A, a relationship is established between the world frame 172 and the rig frame 196. The rig frame 196 is not located in the same position as the EM receiver 150. Due to factory calibration, the location of the EM receiver 150 relative to the rig frame 196 is known. At B, an adjustment is made to calculate the rig frame 196 relative to the location of the EM receiver 150. At C, an estimation is made of the location of the EM receiver 150 relative to the EM transmitter 154. As noted above, such an estimation may be made using the SLAM system 48. Due to factory calibration, the location of the EM transmitter 154 is known relative to the location of the totem IMU 156. At D, an adjustment is made to determine the location of the totem IMU 156 relative to the EM transmitter 154. The calculations made at A, B, C and D thus establish the location of the totem IMU 156 in the world frame 172. The pose of the totem IMU 156 can then be reset based on the location of the totem IMU 156 in the world frame 172 as calculated.



FIG. 10 shows a diagrammatic representation of a machine in the exemplary form of a computer system 900 within which a set of instructions, for causing the machine to perform any one or more of the methodologies discussed herein, may be executed, according to some embodiments. In alternative embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein.


The exemplary computer system 900 includes a processor 902 (e.g., a central processing unit (CPU), a graphics processing unit (GPU) or both), a main memory 904 (e.g., read only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM) or Rambus DRAM (RDRAM), etc.), and a static memory 906 (e.g., flash memory, static random access memory (SRAM), etc.), which communicate with each other via a bus 908.


The computer system 900 may further include a disk drive unit 916, and a network interface device 920.


The disk drive unit 916 includes a machine-readable medium 922 on which is stored one or more sets of instructions 924 (e.g., software) embodying any one or more of the methodologies or functions described herein. The software may also reside, completely or at least partially, within the main memory 904 and/or within the processor 902 during execution thereof by the computer system 900, the main memory 904 and the processor 902 also constituting machine-readable media.


The software may further be transmitted or received over a network 928 via the network interface device 920.


The computer system 900 includes a laser driver chip 950 that is used to drive projectors to generate laser light. The laser driver chip 950 includes its own data store 960 and its own processor 962.


While the machine-readable medium 922 is shown in an exemplary embodiment to be a single medium, the term “machine-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine-readable medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present invention. The term “machine-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals.


While certain exemplary embodiments have been described and shown in the accompanying drawings, it is to be understood that such embodiments are merely illustrative and not restrictive of the current invention, and that this invention is not restricted to the specific constructions and arrangements shown and described since modifications may occur to those ordinarily skilled in the art.

Claims
  • 1. A user interaction system comprising: a totem having:a totem body; anda totem inertial measurement unit (IMU) located on the totem body, to generate a totem IMU signal due to movement of the totem;a head unit;a processor;a storage device connected to the processor;a set of instructions on the storage device and executable by the processor, including:a world frame;an executable routine connected to the totem IMU to generate a first location of the totem in the world frame based on the totem IMU data;a location determination modeler that determines a second location of the totem relative to the world frame; anda location correction routine connected to the first location and the second location to reset sensors of the totem IMU to match the second location;a data source to carry image data; anda display system connected to the data source to display a virtual object using the image data to a user, a location of the virtual object being based on the first location of the totem.
  • 2. The user interaction system of claim 1, further comprising: a comparator connected to the first location and the second location;a drift declarer connected to the comparator to declare a drift only if the first location is more than a predetermined distance from the second location; anda location correction routine connected to the drift declarer to reset sensors of the totem IMU to match the second location only if the drift is declared.
  • 3. The user interaction system of claim 1, wherein location determination modeler determines a location of the totem relative to the head unit and a location of the head unit relative to the world frame to establish a second location of the totem relative to the world frame.
  • 4. The user interaction system of claim 1, further comprising: a render engine having an input connected to the data channel to receive the image data and an output to the display system, the render engine providing a data stream to the display system that includes the virtual object positioned at the first location.
  • 5. The user interaction system of claim 1, wherein the totem IMU reduces jitter of the virtual object.
  • 6. The user interaction system of claim 1, wherein the totem IMU includes at least one of a gyroscope and an accelerometer.
  • 7. The user interaction system of claim 1, wherein the totem includes: an electromagnetic (EM) transmitter on the totem body;wherein the head unit includes:a head unit body; andan EM receiver on the head unit body to receive an EM wave transmitted by the EM transmitter, the EM wave being indicative of a location of the totem;wherein the executable routine is a fusion routine connected to the EM receiver and the totem IMU to generate the first location, which is a fused location of the totem in the world frame based on a combination of the EM wave with the totem IMU data;wherein the location determination modeler is an unfused location determination modeler that determines a location of the totem relative to the head unit and a location of the head unit relative to the world frame to establish the second location, which is an unfused location of the totem relative to the world frame; andwherein a location of the virtual object is based on the fused location of the totem.
  • 8. The user interaction system of claim 7, wherein the fusion routine executes at a first frequency and the unfused location determination modeler executes at a second frequency that is different from the first frequency.
  • 9. The user interaction system of claim 7, wherein the EM receiver detects six-degrees of freedom (“6dof”) movement of the EM transmitter relative to the EM receiver.
  • 10. The user interaction system of claim 7, wherein the predetermined distance is less than 100 mm.
  • 11. The user interaction system of claim 7, wherein the location correction routine is executable to carry out a method that includes: storing a rig frame, being a mathematical object located between eyepieces of the head unit to serve as the basis for defining where an object lies relative to the head unit;taking a measurement to determine a rig frame pose, being the rig frame relative to the world frame;deriving a receiver-to-world pose using known extrinsics as provided by factory-level calibration for the relationship between the EM receiver and the rig frame;estimating an EM relationship between the EM receiver and EM transmitter to derive a transmitter-in-world pose; andderiving a totem IMU-in-world pose as a non-fused pose using known extrinsics of the totem for the relationship between the EM transmitter and the totem IMU.
  • 12. The user interaction system of claim 1, further comprising: a camera on the head unit body, the camera positioned to capture an image of the totem; anda simultaneous localization and mapping (SLAM) system connected to the camera to receive the image of the totem, the SLAM system determining the unfused location based on the image of the totem.
  • 13. The user interaction system of claim 1, wherein the display system includes: a transparent waveguide secured to the head unit body that permits light from the totem through to an eye of a user wearing the head unit body; anda projector that converts that image data to light, the light from the projector entering into the waveguide at an entry pupil and leaving the waveguide at an exit pupil to the eye of the user.
  • 14. The user interaction system of claim 1, further comprising: a head unit detection device that detects movement of the head-mountable frame,the set of instructions including:a display adjustment algorithm that is connected to the head unit detection device and receives a measurement based on movement detected by the head unit detection device and calculates placement value; anda render engine that modifies a position of the virtual object within a view of the eye based on the placement value.
  • 15. The user interaction system of claim 14, wherein the head unit detection device includes: a head unit IMU mounted to the head-mountable frame, the head unit IMU including a motion sensor that detects movement of the head-mountable frame.
  • 16. The user interaction system of claim 15, wherein the head unit IMU includes at least one of a gyroscope and an accelerometer.
  • 17. The user interaction system of claim 14, wherein the head unit detection device includes: a head unit camera mounted to the head-mountable frame, the head unit camera detecting movement of the head-mountable frame by taking images of objects within view of the head unit camera,the set of instructions including:a simultaneous localization and mapping (SLAM) system connected to the camera to receive the images of the objects to detect a pose position of the head unit body, the render engine modifying the position of the virtual object based on the pose position.
CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 17/518,148, filed on Nov. 3, 2021, which is a continuation of U.S. patent application Ser. No. 17/002,663, filed on Aug. 25, 2020 now U.S. Pat. No. 11,216,086, which is a continuation of U.S. patent application Ser. No. 16/523,779, filed on Jul. 26, 2019 now U.S. Pat. No. 10,795,458, which claims priority from U.S. Provisional Patent Application No. 62/714,609, filed on Aug. 3, 2018, and U.S. Provisional Patent Application No. 62/818,032, filed on Mar. 13, 2019, all of which are incorporated herein by reference in their entirety.

