DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after allowance or after an Office action under Ex Parte Quayle, 25 USPQ 74, 453 O.G. 213 (Comm'r Pat. 1935). Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, prosecution in this application has been reopened pursuant to 37 CFR 1.114. Applicant's submission filed on 03/30/2026 has been entered.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Huston et al., U.S. Patent Number 11,449,460 B2, in view of Gomez Gonzalez et al., U.S. Patent Publication Number 20210343087 A1.
Regarding claim 1, Huston discloses a method, comprising: sending, from a server system to each of a plurality of mobile devices at a venue, instructions (col. 5, lines 27-30, “geo-referenced” means a message fixed to a particular location or object; thus, the message might be fixed to a venue location; col. 12, lines 3-7, communication instructions to facilitate communicating with one or more additional device, one or more computers and/or one or more servers; may include graphical user interface instructions), each of the mobile devices independently maintaining a corresponding internal coordinate system (col. 10, lines 6-37, mobile device includes a GPS positioning system; another position system can be provided by a separate device coupled to the mobile device, or can be provided internal to the mobile device) and the corresponding image metadata including information about a location and an orientation of the corresponding mobile device within the venue in the internal coordinate system of the corresponding mobile device when capturing the image data (col. 13, lines 27-32, as shown in figure 15, with a convention camera, photographers at points A, B and C, are photographing a Target; the metadata (EXIF, FIG. 12) gives orientation and Depth of field from each point A, B and C, i.e. the orientations and depth of field are associated with vectors from the points A, B and C to the target in FIG. 1b); receiving, at the server system from each of the plurality of mobile devices, the corresponding image data and corresponding image metadata generated in response to the instructions (col. 16, lines 65-67, server process response by providing a service (e.g., providing map information); sensor processing instructions to facilitate sensor-related processing and functions); building by the server system of the model of the venue in a real world coordinate system from the image data and image metadata from the plurality of mobile devices (col. 17, lines 63-65; the goal is to acquire as many useful images and data to build and update models of locations; the models include both 3D virtual models and images; col. 17, lines 56-59, location module may be utilized to determine location coordinates for use by an application on device and/or the content platform or image processing server; increased accuracy reduces the position error of a target, reducing computational effort and time); (col. 18, lines 50-66, feature points are extracted from a set of training images and stored; a key advantage of such a method of feature point extraction transforms an image into feature vectors invariant to image translation, scaling, and rotation; col. 16, lines 61-62, application of the device request experience and/or content data from the content platform on demand).
However, it is noted that Huston discloses messaging fixed to a venue location, but fails to disclose sending, from a server system to each of a plurality of mobile devices at a venue, instructions to generate image data and corresponding image metadata of an event at the venue to use to build a model of the venue. It is further noted that Huston discloses useful images and data to build and update models of locations and extraction transforms for an image into feature vectors, but fails to disclose specifically disclose a corresponding transformation between each of the mobile device's internal coordinate system and the real world coordinate system from the corresponding image data and image metadata of each of the mobile devices and the model of the venue in the real world coordinate system built from the image data and image metadata from the plurality of mobile devices; including generating the transformation between each of the mobile device's internal coordinate system and the real world coordinate system from the corresponding image metadata including the location and an orientation in the mobile device's internal coordinate system and the corresponding location and orientation in the real world coordinate system determined from a comparison of a set of features extracted from the image data from each of the mobile devices.
Gomez Gonzalez discloses paragraph 0160, components of a passable world may be distributed, with some portions executing locally on a XR device and some portions executing remotely, such as on a network connected server; allocation of the processing and storage of information between the local XR device and the cloud may impact functionality and user experience of an XR system. Gomez Gonzalez further discloses building by the server system of the model of a real world coordinate system from the image data and image metadata from the plurality of mobile devices, the model comprising a reference map including location data of a set reference features in the real world coordinate system (paragraph 0132, AR content may be persisted relative to a model of the physical world (e.g. a mesh); paragraph 0132, mesh model of the physical world may be created by the AR display system; data inputs are inputs such as geolocation, user identification, and current activity; paragraph 0337, map point may represent a feature of physical object that may include multiple features; uploaded to a cloud to merge with the map); generating by the server system (paragraph 0238, the server may have a map storing routine, a canonical map, a map transmitter, and a map merge algorithm) of a corresponding transformation between each of the mobile device's internal coordinate system and the real world coordinate system from the corresponding image data and image metadata of each of the mobile devices and the model of the venue in the real world coordinate system built from the image data and image metadata from the plurality of mobile devices (paragraph 0339, XR devices established relationships between their respective world coordinate system, with the canonical coordinate frame; a transformation between its world coordinate system and the coordinate system of the canonical map), including generating the transformation between each of the mobile device's internal coordinate system and the real world coordinate system from the corresponding image metadata including the location and an orientation in the mobile device's internal coordinate system and the corresponding location and orientation in the real world coordinate system determined from a comparison of a set of features extracted from the image data from each of the mobile devices (paragraph 0340-0341, a transformation can be computed between a local PCF on the respective device to a respective persistent pose on the canonical map; with these transformations, each device may use its local PCFs, which can be detected locally on the device by processing images detected with sensors on the device, to determine where with respect to the local device); and transmitting from the server system to each of the mobile devices the corresponding transformation between the mobile device's internal coordinate system and the real world coordinate system (paragraph 0243, the map transmitter may transmit the canonical map together with the persistent position and/or the PCTs to the second XR device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the crowd source venue sharing as disclosed by Huston, building transformations with respect to each device coordinate system on a server as disclosed by Gomez Gonzalez, to improve allocation of the processing and storage of information between the local XR device and the cloud functionality and user experience of an XR system in a crowd sourced venue, to enable users to share an experience as disclosed by both Huston and Gomez Gonzalez.
