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 .
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.
Claims 1-8 and 10-20 are rejected under 35 U.S.C. 103 as being unpatentable over Jovanovic, et al. (US 20190051054 A1) in view of Davies (US 20140118339 A1) and AfterAcademy Tech (AfterAcademy Tech “Find the most frequent element in an array - Interview Problem” 2019 AfterAcademy).
Regarding claim 1, Jovanovic teaches A computer-implemented method for estimating a room axis, comprising:
receiving, at a mobile computing device, an input command to initiate capturing visual documentation of an interior environment; (spec [0033]; “(ii) a screen configured to allow a user of the electronic device to interact with the AR application,”)
capturing, with a camera of the mobile computing device (spec [0033]; “the electronic device comprises (i) a camera that captures a sequence of images from a sequence of poses of an indoor or outdoor space,”) in communication with an augmented reality (AR) engine, a plurality of images of the interior environment, (spec [0033]; “(iii) an augmented reality (AR) engine configured to generate a 3D model of the space with less user time commitment (2 to 5 minutes).”) wherein the interior environment comprises one or more walls (spec [0033]; “In some embodiments, measurements and renderings of the floor space, walls, ceiling, features or objects, and/or the 3D model of the space are exported to the cloud back-end,”) and a plurality of objects, (spec [0033]; “In some embodiments, objects within the space (e.g., windows, vents, doors, counters, appliances, islands, or other fixtures) are measured and included in the 3D model.”) each wall and object having a surface oriented along a corresponding plane; (spec [0053]; “Non-limiting examples of objects comprise cabinets, counters, island counters, permanent fixtures, doors, windows, wall openings, electrical outlets, vents, ducts, appliances, and damage in the space. In some embodiments, the objects are objects in the floor plane, wall plane and/or ceiling plane.”)
However, Jovanovic does not teach determining, based on information received from the AR engine, local axis orientations for at least a portion of the walls and objects;
Davies teaches determining, based on information received from the AR engine, local axis orientations for at least a portion of the walls and objects; (spec [0039]; “Referring again to FIG. 2, the tracking system 202 may determine a frame of reference 206 for a real object. In other words, the tracking system 202 may track the real object.”)
It would be obvious for a person having ordinary skill in the art to take Jovanovic’s electronic device and then combine it with Davies’ tracking system to detect local axis orientations. The person possessing ordinary skill in the art would be motivated to measure individual objects/walls in a more convenient way.
However, Jovanovic in view of Davies does not teach estimating a room axis of the interior environment based on a voting process, wherein a majority of matching local axis orientations are utilized to estimate the room axis; and
outputting an indication of the room axis.
AfterAcademy Tech teaches estimating a room axis of the interior environment based on a voting process, wherein a majority of matching local axis orientations are utilized to estimate the room axis; and (Using Hash Table, Solution Steps; “Create a Hash Table to store frequency of each [local axis orientation] in the given array. Consider [local axis orientation] in the array as key and their frequency [(e.g. their votes)] as value”)
outputting an indication of the room axis. (Using Hash Table, Solution Steps; “And finally, return the [local axis orientation with the most votes as the room axis] i.e. return ans”)
It would be obvious for a person having ordinary skill in the art to take Jovanovic’s electronic device, combine it with Davies’ tracking system to detect local axis orientations, and then use a AfterAcademy Tech’s hash table, storing the local axis orientations as keys and the votes as the value and then returning the local axis orientation with the highest vote count as the room axis. The person possessing ordinary skill in the art would be motivated to create a digital representation of a room so to more efficiently measure a room’s dimensions and reduce costs for construction.
Regarding claim 2, Jovanovic teaches The method of claim 1, further comprising outputting annotations corresponding to the local axis orientations. (spec [0032]; “The present approach demonstrates the ability to generate fully annotated 3D models of a space using a smartphone that is at least as accurate as prior technologies based on advanced sensors,”)
Regarding claim 3, Jovanovic teaches The method of claim 2, wherein outputting at least a portion of the annotations is performed during the capturing to provide feedback to a user. (spec [0049]; “the annotations are derived from computer vision and machine learning algorithms acting on a live camera data to label objects, fixtures, appliances, and the like in the space automatically.”)
Regarding claim 4, Jovanovic teaches The method of claim 2, wherein the annotations align with the local axis orientations and correspond to locations and angles where the objects are physically placed in the interior environment. (spec [0083] and figs 11-14; “Upon indicating the placement of each vertical object and feature within the space, the AR interface calculates and displays the dimensions of each object and feature by reference to the fixed coordinate system. Also, upon indicating the placement of each vertical object and feature within the space, the user manually tags each object and feature with an identifying annotation.”)