US Referenced Citations (507)
Number Name Date Kind
4344092 Miller Aug 1982 A
4652930 Crawford Mar 1987 A
4810080 Grendol et al. Mar 1989 A
4997268 Dauvergne Mar 1991 A
5007727 Kahaney et al. Apr 1991 A
5074295 Willis Dec 1991 A
5240220 Elberbaum Aug 1993 A
5251635 Dumoulin et al. Oct 1993 A
5410763 Bolle May 1995 A
5455625 Englander Oct 1995 A
5495286 Adair Feb 1996 A
5497463 Stein et al. Mar 1996 A
5659701 Amit et al. Aug 1997 A
5682255 Friesem et al. Oct 1997 A
5689669 Lynch Nov 1997 A
5826092 Flannery Oct 1998 A
5854872 Tai Dec 1998 A
5864365 Sramek et al. Jan 1999 A
5937202 Crosetto Aug 1999 A
6002853 de Hond Dec 1999 A
6012811 Chao et al. Jan 2000 A
6016160 Coombs et al. Jan 2000 A
6064749 Hirota et al. May 2000 A
6076927 Owens Jun 2000 A
6079982 Meader Jun 2000 A
6117923 Amagai et al. Sep 2000 A
6119147 Toomey et al. Sep 2000 A
6124977 Takahashi Sep 2000 A
6179619 Tanaka Jan 2001 B1
6191809 Hori et al. Feb 2001 B1
6219045 Leahy et al. Apr 2001 B1
6243091 Berstis Jun 2001 B1
6271843 Lection et al. Aug 2001 B1
6362817 Powers et al. Mar 2002 B1
6375369 Schneider et al. Apr 2002 B1
6385735 Wilson May 2002 B1
6396522 Vu May 2002 B1
6414679 Miodonski et al. Jul 2002 B1
6538655 Kubota Mar 2003 B1
6541736 Huang et al. Apr 2003 B1
6570563 Honda May 2003 B1
6573903 Gantt Jun 2003 B2
6590593 Robertson et al. Jul 2003 B1
6621508 Shiraishi et al. Sep 2003 B1
6690393 Heron et al. Feb 2004 B2
6757068 Foxlin Jun 2004 B2
6784901 Harvfey et al. Aug 2004 B1
6961055 Doak Nov 2005 B2
7046515 Wyatt May 2006 B1
7051219 Hwang May 2006 B2
7076674 Cervantes Jul 2006 B2
7111290 Yates, Jr. Sep 2006 B1
7119819 Robertson et al. Oct 2006 B1
7219245 Raghuvanshi May 2007 B1
7382288 Wilson Jun 2008 B1
7414629 Santodomingo Aug 2008 B2
7431453 Hogan Oct 2008 B2
7467356 Gettman et al. Dec 2008 B2
7542040 Templeman Jun 2009 B2
7573640 Nivon et al. Aug 2009 B2
7653877 Matsuda Jan 2010 B2
7663625 Chartier et al. Feb 2010 B2
7724980 Shenzhi May 2010 B1
7746343 Charaniya et al. Jun 2010 B1
7751662 Kleemann Jul 2010 B2
7758185 Lewis Jul 2010 B2
7788323 Greenstein et al. Aug 2010 B2
7804507 Yang et al. Sep 2010 B2
7814429 Buffet et al. Oct 2010 B2
7817150 Reichard et al. Oct 2010 B2
7844724 Van Wie et al. Nov 2010 B2
8060759 Arnan et al. Nov 2011 B1
8120851 Iwasa Feb 2012 B2
8214660 Capps, Jr. Jul 2012 B2
8246408 Elliot Aug 2012 B2
8353594 Lewis Jan 2013 B2
8360578 Nummela et al. Jan 2013 B2
8508676 Silverstein et al. Aug 2013 B2
8547638 Levola Oct 2013 B2
8605764 Rothaar et al. Oct 2013 B1
8619365 Harris et al. Dec 2013 B2
8696113 Lewis Apr 2014 B2
8698701 Margulis Apr 2014 B2
8733927 Lewis May 2014 B1
8736636 Kang May 2014 B2
8759929 Shiozawa et al. Jun 2014 B2
8793770 Lim Jul 2014 B2
8823855 Hwang Sep 2014 B2
8847988 Geisner et al. Sep 2014 B2
8874673 Kim Oct 2014 B2
9010929 Lewis Apr 2015 B2
9015501 Gee Apr 2015 B2
9086537 Iwasa et al. Jul 2015 B2
9095437 Boyden et al. Aug 2015 B2
9239473 Lewis Jan 2016 B2
9244293 Lewis Jan 2016 B2
9244533 Friend et al. Jan 2016 B2
9383823 Geisner et al. Jul 2016 B2
9489027 Ogletree Nov 2016 B1
9519305 Wolfe Dec 2016 B2
9581820 Robbins Feb 2017 B2
9582060 Balatsos Feb 2017 B2
9658473 Lewis May 2017 B2
9671566 Abovitz et al. Jun 2017 B2
9671615 Vallius et al. Jun 2017 B1
9696795 Marcolina et al. Jul 2017 B2
9798144 Sako et al. Oct 2017 B2
9874664 Stevens et al. Jan 2018 B2
9880441 Osterhout Jan 2018 B1
9918058 Takahasi et al. Mar 2018 B2
9955862 Freeman et al. May 2018 B2
9978118 Ozgumer et al. May 2018 B1
9996797 Holz et al. Jun 2018 B1
10018844 Levola et al. Jul 2018 B2
10082865 Raynal et al. Sep 2018 B1
10151937 Lewis Dec 2018 B2
10185147 Lewis Jan 2019 B2
10218679 Jawahar Feb 2019 B1
10241545 Richards et al. Mar 2019 B1
10317680 Richards et al. Jun 2019 B1
10436594 Belt et al. Oct 2019 B2
10516853 Gibson et al. Dec 2019 B1
10551879 Richards et al. Feb 2020 B1
10578870 Kimmel Mar 2020 B2
10698202 Kimmel et al. Jun 2020 B2
10856107 Mycek et al. Oct 2020 B2
10825424 Zhang Nov 2020 B2
10987176 Poltaretskyi et al. Apr 2021 B2
11190681 Brook et al. Nov 2021 B1
11209656 Choi et al. Dec 2021 B1
11236993 Hall et al. Feb 2022 B1
20010010598 Aritake et al. Aug 2001 A1
20010018667 Kim Aug 2001 A1
20020007463 Fung Jan 2002 A1
20020108064 Nunally Feb 2002 A1
20020063913 Nakamura et al. May 2002 A1
20020071050 Homberg Jun 2002 A1
20020095463 Matsuda Jul 2002 A1
20020113820 Robinson et al. Aug 2002 A1
20020122648 Mule'et al. Sep 2002 A1
20020140848 Cooper et al. Oct 2002 A1
20030028816 Bacon Feb 2003 A1
20030048456 Hill Mar 2003 A1
20030067685 Niv Apr 2003 A1
20030077458 Korenaga et al. Apr 2003 A1
20030115494 Cervantes Jun 2003 A1
20030218614 Lavelle et al. Nov 2003 A1
20030219992 Schaper Nov 2003 A1
20030226047 Park Dec 2003 A1
20040001533 Tran et al. Jan 2004 A1
20040021600 Wittenberg Feb 2004 A1
20040025069 Gary et al. Feb 2004 A1
20040042377 Nikoloai et al. Mar 2004 A1
20040073822 Greco Apr 2004 A1
20040073825 Itoh Apr 2004 A1
20040111248 Granny et al. Jun 2004 A1
20040113887 Pair et al. Jun 2004 A1
20040174496 Ji et al. Sep 2004 A1
20040186902 Stewart Sep 2004 A1
20040193441 Altieri Sep 2004 A1
20040201857 Foxlin Oct 2004 A1
20040238732 State et al. Dec 2004 A1
20040240072 Schindler et al. Dec 2004 A1
20040246391 Travis Dec 2004 A1
20040268159 Aasheim et al. Dec 2004 A1
20050001977 Zelman Jan 2005 A1
20050034002 Flautner Feb 2005 A1
20050093719 Okamoto et al. May 2005 A1
20050128212 Edecker et al. Jun 2005 A1
20050157159 Komiya et al. Jul 2005 A1
20050177385 Hull Aug 2005 A1
20050231599 Yamasaki Oct 2005 A1
20050273792 Inohara et al. Dec 2005 A1
20060013435 Rhoads Jan 2006 A1
20060015821 Jacques Parker et al. Jan 2006 A1
20060019723 Vorenkamp Jan 2006 A1
20060038880 Starkweather et al. Feb 2006 A1
20060050224 Smith Mar 2006 A1
20060090092 Verhulst Apr 2006 A1
20060126181 Levola Jun 2006 A1
20060129852 Bonola Jun 2006 A1
20060132914 Weiss et al. Jun 2006 A1
20060179329 Terechko Aug 2006 A1
20060221448 Nivon et al. Oct 2006 A1
20060228073 Mukawa et al. Oct 2006 A1
20060250322 Hall et al. Nov 2006 A1
20060259621 Ranganathan Nov 2006 A1
20060268220 Hogan Nov 2006 A1
20070058248 Nguyen et al. Mar 2007 A1
20070103836 Oh May 2007 A1
20070124730 Pytel May 2007 A1
20070159673 Freeman et al. Jul 2007 A1
20070188837 Shimizu et al. Aug 2007 A1
20070198886 Saito Aug 2007 A1
20070204672 Huang et al. Sep 2007 A1
20070213952 Cirelli Sep 2007 A1
20070283247 Brenneman et al. Dec 2007 A1
20080002259 Ishizawa et al. Jan 2008 A1
20080002260 Arrouy et al. Jan 2008 A1
20080030429 Hailpern Feb 2008 A1
20080043334 Itzkovitch et al. Feb 2008 A1
20080046773 Ham Feb 2008 A1
20080063802 Maula et al. Mar 2008 A1
20080068557 Menduni et al. Mar 2008 A1
20080125218 Collins May 2008 A1
20080146942 Dala-Krishna Jun 2008 A1
20080173036 Willaims Jul 2008 A1
20080177506 Kim Jul 2008 A1
20080205838 Crippa et al. Aug 2008 A1
20080215907 Wilson Sep 2008 A1
20080225393 Rinko Sep 2008 A1
20080235570 Sawada et al. Sep 2008 A1
20080246693 Hailpern et al. Oct 2008 A1
20080316768 Travis Dec 2008 A1
20090076791 Rhoades et al. Mar 2009 A1
20090091583 McCoy Apr 2009 A1
20090153797 Allon et al. Jun 2009 A1
20090224416 Laakkonen et al. Sep 2009 A1
20090245730 Kleemann Oct 2009 A1