Regarding claim 2, Huston discloses further comprising: retrieving point features of the venue in a first coordinate system and locations of a set of fiducials features for the venue in the real world coordinate system by the server system from one or more databases (col. 5, lines message might be fixed to a venue location, e.g., a golf course fence; an object is typically geo-referenced using either a position technology; can be geo-reference using machine vision; if machine vision is used (i.e., object recognition), applications can be “markerless” or use “markers,” sometimes known as fiducials; marker-based often uses a square marker with high contrast), wherein the location of each of the fiducial features is at corresponding point of the venue in the real world coordinate system as determined in a survey of the venue, and wherein the server system further builds the model of the venue in the real world coordinate system from the point features of the venue in the first coordinate system and the locations of the set of fiducials features for the venue in the real world coordinate system (col. 18, lines 60-66, feature points are extracted from a set of training images and stored; a key advantage of such a method of feature point extraction transforms an image into feature vectors invariant to image translation, scaling, and rotation).
Regarding claim 3, Huston discloses further comprising: building the model of the venue in the real world coordinate system from the point features of the venue in the first coordinate system and the locations of the set of fiducials features for the venue in the real world coordinate system prior to the event (col. 22, lines 25-29, capture of images and the building of a 3D model of events occurring in the wedding chapel; col. 22, lines 37-44, the wedding chapel has been scanned in advance of any event with a composite scanning system); and during the event, revising the model of the venue in the real world coordinate system using the image data of the venue for the event and image metadata as received from the plurality of mobile devices during the event (col. 22, lines 45-52, during the event, i.e., the wedding, the camera systems additionally capture images (and audio) of the event; one or more wedding guests are accompanied with a mobile device 10, 12 or 220, to capture images and audio from the event and wirelessly convey the information to the network; the information captured in real-time during the event are processed at the server and update the databases).
Regarding claim 4, Huston discloses wherein building the model of the venue in the real world coordinate system includes: building the model of the venue in the real world coordinate system from the point features of the venue in the first coordinate system, the locations of the set of fiducials features for the venue in the real world coordinate system, and the image data of the venue for the event and image metadata as received from the plurality of mobile devices prior to the event (col. 17, lines 16-20, crowdsourced random images and metadata can be used to update the images stored in a database; for example, a newly acquired image from a mobile device can be matched to the corresponding image in a database; col. 17, lines 63-65, the goal is to acquire as many useful images and data to build and update models of locations; col. 18, lines 51-61, image alignment and stitching; uses an feature point detection and matching algorithm; using SIFT, feature points are extracted from a set of training images and stored).
Regarding claim 5, Huston discloses wherein generating the corresponding transformation between each mobile device's internal coordinate system and the real world coordinate system includes: determining a transformation between the image data from the mobile device and the point features of the venue from the image data and image metadata from the mobile device and from the point features of the venue; locating the set of fiducials features in the real world coordinate system within the image data from the mobile device; and determining, from the transformation between the image data from the mobile device and the point features of the venue and from locating of features in the real world coordinate system within the image data from the mobile device, a pose for the image from the mobile device in the real world coordinate system.
Regarding claim 6, Huston discloses wherein building the model of the venue in the real world coordinate system includes: processing the image data and image metadata from the plurality of mobile devices to generate point features of the venue and the locations of a set of fiducials features in the world coordinate system, wherein the transformation between each of the mobile device's internal coordinate system and the real world coordinate system is generated by comparing the image data and image metadata from the mobile device with the point features of the venue (col. 17, lines 16-20, crowdsourced random images and metadata can be used to update the images stored in a database; for example, a newly acquired image from a mobile device can be matched to the corresponding image in a database; col. 18, lines 30-33, aligning targets in multiple images; col. 17, lines 63-65, the goal is to acquire as many useful images and data to build and update models of locations; col. 18, lines 51-61, image alignment and stitching).
(Huston – col. 22, lines 45-58, during the event i.e., the wedding, the camera systems additionally capture images (and audio) of the event; further, one or more wedding guests are accompanied with a mobile device, to capture images and audio from the event and wirelessly convey the information to the network; the information captured in real-time during the event are processed at server and update the databases; )
Regarding claim 7, Huston discloses wherein building the model of the venue in the real world coordinate system further includes: extracting one or more of the set of fiducial features from the image data and image metadata from the plurality of mobile devices (col. 18, lines 60-66, feature points are extracted from a set of training image and stored; a key advantage of such a method of feature point extraction transforms an image into feature vectors invariant to image translation, scaling, and rotation, and partially invariant).
Regarding claim 8, Huston discloses further comprising: receiving, at the server system for one or more of the mobile devices, an independent request for graphics to be displayed by the mobile device over a view of the venue as specified by location and orientation in the real world coordinate system; and transmitting from the server system to each of the mobile devices the requested graphics (col. 16, lines 61-62, application of the device request experience and/or content data from the content platform on demand; col. 6, lines 57-67, the venue for an event or “experience" can be a real view or depicted as a photo background environment or a virtual environment, or a mixture, sometimes referred to as “mixed reality”; “augmented reality” images overlaid the event venue background).
Regarding claims 9-17, they are rejected based upon similar rational as above claims 1-8, Huston further discloses a system, comprising: one or more servers configured to access data from one or more databases, to receive data from and transmit data to a plurality of mobile devices, and to: send, to each of a plurality of mobile devices at a venue (figure 3; col. 23, lines 59-65, uses the crowd-sourced content of the event to display in near real-time a panoramic/3D rendering; the images, sounds and 3D model are available to subscribers/user from the experience platform by using the application).
Regarding claim 18, Huston discloses wherein: the image data from the plurality of mobile devices comprise crowd sourced images that are used to achieve a cumulative result in a form of the model, wherein identity and/or number of the plurality of mobile devices are not known in advance prior to the event at the venue (col. 17, lines 16-20, crowdsourced random images and metadata can be used to update the images stored in a database; for example, a newly acquired image from a mobile device can be matched to the corresponding image in a database; col. 18, lines 30-33, aligning targets in multiple images; col. 17, lines 63-65, the goal is to acquire as many useful images and data to build and update models of locations; col. 18, lines 51-61, image alignment and stitching).
Regarding claim 19, it is rejected based upon similar rational as above claim 1. Huston further discloses a method, comprising: independently maintaining by each of a plurality of mobile devices at a venue a corresponding internal coordinate system (col. 10, lines 6-37, mobile device includes a GPS positioning system; another position system can be provided by a separate device coupled to the mobile device, or can be provided internal to the mobile device); the corresponding image metadata including information about a location and an orientation of the corresponding mobile device within the venue in the internal coordinate system of the corresponding mobile device when capturing the image data (col. 13, lines 27-32, as shown in figure 15, with a convention camera, photographers at points A, B and C, are photographing a Target; the metadata (EXIF, FIG. 12) gives orientation and Depth of field from each point A, B and C, i.e. the orientations and depth of field are associated with vectors from the points A, B and C to the target in FIG. 1b); receiving, at the server system from each of the plurality of mobile devices, the corresponding image data and corresponding image metadata generated in response to the instructions (col. 13, lines 27-32, as shown in figure 15, with a convention camera, photographers at points A, B and C, are photographing a Target; the metadata (EXIF, FIG. 12) gives orientation and Depth of field from each point A, B and C, i.e. the orientations and depth of field are associated with vectors from the points A, B and C to the target in FIG. 1b); building by the server system of the model of the venue in a real world coordinate system from the image data and image metadata from the plurality of mobile devices, the model comprising a reference map including location data of a set reference features in the real world coordinate system (col. 17, lines 63-65; the goal is to acquire as many useful images and data to build and update models of locations; the models include both 3D virtual models and images; col. 17, lines 56-59, location module may be utilized to determine location coordinates for use by an application on device and/or the content platform or image processing server; increased accuracy reduces the position error of a target, reducing computational effort and time).
However, it is noted that Huston discloses messaging fixed to a venue location, but fails to disclose sending, from a server system to each of a plurality of mobile devices at a venue, instructions to generate image data and corresponding image metadata of an event at the venue to use to build a model of the venue. It is further noted that Huston discloses useful images and data to build and update models of locations and extraction transforms for an image into feature vectors, but fails to disclose specifically disclose a corresponding transformation between each of the mobile device's internal coordinate system and the real world coordinate system from the corresponding image data and image metadata of each of the mobile devices and the model of the venue in the real world coordinate system built from the image data and image metadata from the plurality of mobile devices.