20090287728 Martine et al. Nov 2009 A1
20090300528 Stambaugh Dec 2009 A1
20090310633 Ikegami Dec 2009 A1
20100005326 Archer Jan 2010 A1
20100019962 Fujita Jan 2010 A1
20100056274 Uusitalo et al. Mar 2010 A1
20100063854 Purvis et al. Mar 2010 A1
20100070378 Trotman et al. Mar 2010 A1
20100079841 Levola Apr 2010 A1
20100115428 Shuping et al. May 2010 A1
20100153934 Lachner Jun 2010 A1
20100194632 Raento et al. Aug 2010 A1
20100205541 Rappaport et al. Aug 2010 A1
20100214284 Rieffel et al. Aug 2010 A1
20100232016 Landa et al. Sep 2010 A1
20100232031 Batchko et al. Sep 2010 A1
20100244168 Shiozawa et al. Sep 2010 A1
20100274567 Carlson et al. Oct 2010 A1
20100274627 Carlson Oct 2010 A1
20100277803 Pockett et al. Nov 2010 A1
20100284085 Laakkonen Nov 2010 A1
20100296163 Sarikko Nov 2010 A1
20100309687 Sampsell et al. Dec 2010 A1
20110010636 Hamilton, II et al. Jan 2011 A1
20110021263 Anderson et al. Jan 2011 A1
20110022870 Mcgrane Jan 2011 A1
20110041083 Gabai et al. Feb 2011 A1
20110050640 Lundback et al. Mar 2011 A1
20110050655 Mukawa Mar 2011 A1
20110122240 Becker May 2011 A1
20110145617 Thomson et al. Jun 2011 A1
20110170801 Lu et al. Jul 2011 A1
20110218733 Hamza et al. Sep 2011 A1
20110286735 Temblay Nov 2011 A1
20110291969 Rashid et al. Dec 2011 A1
20120011389 Driesen Jan 2012 A1
20120050535 Densham et al. Mar 2012 A1
20120075501 Oyagi et al. Mar 2012 A1
20120081392 Arthur Apr 2012 A1
20120089854 Breakstone Apr 2012 A1
20120113235 Shintani May 2012 A1
20120127062 Bar-Zeev et al. May 2012 A1
20120154557 Perez et al. Jun 2012 A1
20120215094 Rahimian et al. Aug 2012 A1
20120218301 Miller Aug 2012 A1
20120246506 Knight Sep 2012 A1
20120249416 Maciocci et al. Oct 2012 A1
20120249741 Maciocci et al. Oct 2012 A1
20120260083 Andrews Oct 2012 A1
20120307075 Margalit Dec 2012 A1
20120307362 Silverstein et al. Dec 2012 A1
20120314959 White et al. Dec 2012 A1
20120320460 Levola Dec 2012 A1
20120326948 Crocco et al. Dec 2012 A1
20130021486 Richardon Jan 2013 A1
20130050258 Liu et al. Feb 2013 A1
20130050642 Lewis et al. Feb 2013 A1
20130050833 Lewis et al. Feb 2013 A1
20130051730 Travers et al. Feb 2013 A1
20130061240 Yan et al. Mar 2013 A1
20130077049 Bohn Mar 2013 A1
20130077170 Ukuda Mar 2013 A1
20130094148 Sloane Apr 2013 A1
20130129282 Li May 2013 A1
20130162940 Kurtin et al. Jun 2013 A1
20130169923 Schnoll et al. Jul 2013 A1
20130205126 Kruglick Aug 2013 A1
20130222386 Tannhauser et al. Aug 2013 A1
20130268257 Hu Oct 2013 A1
20130278633 Ahn et al. Oct 2013 A1
20130314789 Saarikko et al. Nov 2013 A1
20130318276 Dalal Nov 2013 A1
20130336138 Venkatraman et al. Dec 2013 A1
20130342564 Kinnebrew et al. Dec 2013 A1
20130342570 Kinnebrew et al. Dec 2013 A1
20130342571 Kinnebrew et al. Dec 2013 A1
20130343408 Cook Dec 2013 A1
20140002329 Nishimaki et al. Jan 2014 A1
20140013098 Yeung Jan 2014 A1
20140016821 Arth et al. Jan 2014 A1
20140022819 Oh et al. Jan 2014 A1
20140078023 Ikeda et al. Mar 2014 A1
20140082526 Park et al. Mar 2014 A1
20140119598 Ramachandran et al. May 2014 A1
20140126769 Reitmayr et al. May 2014 A1
20140140653 Brown et al. May 2014 A1
20140149573 Tofighbakhsh et al. May 2014 A1
20140168260 O'Brien et al. Jun 2014 A1
20140244983 Mcdonald et al. Aug 2014 A1
20140266987 Magyari Sep 2014 A1
20140267419 Ballard et al. Sep 2014 A1
20140274391 Stafford Sep 2014 A1
20140282105 Nordstrom Sep 2014 A1
20140292645 Tsurumi et al. Oct 2014 A1
20140313228 Kasahara Oct 2014 A1
20143404498 Plagemann et al. Nov 2014
20140359589 Kodsky et al. Dec 2014 A1
20140375680 Ackerman et al. Dec 2014 A1
20150005785 Olson Jan 2015 A1
20150009099 Queen Jan 2015 A1
20150077312 Wang Mar 2015 A1
20150097719 Balachandreswaran et al. Apr 2015 A1
20150123966 Newman May 2015 A1
20150130790 Vazquez, II et al. May 2015 A1
20150134995 Park et al. May 2015 A1
20150138248 Schrader May 2015 A1
20150155939 Oshima et al. Jun 2015 A1
20150168221 Mao et al. Jun 2015 A1
20150205126 Schowengerdt Jul 2015 A1
20150235427 Nobori et al. Aug 2015 A1
20150235431 Schowengerdt Aug 2015 A1
20150253651 Russell et al. Sep 2015 A1
20150256484 Cameron Sep 2015 A1
20150269784 Miyawaki et al. Sep 2015 A1
20150294483 Wells et al. Oct 2015 A1
20150301955 Yakovenko et al. Oct 2015 A1
20150310657 Eden Oct 2015 A1
20150338915 Publicover et al. Nov 2015 A1
20150355481 Hilkes et al. Dec 2015 A1
20160004102 Nisper et al. Jan 2016 A1
20160015470 Border Jan 2016 A1
20160027215 Burns et al. Jan 2016 A1
20160033770 Fujimaki et al. Feb 2016 A1
20160051217 Douglas et al. Feb 2016 A1
20160077338 Robbins et al. Mar 2016 A1
20160085285 Mangione-Smith Mar 2016 A1
20160085300 Robbins et al. Mar 2016 A1
20160091720 Stafford et al. Mar 2016 A1
20160093099 Bridges Mar 2016 A1
20160093269 Buckley et al. Mar 2016 A1
20160103326 Kimura et al. Apr 2016 A1
20160123745 Cotier et al. May 2016 A1
20160139402 Lapstun May 2016 A1
20160139411 Kang et al. May 2016 A1
20160155273 Lyren et al. Jun 2016 A1
20160180596 Gonzalez del Rosario Jun 2016 A1
20160187654 Border et al. Jun 2016 A1
20160191887 Casas Jun 2016 A1
20160202496 Billetz et al. Jul 2016 A1
20160217624 Finn et al. Jul 2016 A1
20160266412 Yoshida Sep 2016 A1
20160267708 Nistico et al. Sep 2016 A1
20160274733 Hasegawa et al. Sep 2016 A1
20160287337 Aram et al. Oct 2016 A1
20160300388 Stafford et al. Oct 2016 A1
20160321551 Priness et al. Nov 2016 A1
20160327798 Xiao et al. Nov 2016 A1
20160334279 Mittleman et al. Nov 2016 A1
20160357255 Lindh et al. Dec 2016 A1
20160370404 Quadrat et al. Dec 2016 A1
20160370510 Thomas Dec 2016 A1
20170038607 Camara Feb 2017 A1
20170060225 Zha et al. Mar 2017 A1
20170061696 Li et al. Mar 2017 A1
20170064066 Das et al. Mar 2017 A1
20170100664 Osterhout et al. Apr 2017 A1
20170102544 Vallius et al. Apr 2017 A1
20170115487 Travis Apr 2017 A1
20170122725 Yeoh et al. May 2017 A1
20170123526 Trail et al. May 2017 A1
20170127295 Black et al. May 2017 A1
20170131569 Aschwanden et al. May 2017 A1
20170147066 Katz et al. May 2017 A1
20170160518 Lanman et al. Jun 2017 A1
20170161951 Fix et al. Jun 2017 A1
20170185261 Perez et al. Jun 2017 A1
20170192239 Nakamura et al. Jul 2017 A1
20170201709 Igarashi et al. Jul 2017 A1
20170205903 Miller Jul 2017 A1
20170206668 Poulos et al. Jul 2017 A1
20170213388 Margolis et al. Jul 2017 A1
20170214907 Lapstun Jul 2017 A1
20170219841 Popovich et al. Aug 2017 A1
20170232345 Rofougaran et al. Aug 2017 A1
20170235126 DiDomenico Aug 2017 A1
20170235129 Kamakura Aug 2017 A1
20170235142 Wall et al. Aug 2017 A1
20170235144 Piskunov et al. Aug 2017 A1
20170235147 Kamakura Aug 2017 A1
20170243403 Daniels et al. Aug 2017 A1
20170246070 Osterhout et al. Aug 2017 A1
20170254832 Ho et al. Sep 2017 A1
20170256096 Faaborg et al. Sep 2017 A1
20170258526 Lang Sep 2017 A1
20170266529 Reikmoto Sep 2017 A1
20170270712 Tyson et al. Sep 2017 A1
20170281054 Stever et al. Oct 2017 A1
20170287376 Bakar et al. Oct 2017 A1
20170293141 Schowengerdt et al. Oct 2017 A1
20170307886 Stenberg et al. Oct 2017 A1
20170307891 Bucknor et al. Oct 2017 A1
20170312032 Amanatullah et al. Nov 2017 A1
20170322418 Liu et al. Nov 2017 A1
20170322426 Tervo Nov 2017 A1
20170329137 Tervo Nov 2017 A1
20170332098 Rusanovskyy et al. Nov 2017 A1