Gomez Gonzalez discloses paragraph 0160, components of a passable world may be distributed, with some portions executing locally on a XR device and some portions executing remotely, such as on a network connected server; allocation of the processing and storage of information between the local XR device and the cloud may impact functionality and user experience of an XR system. Gomez Gonzalez further discloses building by the server system of the model of a real world coordinate system from the image data and image metadata from the plurality of mobile devices, the model comprising a reference map including location data of a set reference features in the real world coordinate system (paragraph 0132, AR content may be persisted relative to a model of the physical world (e.g. a mesh); paragraph 0132, mesh model of the physical world may be created by the AR display system; data inputs are inputs such as geolocation, user identification, and current activity; paragraph 0337, map point may represent a feature of physical object that may include multiple features; uploaded to a cloud to merge with the map); generating by the server system (paragraph 0238, the server may have a map storing routine, a canonical map, a map transmitter, and a map merge algorithm) of a corresponding transformation between each of the mobile device's internal coordinate system and the real world coordinate system from the corresponding image data and image metadata of each of the mobile devices and the model of the venue in the real world coordinate system built from the image data and image metadata from the plurality of mobile devices (paragraph 0339, XR devices established relationships between their respective world coordinate system, with the canonical coordinate frame; a transformation between its world coordinate system and the coordinate system of the canonical map), including generating the transformation between each of the mobile device's internal coordinate system and the real world coordinate system from the corresponding image metadata including the location and an orientation in the mobile device's internal coordinate system and the corresponding location and orientation in the real world coordinate system determined from a comparison of a set of features extracted from the image data from each of the mobile devices (paragraph 0340-0341, a transformation can be computed between a local PCF on the respective device to a respective persistent pose on the canonical map; with these transformations, each device may use its local PCFs, which can be detected locally on the device by processing images detected with sensors on the device, to determine where with respect to the local device); and transmitting from the server system to each of the mobile devices the corresponding transformation between the mobile device's internal coordinate system and the real world coordinate system (paragraph 0243, the map transmitter may transmit the canonical map together with the persistent position and/or the PCTs to the second XR device).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to include in the crowd source venue sharing as disclosed by Huston, building transformations with respect to each device coordinate system on a server as disclosed by Gomez Gonzalez, to improve allocation of the processing and storage of information between the local XR device and the cloud functionality and user experience of an XR system in a crowd sourced venue, to enable users to share an experience as disclosed by both Huston and Gomez Gonzalez.
Regarding claim 20, Huston discloses further comprising: receiving point features of the venue and locations of a set of fiducials features for the venue in the real world coordinate system by the server system from one or more databases, wherein building the model of the venue in the real world coordinate system includes: building the model of the venue in the real world coordinate system from the point features of the venue, the locations of the set of fiducials features for the venue in the real world coordinate system, and the image data of the venue for the event and image metadata as received from the plurality of mobile devices prior to the event (col. 5, lines message might be fixed to a venue location, e.g., a golf course fence; an object is typically geo-referenced using either a position technology; can be geo-reference using machine vision; if machine vision is used (i.e., object recognition), applications can be “markerless” or use “markers,” sometimes known as fiducials; marker-based often uses a square marker with high contrast; col. 18, lines 60-66, feature points are extracted from a set of training images and stored; a key advantage of such a method of feature point extraction transforms an image into feature vectors invariant to image translation, scaling, and rotation; col. 10, lines 6-37, mobile device includes a GPS positioning system; another position system can be provided by a separate device coupled to the mobile device, or can be provided internal to the mobile device).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jiang et al., U.S. Patent Publication Number 2017/0076499 A1
Jiang discloses paragraph 0030, on-the-fly map generation module 110, a static/dynamic map separation module 120, a localization module 130, a static and dynamic map update module 140, and an AR and user interaction module 150. The remaining modules—crowd map and camera registration module 160, crowd static/dynamic map refinement module 170, bundled static/dynamic map update and camera pose refinement module 180, and crowd AR and user interaction module 190—permit the mobile cameras C.sub.1 and C.sub.2 to share their inserted AR objects. Jiang discloses receiving input from the user or device as to the location (paragraph 0033); extracting a set of two-dimensional (2D) key points from a plurality of image frames (paragraph 0034); Scale-Invariant Feature Transform (SIFT) descriptor; matching key points (paragraphs 0035- 0037).
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MOTILEWA . GOOD JOHNSON
Primary Examiner
Art Unit 2616
/MOTILEWA GOOD-JOHNSON/Primary Examiner, Art Unit 2619
Prosecution Insights