20170336636 Amitai et al. Nov 2017 A1
20170357332 Balan Dec 2017 A1
20170363871 Vallius Dec 2017 A1
20170371394 Chan Dec 2017 A1
20170371661 Sparling Dec 2017 A1
20180014266 Chen Jan 2018 A1
20180024289 Fattal Jan 2018 A1
20180044173 Netzer Feb 2018 A1
20180052007 Teskey et al. Feb 2018 A1
20180052501 Jones, Jr. et al. Feb 2018 A1
20180059305 Popovich et al. Mar 2018 A1
20180067779 Pillalamarri et al. Mar 2018 A1
20180070855 Eichler Mar 2018 A1
20180082480 White et al. Mar 2018 A1
20180084245 Lapstun Mar 2018 A1
20180088185 Woods et al. Mar 2018 A1
20180102981 Kurtzman et al. Apr 2018 A1
20180108179 Tomlin et al. Apr 2018 A1
20180114298 Malaika et al. Apr 2018 A1
20180129112 Osterhout May 2018 A1
20180131907 Schmirler et al. May 2018 A1
20180136466 Ko May 2018 A1
20180144691 Choi et al. May 2018 A1
20180150971 Adachi et al. May 2018 A1
20180151796 Akahane May 2018 A1
20180172995 Lee et al. Jun 2018 A1
20180188115 Hsu et al. Jul 2018 A1
20180189568 Powderly et al. Jul 2018 A1
20180190017 Mendez et al. Jul 2018 A1
20180191990 Motoyama et al. Jul 2018 A1
20180218545 Garcia et al. Aug 2018 A1
20180250589 Cossairt et al. Sep 2018 A1
20180260218 Gopal Sep 2018 A1
20180284877 Klein Oct 2018 A1
20180292654 Wall et al. Oct 2018 A1
20180299678 Singer et al. Oct 2018 A1
20180357472 Dreessen Dec 2018 A1
20190005069 Filgueiras et al. Jan 2019 A1
20190011691 Peyman Jan 2019 A1
20190056591 Tervo et al. Feb 2019 A1
20190087015 Lam et al. Mar 2019 A1
20190101758 Zhu et al. Apr 2019 A1
20190107723 Lee et al. Apr 2019 A1
20190137788 Suen May 2019 A1
20190155034 Singer et al. May 2019 A1
20190155439 Mukherjee et al. May 2019 A1
20190158926 Kang et al. May 2019 A1
20190162950 Lapstun May 2019 A1
20190167095 Krueger Jun 2019 A1
20190172216 Ninan et al. Jun 2019 A1
20190178654 Hare Jun 2019 A1
20190182415 Sivan Jun 2019 A1
20190196690 Chong et al. Jun 2019 A1
20190206116 Xu et al. Jul 2019 A1
20190219815 Price et al. Jul 2019 A1
20190243123 Bohn Aug 2019 A1
20190287270 Nakamura et al. Sep 2019 A1
20190318502 He et al. Oct 2019 A1
20190318540 Piemonte et al. Oct 2019 A1
20190321728 Imai et al. Oct 2019 A1
20190347853 Chen et al. Nov 2019 A1
20190380792 Poltaretskyi et al. Dec 2019 A1
20190388182 Kumar et al. Dec 2019 A1
20200066045 Stahl et al. Feb 2020 A1
20200098188 Bar-Zeev et al. Mar 2020 A1
20200100057 Galon et al. Mar 2020 A1
20200110928 Al Jazaery et al. Apr 2020 A1
20200117267 Gibson et al. Apr 2020 A1
20200117270 Gibson et al. Apr 2020 A1
20200184217 Faulkner Jun 2020 A1
20200184653 Faulker Jun 2020 A1
20200202759 Ukai et al. Jun 2020 A1
20200242848 Ambler et al. Jul 2020 A1
20200309944 Thoresen et al. Oct 2020 A1
20200356161 Wagner Nov 2020 A1
20200368616 Delamont Nov 2020 A1
20200391115 Leeper et al. Dec 2020 A1
20200409528 Lee Dec 2020 A1
20210008413 Asikainen et al. Jan 2021 A1
20210033871 Jacoby et al. Feb 2021 A1
20210041951 Gibson et al. Feb 2021 A1
20210053820 Gurin et al. Feb 2021 A1
20210093391 Poltaretskyi et al. Apr 2021 A1
20210093410 Gaborit et al. Apr 2021 A1
20210093414 Moore et al. Apr 2021 A1
20210097886 Kuester et al. Apr 2021 A1
20210132380 Wieczorek May 2021 A1
20210141225 Meynen et al. May 2021 A1
20210142582 Jones et al. May 2021 A1
20210158627 Cossairt et al. May 2021 A1
20210173480 Osterhout et al. Jun 2021 A1
20220366598 Azimi et al. Nov 2022 A1
Foreign Referenced Citations (112)
Number Date Country
100416340 Sep 2008 CN
101449270 Jun 2009 CN
103460255 Dec 2013 CN
104040410 Sep 2014 CN
104603675 May 2015 CN
105938426 Sep 2016 CN
106662754 May 2017 CN
107683497 Feb 2018 CN
109223121 Jan 2019 CN
105190427 Nov 2019 CN
0504930 Mar 1992 EP
0535402 Apr 1993 EP
0632360 Jan 1995 EP
1215522 Jun 2002 EP
1494110 Jan 2005 EP
1938141 Jul 2008 EP
1943556 Jul 2008 EP
2290428 Mar 2011 EP
2350774 Aug 2011 EP
1237067 Jan 2016 EP
3139245 Mar 2017 EP
3164776 May 2017 EP
3236211 Oct 2017 EP
2723240 Aug 2018 EP
2896986 Feb 2021 EP
2499635 Aug 2013 GB
2542853 Apr 2017 GB
938DEL2004 Jun 2006 IN
H03-036974 Apr 1991 JP
H10-333094 Dec 1998 JP
2002-529806 Sep 2002 JP
2003-029198 Jan 2003 JP
2003-141574 May 2003 JP
2003-228027 Aug 2003 JP
2003-329873 Nov 2003 JP
2005-303843 Oct 2005 JP
2007-012530 Jan 2007 JP
2007-86696 Apr 2007 JP
2007-273733 Oct 2007 JP
2008-257127 Oct 2008 JP
2009-090689 Apr 2009 JP
2009-244869 Oct 2009 JP
2010-014443 Jan 2010 JP
2010-139575 Jun 2010 JP
2011-033993 Feb 2011 JP
2011-257203 Dec 2011 JP
2011-530131 Dec 2011 JP
2012-015774 Jan 2012 JP
2012-235036 Nov 2012 JP
2013-525872 Jun 2013 JP
2014-500522 Jan 2014 JP
2014-192550 Oct 2014 JP
2015-191032 Nov 2015 JP
2016-502120 Jan 2016 JP
2016-85463 May 2016 JP
2016-516227 Jun 2016 JP
2017-015697 Jan 2017 JP
2017-153498 Sep 2017 JP
2017-531840 Oct 2017 JP
6232763 Nov 2017 JP
6333965 May 2018 JP
2005-0010775 Jan 2005 KR
10-2006-0059992 Jun 2006 KR
10-2011-0006408 Jan 2011 KR
10-1372623 Mar 2014 KR
10-2017-0017243 Feb 2017 KR
201219829 May 2012 TW
201803289 Jan 2018 TW
1991000565 Jan 1991 WO
2000030368 Jun 2000 WO
2002071315 Sep 2002 WO
2004095248 Nov 2004 WO
2006132614 Dec 2006 WO
2007041678 Apr 2007 WO
2007037089 May 2007 WO
2007085628 Aug 2007 WO
2007085682 Aug 2007 WO
2007102144 Sep 2007 WO
2008148927 Dec 2008 WO
2009101238 Aug 2009 WO
2010015807 Feb 2010 WO
2014203440 Dec 2010 WO
2012030787 Mar 2012 WO
2013049012 Apr 2013 WO
2013062701 May 2013 WO
2014033306 Mar 2014 WO
2015079610 Jun 2015 WO
2015143641 Oct 2015 WO
2015143641 Oct 2015 WO
2016054092 Apr 2016 WO
2017004695 Jan 2017 WO
2017044761 Mar 2017 WO
2017049163 Mar 2017 WO
2017051595 Mar 2017 WO
2017120475 Jul 2017 WO
2017176861 Oct 2017 WO
2017203201 Nov 2017 WO
2018008232 Jan 2018 WO
2018031261 Feb 2018 WO
2018022523 Feb 2018 WO
2018044537 Mar 2018 WO
2018039273 Mar 2018 WO
2018057564 Mar 2018 WO
2018085287 May 2018 WO
2018087408 May 2018 WO
2018097831 May 2018 WO
2018166921 Sep 2018 WO
2018166921 Sep 2018 WO
2018236587 Dec 2018 WO
2019040493 Feb 2019 WO
2019148154 Aug 2019 WO
2020010226 Jan 2020 WO
Non-Patent Literature Citations (260)
Entry
“Decision of Rejection dated Jan. 5, 2023 with English translation”, Chinese Patent Application No. 201880079474.6, (10 pages).
“Extended European Search Report dated Apr. 5, 2023”, European Patent Application No. 20888716.6, (11 pages).
“Final Office Action dated Mar. 10, 2023”, U.S. Appl. No. 17/357,795, (15 pages).
“First Office Action dated Apr. 21, 2023 with English translation”, Japanese Patent Application No. 2021-509779, (26 pages).
“First Office Action dated Apr. 13, 2023 with English Translation”, Japanese Patent Application No. 2020-567766, (7 pages).
“First Office Action dated Dec. 22, 2022 with English translation”, Chinese Patent Application No. 201980061450.2, (11 pages).
“First Office Action dated Jan. 24, 2023 with English translation”, Japanese Patent Application No. 2020-549034, (7 pages).
“First Office Action dated Jan. 30, 2023 with English translation”, Chinese Patent Application No. 201980082951.9, (5 pages).
“First Office Action dated Mar. 27, 2023 with English translation”, Japanese Patent Application No. 2020-566617, (6 pages).
“First Office Action dated Mar. 6, 2023 with English translation”, Korean Patent Application No. 10-2020-7019685, (7 pages).
“Non Final Office Action dated Apr. 13, 2023”, U.S. Appl. No. 17/098,043, (7 pages).
“Non Final Office Action dated Feb. 3, 2023”, U.S. Appl. No. 17/429, 100, (16 pages).
“Non Final Office Action dated Feb. 3, 2023”, U.S. Appl. No. 17/497,965, (32 pages).
“Non Final Office Action dated Jun. 14, 2023”, U.S. Appl. No. 17/516,483, (10 pages).
“Non Final Office Action dated Mar. 1, 2023”, U.S. Appl. No. 18/046,739, (34 pages).
“Non Final Office Action dated May 11, 2023”, U.S. Appl. No. 17/822,279, (24 pages).
“Office Action dated Apr. 13, 2023 with English translation”, Japanese Patent Application No. 2020-533730, (13 pages).
“Office Action dated Mar. 30, 2023 with English translation”, Japanese Patent Application No. 2020-566620, (10 pages).
“Second Office Action dated May 2, 2023 with English Translation”, Japanese Patent Application No. 2020-549034, (6 pages).
Li, Yujia , et al., “Graph Matching Networks for Learning the Similarity of Graph Structured Objects”, arxiv.org, Cornell University Library, 201 Olin Library Cornell University Ithaca, NY 14853, XP081268608, Apr. 29, 2019.
Luo, Zixin , et al., “ContextDesc: Local Descriptor Augmentation With Cross-Modality Context”, 2019 ieee/cvf Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, XP033686823, DOI: 10.1109/CVPR.2019.00263 [retrieved on Jan. 8, 2020], Jun. 15, 2019, pp. 2522-2531.
Zhang, Zen , et al., “Deep Graphical Feature Learning for the Feature Matching Problem”, 2019 IEEE/CVF International Conference on Computer Vision (ICCV), IEEE, XP033723985, DOI: 10.1109/ICCV.2019.00519 [retrieved on Feb. 24, 2020], Oct. 27, 2019, pp. 5086-5095.
“ARToolKit: Hardware”, https://web.archive.org/web/20051013062315/http://www.hitl.washington.edu:80/artoolkit/documentation/hardware.htm (downloaded Oct. 26, 2020), Oct. 13, 2015, (3 pages).
“Communication according to Rule 164(1) EPC dated Feb. 23, 2022”, European Patent Application No. 20753144.3 , (11 pages).
“Communication Pursuant to Article 94(3) EPC dated Sep. 4, 2019”, European Patent Application No. 10793707.0 , (4 pages).
“Communication Pursuant to Article 94(3) EPC dated Apr. 25, 2022”, European Patent Application No. 18885707.2 , (5 pages).
“Communication Pursuant to Article 94(3) EPC dated Jan. 4, 2022”, European Patent Application No. 20154070.5 , (8 pages).
“Communication Pursuant to Article 94(3) EPC dated May 30, 2022”, European Patent Application No. 19768418.6 , (6 pages).
“Communication Pursuant to Article 94(3) EPC dated Oct. 21, 2021”, European Patent Application No. 16207441.3 , (4 pages).
“Communication Pursuant to Rule 164(1) EPC datedFeb. 23, 2022”, European Patent Application No. 20753144.3 , (11 pages).
“Communication Pursuant to Rule 164(1) EPC dated Jul. 27, 2021”, European Patent Application No. 19833664.6 , (11 pages).
“European Search Report dated Oct. 15, 2020”, European Patent Application No. 20180623.9 , (10 pages).
“Extended European Search Report dated Dec. 14, 2022”, European Patent Application No. 20886547.7 , (8 pages).
“Extended European Search Report dated Jul. 20, 2022”, European Patent Application No. 19885958.9 , (9 pages).
“Extended European Search Report dated May 20, 2020”, European Patent Application No. 20154070.5 , (7 pages).
“Extended European Search Report dated Jan. 22, 2021”, European Patent Application No. 18890390.0 , (11 pages).
“Extended European Search Report dated Nov. 3, 2020”, European Patent Application No. 18885707.2 , (7 pages).
“Extended European Search Report dated Jun. 30, 2021”, European Patent Application No. 19811971.1 , (9 pages).
“Extended European Search Report dated Mar. 4, 2021”, European Patent Application No. 19768418.6 , (9 pages).
“Extended European Search Report dated Nov. 4, 2020”, European Patent Application No. 20190980.1 , (14 pages).
“Extended European Search Report dated Aug. 24, 2022”, European Patent Application No. 20846338.0 , (13 pages).
“Extended European Search Report dated Aug. 8, 2022”, European Patent Application No. 19898874.3 , (8 pages).
“Extended European Search Report dated Sep. 8, 2022”, European Patent Application No. 20798769.4 , (13 pages).
“Extended European Search Report dated Nov. 3, 2022”, European Patent Application No. 20770244.0 , (23 pages).
“Extended European Search Report dated Jun. 12, 2017”, European Patent Application No. 16207441.3 , (8 pages).
“Extended European Search Report dated Jan. 28, 2022”, European Patent Application No. 19815876.8 , (9 pages).
“Extended European Search Report dated Jan. 4, 2022”, European Patent Application No. 19815085.6 , (9 pages).
“Extended European Search Report dated Jul. 16, 2021”, European Patent Application No. 19810142.0 , (14 pages).
“Extended European Search Report dated Jul. 30, 2021”, European Patent Application No. 19839970.1 , (7 pages).
“Extended European Search Report dated Jun. 19, 2020”, European Patent Application No. 20154750.2 , (10 pages).
“Extended European Search Report dated Mar. 22, 2022”, European Patent Application No. 19843487.0 , (14 pages).
“Extended European Search Report dated May 16, 2022”, European Patent Application No. 19871001.4 , (9 pages).
“Extended European Search Report dated May 30, 2022”, European Patent Application No. 20753144.3, (10 pages).
“Extended European Search Report dated Oct. 27, 2021”, European Patent Application No. 19833664.6, (10 pages).
“Extended European Search Report dated Sep. 20, 2021”, European Patent Application No. 19851373.1, (8 pages).
“Extended European Search Report dated Sep. 28, 2021”, European Patent Application No. 19845418.3, (13 pages).
“Final Office Action dated Aug. 10, 2020”, U.S. Appl. No. 16/225,961, (13 pages).
“Final Office Action dated Dec. 29, 2022”, U.S. Appl. No. 17/098,059, (32 pages).
“Final Office Action dated Dec. 4, 2019”, U.S. Appl. No. 15/564,517, (15 pages).
“Final Office Action dated Feb. 19, 2020”, U.S. Appl. No. 15/552,897, (17 pages).
“Final Office Action dated Feb. 23, 2022”, U.S. Appl. No. 16/748, 193, (23 pages).
“Final Office Action dated Feb. 3, 2022”, U.S. Appl. No. 16/864,721, (36 pages).
“Final Office Action dated Jul. 13, 2022”, U.S. Appl. No. 17/262, 991, (18 pages).
“Final Office Action dated Jun. 15, 2021”, U.S. Appl. No. 16/928,313, (42 pages).
“Final Office Action dated Mar. 1, 2021”, U.S. Appl. No. 16/214,575, (29 pages).
“Final Office Action dated Mar. 19, 2021”, U.S. Appl. No. 16/530,776, (25 pages).
“Final Office Action dated Nov. 24, 2020”, U.S. Appl. No. 16/435,933, (44 pages).
“Final Office Action dated Sep. 17, 2021”, U.S. Appl. No. 16/938,782, (44 pages).
“First Examination Report dated Dec. 8, 2022”, Australian Patent Application No. 2018392482, (3 pages).
“First Examination Report dated Jul. 27, 2022”, Chinese Patent Application No. 201980036675.2, (5 pages).
“First Examination Report dated Jul. 28, 2022”, Indian Patent Application No. 202047024232, (6 pages).
“First Examination Report dated May 13, 2022”, Indian Patent Application No. 202047026359, (8 pages).
“First Office Action dated Feb. 11, 2022 with English translation”, Chinese Patent Application No. 201880089255.6, (17 pages).
“First Office Action dated Mar. 14, 2022 with English translation”, Chinese Patent Application No. 201880079474.6, (11 pages).
“First Office Action dated Sep. 16, 2022 with English translation”, Chinese Patent Application No. 201980063642.7, (7 pages).
“FS_XR5G: Permanent document, v0.4.0” , Qualcomm Incorporated, 3GPP TSG-SA 4 Meeting 103 retrieved from the Internet: URL:http://www.3gpp.org/ftp/Meetings%5F3GPP%5FSYNC/SA4/Docs/S4%2Dl90526%2Ezip [retrieved on Apr. 12, 2019] , Apr. 12, 2019, (98 pages).
“International Search Report and Written Opinion dated Feb. 12, 2021”, International Application No. PCT/US20/60555, (25 pages).
“International Search Report and Written Opinion dated Mar. 12, 2020”, International PCT Patent Application No. PCT/US19/67919, (14 pages).
“International Search Report and Written Opinion dated Aug. 15, 2019”, International PCT Patent Application No. PCT/US19/33987, (20 pages).
“International Search Report and Written Opinion dated Jun. 15, 2020”, International PCT Patent Application No. PCT/US2020/017023, (13 pages).
“International Search Report and Written Opinion dated Oct. 16, 2019”, International PCT Patent Application No. PCT/US19/43097, (10 pages).
“International Search Report and Written Opinion dated Oct. 16, 2019”, International PCT Patent Application No. PCT/US19/36275, (10 pages).
“International Search Report and Written Opinion dated Oct. 16, 2019”, International PCT Patent Application No. PCT/US19/43099, (9 pages).
“International Search Report and Written Opinion dated Jun. 17, 2016”, International PCT Patent Application No. PCT/FI2016/050172, (9 pages).
“International Search Report and Written Opinion dated Feb. 2, 2021”, International PCT Patent Application No. PCT/US20/60550, (9 pages).
“International Search Report and Written Opinion dated Oct. 22, 2019”, International PCT Patent Application No. PCT/US19/43751, (9 pages).
“International Search Report and Written Opinion dated Dec. 23, 2019”, International PCT Patent Application No. PCT/US19/44953, (11 pages).
“International Search Report and Written Opinion dated May 23, 2019”, International PCT Patent Application No. PCT/US18/66514, (17 pages).
“International Search Report and Written Opinion dated Sep. 26, 2019”, International PCT Patent Application No. PCT/US19/40544, (12 pages).
“International Search Report and Written Opinion dated Aug. 27, 2019” , International PCT Application No. PCT/US2019/035245, (8 pages).
“International Search Report and Written Opinion dated Dec. 27, 2019”, International Application No. PCT/US19/47746, (16 pages).
“International Search Report and Written Opinion dated Dec. 3, 2020”, International Patent Application No. PCT/US20/43596, (25 pages).
“International Search Report and Written Opinion dated Sep. 30, 2019”, International Patent Application No. PCT/US19/40324, (7 pages).
“International Search Report and Written Opinion dated Sep. 4, 2020”, International Patent Application No. PCT/US20/31036, (13 pages).
“International Search Report and Written Opinion dated Jun. 5, 2020”, International Patent Application No. PCT/US20/19871, (9 pages).
“International Search Report and Written Opinion dated Aug. 8, 2019”, International PCT Patent Application No. PCT/US2019/034763, (8 pages).
“International Search Report and Written Opinion dated Oct. 8, 2019”, International PCT Patent Application No. PCT/US19/41151, (7 pages).
“International Search Report and Written Opinion dated Jan. 9, 2020”, International Application No. PCT/US19/55185, (10 pages).
“International Search Report and Written Opinion dated Feb. 28, 2019”, International Patent Application No. PCT/US18/64686, (8 pages).
“International Search Report and Written Opinion dated Feb. 7, 2020”, International PCT Patent Application No. PCT/US2019/061265, (11 pages).
“International Search Report and Written Opinion dated Jun. 11, 2019”, International PCT Application No. PCT/US19/22620, (7 pages).
“Invitation to Pay Additional Fees dated Aug. 15, 2019”, International PCT Patent Application No. PCT/US19/36275, (2 pages).
“Invitation to Pay Additional Fees dated Sep. 24, 2020”, International Patent Application No. PCT/US2020/043596, (3 pages).
“Invitation to Pay Additional Fees dated Oct. 22, 2019”, International PCT Patent Application No. PCT/US19/47746, (2 pages).
“Invitation to Pay Additional Fees dated Apr. 3, 2020”, International Patent Application No. PCT/US20/17023, (2 pages).
“Invitation to Pay Additional Fees dated Oct. 17, 2019”, International PCT Patent Application No. PCT/US19/44953, (2 pages).
“multi-core processor” , TechTarget, 2013 , (1 page).
“Non Final Office Action dated Nov. 19, 2019”, U.S. Appl. No. 16/355,611, (31 pages).
“Non Final Office Action dated Apr. 1, 2022”, U.S. Appl. No. 17/256,961, (65 pages).
“Non Final Office Action dated Apr. 11, 2022” , U.S. Appl. No. 16/938,782, (52 pages).
“Non Final Office Action dated Apr. 12, 2022”, U.S. Appl. No. 17/262,991, (60 pages).
“Non Final Office Action dated Aug. 21, 2019”, U.S. Appl. No. 15/564,517, (14 pages).
“Non Final Office Action dated Aug. 4, 2021”, U.S. Appl. No. 16/864,721, (21 pages).
“Non Final Office Action dated Dec. 7, 2022”, U.S. Appl. No. 17/357,795, (63 pages).
“Non Final Office Action dated Feb. 2, 2022” , U.S. Appl. No. 16/783,866, (8 pages).
“Non Final Office Action dated Jan. 24, 2023” , U.S. Appl. No. 17/497,940, (10 pages).
“Non Final Office Action dated Jan. 26, 2021”, U.S. Appl. No. 16/928,313, (33 pages).
“Non Final Office Action dated Jan. 27, 2021”, U.S. Appl. No. 16/225,961, (15 pages).
“Non Final Office Action dated Jul. 26, 2022”, U.S. Appl. No. 17/098,059, (28 pages).
“Non Final Office Action dated Jul. 27, 2020”, U.S. Appl. No. 16/435,933, (16 pages).
“Non Final Office Action dated Jul. 9, 2021”, U.S. Appl. No. 17/002,663, (43 pages).
“Non Final Office Action dated Jul. 9, 2021”, U.S. Appl. No. 16/833,093, (47 pages).
“Non Final Office Action dated Jun. 10, 2021”, U.S. Appl. No. 16/938,782, (40 Pages).
“Non Final Office Action dated Jun. 17, 2020”, U.S. Appl. No. 16/682,911, (22 pages).
“Non Final Office Action dated Jun. 19, 2020”, U.S. Appl. No. 16/225,961, (35 pages).
“Non Final Office Action dated Jun. 29, 2021”, U.S. Appl. No. 16/698,588, (58 pages).
“Non Final Office Action dated Mar. 3, 2021”, U.S. Appl. No. 16/427,337, (41 pages).
“Non Final Office Action dated Mar. 31, 2022”, U.S. Appl. No. 17/257,814, (60 pages).
“Non Final Office Action dated Mar. 9, 2022”, U.S. Appl. No. 16/870,676, (57 pages).
“Non Final Office Action dated May 10, 2022”, U.S. Appl. No. 17/140,921, (25 pages).
“Non Final Office Action dated May 17, 2022”, U.S. Appl. No. 16/748,193, (11 pages).
“Non Final Office Action dated May 26, 2021”, U.S. Appl. No. 16/214,575, (19 pages).
“Non Final Office Action dated Nov. 5, 2020”, U.S. Appl. No. 16/530,776, (45 pages).
“Non Final Office Action dated Oct. 22, 2019”, U.S. Appl. No. 15/859,277, (15 pages).
“Non Final Office Action dated Sep. 1, 2020”, U.S. Appl. No. 16/214,575, (40 pages).
“Non Final Office Action dated Sep. 19, 2022”, U.S. Appl. No. 17/263,001, (14 pages).
“Non Final Office Action dated Sep. 20, 2021”, U.S. Appl. No. 17/105,848, (56 pages).
“Non Final Office Action dated Sep. 29, 2021”, U.S. Appl. No. 16/748,193, (62 pages).
“Notice of Allowance dated Mar. 25, 2020”, U.S. Appl. No. 15/564,517, (11 pages).
“Notice of Allowance dated Oct. 5, 2020”, U.S. Appl. No. 16/682,911, (27 pages).
“Notice of Reason for Rejection dated Oct. 28, 2022 with English translation”, Japanese Patent Application No. 2020-531452, (3 pages).
“Notice of Reason of Refusal dated Sep. 11, 2020 with English translation”, Japanese Patent Application No. 2019-140435, (6 pages).
“Office Action dated Nov. 24, 2022 with English Translation”, Japanese Patent Application No. 2020-533730, (11 pages).
“Phototourism Challenge”, CVPR 2019 Image Matching Workshop. https://image matching-workshop.github.io., (16 pages).
“Second Office Action dated Jul. 13, 2022 with English Translation”, Chinese Patent Application No. 201880079474.6, (10 pages).
“Second Office Action dated Jun. 20, 2022 with English Translation”, Chinese Patent Application No. 201880089255.6, (14 pages).
“Summons to attend oral proceedings pursuant to Rule 115(1) EPC mailed on Jul. 15, 2019”, European Patent Application No. 15162521.7, (7 pages).
Aarik, J., et al., “Effect of crystal structure on optical properties of TiO2 films grown by atomic layer deposition” , Thin Solid Films; Publication [online). May 19, 1998 [retrieved Feb. 19, 2020]. Retrieved from the Internet: <URL: https://www.sciencedirect.com/science/article/pii/S0040609097001351 ?via%3Dihub>; DOI: 10.1016/S0040-6090(97)00135-1; see entire document, (2 pages).
Altwaijry, et al., “Learning to Detect and Match Keypoints with Deep Architectures” Proceedings of the British Machine Vision Conference (BMVC), BMVA Press, Sep. 2016, [retrieved on Jan. 8, 2021 (Jan. 8, 2021 )] < URL: http://www.bmva.org/bmvc/2016/papers/paper049/index.html >, en lire document, especially Abstract.
Anonymous, “Koi Pond: Top iPhone App Store Paid App”, https://web.archive.org/web/20080904061233/https://www.iphoneincanada.ca/reviews /koi-pond-top-iphone-app-store-paid-app/—[retrieved on Aug. 9, 2022].
Arandjelović, Relja, et al., “Three things everyone should know to improve object retrieval”, CVPR, 2012, (8 pages).
AZOM, “Silica—Silicon Dioxide (SiO2)” , AZO Materials; Publication [Online]. Dec. 13, 2001 [retrieved Feb. 19, 2020]. Retrieved from the Internet: <URL: https://www.azom.com/article.aspx?Article1D=1114>.
Azuma, Ronald T., “A Survey of Augmented Reality”, Presence: Teleoperators and Virtual Environments 6, 4 (Aug. 1997), 355-385; https://web.archive.org/web/20010604100006/http://www.cs.unc.edu/˜azuma/ARpresence.pdf (downloaded Oct. 26, 2020).
Azuma, Ronald T., “Predictive Tracking for Augmented Reality”, Department of Computer Science, Chapel Hill NC; TR95-007, Feb. 1995, 262 pages.
Battaglia, Peter W, et al. , “Relational inductive biases, deep learning, and graph networks”, arXiv:1806.01261, Oct. 17, 2018, pp. 1-40.
Berg, Alexander C, et al., “Shape matching and object recognition using low distortion correspondences”, In CVPR, 2005, (8 pages).
Bian, Jiawang, et al., “GMS: Grid-based motion statistics for fast, ultra-robust feature correspondence.”, In CVPR (Conference on Computer Vision and Pattern Recognition), 2017, (10 pages).
Bimber, Oliver, et al., “Spatial Augmented Reality: Merging Real and Virtual Worlds”, https://web.media.mit.edu/˜raskar/book/BimberRaskarAugmentedRealityBook.pdf; published by A K Peters/CRC Press (Jul. 31, 2005); eBook (3rd Edition, 2007) , (393 pages).
Brachmann, Eric, et al., “Neural-Guided RANSAC: Learning Where to Sample Model Hypotheses”, In ICCV (International Conference on Computer Vision ), arXiv:1905.04132v2 [cs.CV] Jul. 31, 2019 , (17 pages).
Butail, et al., “Putting the fish in the fish tank: Immersive VR for animal behavior experiments”, In: 2012 IEEE International Conference on Robotics and Automation. May 18, 2012 (May 18, 2012) Retrieved on Nov. 14, 2020 (Nov. 14, 2020) from <http:/lcdcl.umd.edu/papers/icra2012.pdf> entire document.
Caetano, Tibério S, et al., “Learning graph matching”, IEEE TPAMI, 31(6):1048-1058, 2009.
Cech, Jan, et al., “Efficient sequential correspondence selection by cosegmentation”, IEEE TPAMI, 32(9):1568-1581, Sep. 2010.
Chittineni, C., et al., “Single filters for combined image geometric manipulation and enhancement”, Proceedings of SPIE vol. 1903, Image and Video Processing, Apr. 8, 1993, San Jose, CA. (Year: 1993), pp. 111-121.
Cuturi, Marco, “Sinkhorn distances: Lightspeed computation of optimal transport”, NIPS, 2013, (9 pages).
Dai, Angela, et al., “ScanNet: Richly-annotated 3d reconstructions of indoor scenes”, In CVPR, arXiv:1702.04405v2 [cs.CV] Apr. 11, 2017, (22 pages).
Deng, Haowen, et al., “PPFnet: Global context aware local features for robust 3d point matching”, In CVPR, arXiv:1802.02669v2 [cs.CV] Mar. 1, 2018, (12 pages).
Detone, Daniel, et al., “Deep image homography estimation”, In RSS Work-shop: Limits and Potentials of Deep Learning in Robotics, arXiv:1606.03798v1 [cs.CV] Jun. 13, 2016, (6 pages).
Detone, Daniel, et al., “Self-improving visual odometry”, arXiv:1812.03245, Dec. 8, 2018, (9 pages).
Detone, Daniel, et al., “SuperPoint: Self-supervised interest point detection and description”, In CVPR Workshop on Deep Learning for Visual SLAM, arXiv:1712.07629v4 [cs.CV] Apr. 19, 2018, (13 pages).
Dusmanu, Mihai, et al., “D2-net: A trainable CNN for joint detection and description of local features”, CVPR, arXiv:1905.03561v1 [cs.CV] May 9, 2019, (16 pages).
Ebel, Patrick, et al., “Beyond cartesian representations for local descriptors”, ICCV, arXiv:1908.05547v1 [cs.CV] Aug. 15, 2019, (11 pages).
Fischler, Martin A, et al., “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography”, Communications of the ACM, 24(6): 1981, pp. 381-395.
Gilmer, Justin, et al., “Neural message passing for quantum chemistry”, In ICML, arXiv:1704.01212v2 [cs.LG] Jun. 12, 2017, (14 pages).
Giuseppe, Donato, et al., “Stereoscopic helmet mounted system for real time 3D environment reconstruction and indoor ego—motion estimation”, Proc. SPIE 6955, Head- and Helmet-Mounted Displays XIII: Design and Applications, SPIE Defense and Security Symposium, 2008, Orlando, Florida, United States, 69550P.
Goodfellow, “Titanium Dioxide—Titania (TiO2)”, AZO Materials; Publication [online]. Jan. 11, 2002 [retrieved Feb. 19, 2020]. Retrieved from the Internet: <URL: https://www.azom.com/article.aspx?Article1D=1179>.
Hartley, Richard, et al., “Multiple View Geometry in Computer Vision”, Cambridge University Press, 2003 , pp. 1-673.
Jacob, Robert J.K., “Eye Tracking in Advanced Interface Design”, Human-Computer Interaction Lab, Naval Research Laboratory, Washington, D.C., date unknown. 2003, pp. 1-50.
Lee, et al., “Self-Attention Graph Pooling”, Cornell University Library/Computer Science/Machine Learning, Apr. 17, 2019 [retrieved on Jan. 8, 2021 from the Internet< URL: https://arxiv.org/abs/1904.08082 >, entire document.
Lee, Juho, et al., “Set transformer: A frame-work for attention-based permutation-invariant neural networks”, ICML, arXiv:1810.00825v3 [cs.LG] May 26, 2019, (17 pages).
Leordeanu, Marius, et al., “A spectral technique for correspondence problems using pairwise constraints”, Proceedings of (ICCV) International Conference on Computer Vision, vol. 2, pp. 1482-1489, Oct. 2005, (8 pages).
Levola, T., “Diffractive Optics for Virtual Reality Displays”, Journal of the SID EURODISPLAY 14/05, 2005, XP008093627, chapters 2-3, Figures 2 and 10, pp. 467-475.
Levola, Tapani, “Invited Paper: Novel Diffractive Optical Components for Near to Eye Displays—Nokia Research Center”, SID 2006 DIGEST, 2006 SID International Symposium, Society for Information Display, vol. XXXVII, May 24, 2005, chapters 1-3, figures 1 and 3, pp. 64-67.
Li, Yujia, et al., “Graph matching networks for learning the similarity of graph structured objects”, ICML, arXiv:1904.12787v2 [cs.LG] May 12, 2019, (18 pages).
Li, Zhengqi, et al., “Megadepth: Learning single-view depth prediction from internet photos”, In CVPR, fromarXiv: 1804.00607v4 [cs.CV] Nov. 28, 2018, (10 pages).
Libovicky, et al., “Input Combination Strategies for Multi-Source Transformer Decoder”, Proceedings of the Third Conference on Machine Translation (WMT). vol. 1: Research Papers, Belgium, Brussels, Oct. 31-Nov. 1, 2018; retrieved on Jan. 8, 2021 (Jan. 8, 2021) from < URL: https://doi.org/10.18653/v1/W18-64026 >, entire document.
Loiola, Eliane Maria, et al., “A survey for the quadratic assignment problem”, European journal of operational research, 176(2): 2007, pp. 657-690.
Lowe, David G, “Distinctive image features from scale-invariant keypoints”, International Journal of Computer Vision, 60(2): 91-110, 2004, (28 pages).
Luo, Zixin, et al., “ContextDesc: Local descriptor augmentation with cross-modality context”, CVPR, arXiv:1904.04084v1 [cs.CV] Apr. 8, 2019, (14 pages).
Memon, F., et al., “Synthesis, Characterization and Optical Constants of Silicon Oxycarbide”, EPJ Web of Conferences; Publication [online). Mar. 23, 2017 [retrieved Feb. 19, 2020) .<URL: https://www.epj-conferences.org/articles/epjconf/pdf/2017/08/epjconf_nanop2017 _00002.pdf>; DOI: 10.1051/epjconf/201713900002, (8 pages).
Molchanov, Pavlo, et al., “Short-range FMCW monopulse radar for hand-gesture sensing”, 2015 IEEE Radar Conference (RadarCon) (2015), pp. 1491-1496.
Mrad, et al., “A framework for System Level Low Power Design Space Exploration”, 1991.
Munkres, James, “Algorithms for the assignment and transportation problems”, Journal of the Society for Industrial and Applied Mathematics, 5(1): 1957, pp. 32-38.
Ono, Yuki, et al., “LF-Net: Learning local features from images”, 32nd Conference on Neural Information Processing Systems (NIPS 2018), arXiv:1805.09662v2 [cs.CV] Nov. 22, 2018, (13 pages).
Paszke, Adam, et al., “Automatic differentiation in Pytorch”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA , (4 pages).
Péyre, Gabriel, et al., “Computational Optimal Transport”, Foundations and Trends in Machine Learning, 11(5-6):355-607, 2019; arXiv:1803.00567v4 [stat.ML] Mar. 18, 2020, (209 pages).
Qi, Charles Ruizhongtai, et al., “Pointnet++: Deep hierarchical feature learning on point sets in a metric space.”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA., Jun. 7, 2017, (10 pages).
Qi, Charles R, et al., “Pointnet: Deep Learning on Point Sets for 3D Classification and Segmentation”, CVPR, arXiv:1612.00593v2 [cs.CV] Apr. 10, 2017, (19 pages).
Radenović, Filip, et al., “Revisiting Oxford and Paris: Large-Scale Image Retrieval Benchmarking”, CVPR, arXiv:1803.11285v1 [cs.CV] Mar. 29, 2018, (10 pages).
Raguram, Rahul, et al., “A comparative analysis of ransac techniques leading to adaptive real-time random sample consensus”, Computer Vision—ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, Oct. 12-18, 2008, Proceedings, Part I, (15 pages).
Ranftl, René, et al., “Deep fundamental matrix estimation”, European Conference on Computer Vision (ECCV), 2018, (17 pages).
Revaud, Jerome, et al., “R2D2: Repeatable and Reliable Detector and Descriptor”, In NeurIPS, arXiv:1906.06195v2 [cs.CV] Jun. 17, 2019, (12 pages).
Rocco, Ignacio, et al., “Neighbourhood Consensus Networks”, 32nd Conference on Neural Information Processing Systems (NeurIPS 2018), Montréal, Canada, arXiv:1810.10510v2 [cs.CV] Nov. 29, 2018, (20 pages).
Rublee, Ethan, et al., “ORB: An efficient alternative to SIFT or SURF” , Proceedings of the IEEE International Conference on Computer Vision. 2564-2571. 2011; 10.1109/ICCV.2011.612654, (9 pages).
Sarlin, et al., “SuperGlue: Learning Feature Matching with Graph Neural Networks”, Cornell University Library/Computer Science/ Computer Vision and Pattern Recognition, Nov. 26, 2019 [retrieved on Jan. 8, 2021 from the Internet< URL: https://arxiv.org/abs/1911.11763 >, entire document.
Sattler, Torsten, et al. , “SCRAMSAC: Improving RANSAC's efficiency with a spatial consistency filter”, ICCV, 2009: 2090-2097., (8 pages).
Schonberger, Johannes Lutz, et al., “Pixelwise view selection for un-structured multi-view stereo”, Computer Vision—ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, Oct. 11-14, 2016, Proceedings, Part III, pp. 501-518, 2016.
Schonberger, Johannes Lutz, et al., “Structure-from-motion revisited”, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 4104-4113, (11 pages).
Sheng, Liu , et al., “Time-multiplexed dual-focal plane head-mounted display with a liquid lens”, Optics Letters, Optical Society of Amer I CA, US, vol. 34, No. 11, Jun. 1, 2009 (Jun. 1, 2009), XP001524475, ISSN: 0146-9592, pp. 1642-1644.
Sinkhorn, Richard, et al., “Concerning nonnegative matrices and doubly stochastic matrices.”, Pacific Journal of Mathematics, 1967, pp. 343-348.
Spencer, T., et al., “Decomposition of poly(propylene carbonate) with UV sensitive iodonium 11 salts”, Polymer Degradation and Stability; (online]. Dec. 24, 2010 (retrieved Feb. 19, 2020]. , (17 pages).
Tanriverdi, Vildan, et al., “Interacting With Eye Movements in Virtual Environments”, Department of Electrical Engineering and Computer Science, Tufts University; Proceedings of the SIGCHI conference on Human Factors in Computing Systems, Apr. 2000, pp. 1-8.
Thomee, Bart, et al., “YFCC100m: The new data in multimedia research”, Communications of the ACM, 59(2):64-73, 2016; arXiv:1503.01817v2 [cs.MM] Apr. 25, 2016, (8 pages).
Torresani, Lorenzo, et al., “Feature correspondence via graph matching: Models and global optimization”, Computer Vision—ECCV 2008, 10th European Conference on Computer Vision, Marseille, France, Oct. 12-18, 2008, Proceedings, Part II, (15 pages).
Tuytelaars, Tinne, et al., “Wide baseline stereo matching based on local, affinely invariant regions”, BMVC, 2000, pp. 1-14.
Ulyanov, Dmitry, et al., “Instance normalization: The missing ingredient for fast stylization”, arXiv:1607.08022y3 [cs.CV] Nov. 6, 2017, (6 pages).
Vaswani, Ashish, et al., “Attention is all you need”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA; arXiv:1706.03762v5 [cs.CL] Dec. 6, 2017, (15 pages).
Veli{hacek over (c)}kovi{hacek over (c)}, Petar, et al., “Graph attention networks”, ICLR, arXiv:1710.10903v3 [stat.ML] Feb. 4, 2018, (12 pages).
Mllani, Cédric, “Optimal transport: old and new”, vol. 338. Springer Science & Business Media, Jun. 2008, pp. 1-998.
Wang, Xiaolong, et al., “Non-local neural networks”, CVPR, arXiv:1711.07971v3 [cs.CV] Apr. 13, 2018, (10 pages).
Wang, Yue, et al., “Deep Closest Point: Learning representations for point cloud registration”, ICCV, arXiv:1905.03304v1 [cs.CV] May 8, 2019, (10 pages).
Wang, Yue, et al., “Dynamic Graph CNN for learning on point clouds”, ACM Transactions on Graphics, arXiv:1801.07829v2 [cs.CV] Jun. 11, 2019, (13 pages).
Weissel, et al., “Process cruise control: event-driven clock scaling for dynamic power management”, Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systems. Oct. 11, 2002 (Oct. 11, 2002) Retrieved on May 16, 2020 (May 16, 2020) from <URL: https://dl.acm.org/doi/pdf/10.1145/581630.581668>.
Yi, Kwang Moo, et al., “Learning to find good correspondences”, CVPR, arXiv: 1711.05971v2 [cs.CV] May 21, 2018, (13 pages).
Yi, Kwang Moo, et al., “Lift: Learned invariant feature transform”, ECCV, arXiv:1603.09114v2 [cs.CV] Jul. 29, 2016, (16 pages).
Zaheer, Manzil, et al., “Deep Sets”, 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA; arXiv:1703.06114v3 [cs.LG] Apr. 14, 2018, (29 pages).
Zhang, Jiahui, et al., “Learning two-view correspondences and geometry using order-aware network”, ICCV; aarXiv:1908.04964v1 [cs.CV] Aug. 14, 2019, (11 pages).
Zhang, Li, et al., “Dual graph convolutional net-work for semantic segmentation”, BMVC, 2019; arXiv:1909.06121v3 [cs.CV] Aug. 26, 2020, (18 pages).
“Communication Pursuant to Article 94(3) EPC dated Feb. 28, 2023”, European Patent Application No. 19845418.3, (6 Pages).
“Communication Pursuant to Article 94(3) EPC dated Jul. 28, 2023”, European Patent Application No. 19843487.0, (15 pages).
“Communication Pursuant to Article 94(3) EPC dated May 23, 2023”, European Patent Application No. 18890390.0, (5 pages).
“Final Office Action dated Oct. 16, 2023”, U.S. Appl. No. 17/098,043, (7 pages).
“Final Office Action dated Dec. 1, 2023”, U.S. Appl. No. 17/357,795, (18 pages).
“Final Office Action dated Sep. 8, 2023 with English translation”, Japanese Patent Application No. 2020-566620, (18 pages).
“First Examination Report dated Aug. 8, 2023”, Australian Patent Application No. 2018379105, (3 pages).
“First Office Action dated Jul. 4, 2023 with English translation”, Japanese Patent Application No. 2021-505669, (6 pages).
“First Office Action dated Jun. 13, 2023 with English translation”, Japanese Patent Application No. 2020-567853, (7 pages).
“First Office Action dated May 26, 2023 with English translation”, Japanese Patent Application No. 2021-500607, (6 pages).
“First Office Action dated May 30, 2023 with English translation”, Japanese Patent Application No. 2021-519873, (8 pages).
“First Office Action dated Sep. 29, 2023 with English translation”, Japanese Patent Application No. 2023-10887, (5 pages).
“Non Final Office Action dated Aug. 2, 2023”, U.S. Appl. No. 17/807,600, (25 pages).
“Non Final Office Action dated Jul. 20, 2023”, U.S. Appl. No. 17/650,188, (11 pages).
“Non Final Office Action dated Nov. 22, 2023”, U.S. Appl. No. 17/268,376, (8 pages).
“Non Final Office Action dated Nov. 3, 2023”, U.S. Appl. No. 17/416,248, (17 pages).
“Non Final Office Action dated Oct. 11, 2023”, U.S. Appl. No. 17/357,795, (14 pages).
“Non Final Office Action dated Oct. 24, 2023”, U.S. Appl. No. 17/259,020, (21 pages).
“Notice of Allowance dated Jul. 27, 2023 with English translation”, Korean Patent Application No. 10-2020-7019685, (4 pages).
“Office Action dated Jul. 20, 2023 with English translation”, Japanese Patent Application No. 2021-505884, (6 pages).
“Office Action dated Jun. 8, 2023 with English translation”, Japanese Patent Application No. 2021-503762, (6 pages).
“Office Action dated Nov. 7, 2023 with English translation”, Korean Patent Application No. 10-2023-7036734, (5 pages).
“Penultimate Office Action dated Oct. 19, 2023 with English translation”, Japanese Patent Application No. 2021-509779, (5 pages).
“Second Office Action dated Sep. 25, 2023 with English translation”, Japanese Patent Application No. 2020-567853, (8 pages).
“Wikipedia Dioptre”, Jun. 22, 2018 (Jun. 22, 2018), XP093066995, Retrieved from the Internet: URL:https://en.wikipedia.org/w/index.php? title=Dioptre&direction=next&oldid=846451540 [retrieved on Jul. 25, 2023], (3 pages).
“Extended European Search Report issued on Jan. 8, 2024”, European Patent Application No. 23195266.4, (8 pages).
“First Office Action mailed Dec. 12, 2023 with English translation”, Japanese Patent Application No. 2021-545712, (8 pages).
“First Office Action mailed Dec. 20, 2023 with English translation”, Chinese Patent Application No. 201980050600.X, (21 pages).
“First Office Action mailed Dec. 27, 2023 with English translation”, Chinese Patent Application No. 201980075942.7, (7 pages).
“First Office Action mailed on Dec. 11, 2023 with translation”, Chinese Patent Application No. 201980032005.3, (17 pages).
“First Office Action mailed on Dec. 25, 2023 with English translation”, Chinese Patent Application No. 2019800046303.8, (13 pages).
“Non Final Office Action mailed on Feb. 26, 2024”, U.S. Appl. No. 18/046,739, (48 pages).
“Office Action mailed on Dec. 14, 2023 with English translation”, Japanese Patent Application No. 2021-526564, (13 pages).
Related Publications (1)
Number Date Country
20230185387 A1 Jun 2023 US
Provisional Applications (2)
Number Date Country
62818032 Mar 2019 US
62714609 Aug 2018 US
Continuations (3)
Number Date Country
Parent 17518148 Nov 2021 US
Child 18165715 US
Parent 17002663 Aug 2020 US
Child 17518148 US
Parent 16523779 Jul 2019 US
Child 17002663 US