Regarding claim 5, Jovanovic teaches The method of claim 2, wherein the annotations align with the local axis orientations and correspond to locations and angles where physical walls meet a physical floor. (spec [0044] and figs 2-9; “in real-time and using touch gestures and/or voice commands to initiate and complete segmental measurements around the perimeter of the floor of the space to generate a defined floorplan of the space. A floorplan is generated when the user indicates positions of corners of the floor space via the AR interface in reference to the fixed coordinate system.”)
Regarding claim 6, Jovanovic teaches The method of claim 1, wherein the plurality of images are captured as a user moves to different locations in the interior environment. (spec [0036]; “In some embodiments, the active reticle scales with distance as it tracks the user's movement in the 3D environment via the AR interface.”)
Regarding claim 7, Jovanovic teaches The method of claim 1, wherein the visual documentation comprises a three-dimensional (3D) mapping of the interior environment. (spec [0032]; “Described herein are devices, methods, systems, and media for capturing floorplans, vertical measurements floor-to-ceiling height, measurement of objects such as doors/windows, and generation of 3D interior models within smartphones with a 2-5 minute user time commitment.”)
Regarding claim 8, Jovanovic teaches The method of claim 1, further comprising capturing dimensions of the interior environment based on the estimated room axis. (specs [0044] and [0047]; “An AR interface is configured to allow the user to view the space in a 3D environment, in real-time and using touch gestures and/or voice commands to initiate and complete segmental measurements around the perimeter of the floor of the space to generate a defined floorplan of the space.” and “Ceiling height measurements of a space are generated by the augmented reality (AR) application disclosed herein.”)
Regarding claim 10, Jovanovic teaches A system for capturing spatial documentation of an environment, comprising:
a mobile computing device, comprising: (spec [0035]; “In some embodiments, the augmented reality (AR) application runs entirely locally, e.g., at the mobile device.”)
a camera configured to capture video; (specs [0033] and [0048]; “the electronic device comprises (i) a camera that captures a sequence of images from a sequence of poses of an indoor or outdoor space,” and “In some embodiments, images and/or videos are captured throughout the user's AR session.”)
one or more processors in communication with the camera; (spec [0076]; “In some instances, the system comprises a central processing unit (CPU),”)
an augmented reality (AR) engine in communication with the one or more processors; (spec [0033]; “(iii) an augmented reality (AR) engine configured to generate a 3D model of the space with less user time commitment (2 to 5 minutes).”)
a first memory configured for storing captured video; (fig. 29, 220; “Cloud Portal”)
a second memory storing computer code that causes the one or more processors to: (spec [0076]; “In some embodiments, the system comprises a storage unit to store data and information regarding any aspect of the methods described in this disclosure.”)
The rest of the limitations of claim 10 are recitations of limitations of claim 1, and are rejected using the rationale of claim 1.
Regarding claim 11, it recites the limitations of claim 2, but it depends on claim 10, and is rejected using the same rationale as claim 2.
Regarding claim 12, it recites the limitations of claim 3, but it depends on claim 10, and is rejected using the same rationale as claim 3.
Regarding claim 13, it recites the limitations of claim 4, but it depends on claim 10, and is rejected using the same rationale as claim 4.
Regarding claim 14, it recites the limitations of claim 5, but it depends on claim 10, and is rejected using the same rationale as claim 5.
Regarding claim 15, it recites the limitations of claim 6, but it depends on claim 10, and is rejected using the same rationale as claim 6.
Regarding claim 16, it recites the limitations of claim 7, but it depends on claim 10, and is rejected using the same rationale as claim 7.
Regarding claim 17, it recites the limitations of claim 8, but it depends on claim 10, and is rejected using the same rationale as claim 8.
Regarding claim 18, it recites the limitations of claim 10, but in non-transitory computer-readable medium form, and is rejected using the rationale of claim 10.
Regarding claim 19, it recites the limitations of claims 2-3 and 6, and groups them into a single limitation, but dependent on claim 18, and is rejected using the rationales of claims 2-3 and 6
Regarding claim 20, it recites the limitations of claims 4-5, and groups them into a single limitation, but dependent on claim 18, and is rejected using the rationales of claims 4-5.
Claim 9 is rejected under 35 U.S.C. 103 as being unpatentable over Jovanovic, et al. (US 20190051054 A1) in view of Davies (US 20140118339 A1), AfterAcademy Tech (AfterAcademy Tech "Find the most frequent element in an array - Interview Problem" 2019 AfterAcademy) and Tiramani (US 20060059792 A1).
Regarding claim 9, Jovanovic in view of Davies and AfterAcademy Tech discloses The method of claim 1,
However, Jovanovic in view of Davies and AfterAcademy Tech does not disclose wherein the interior environment comprises one or more of a kitchen, a living room, a utility room, an office, a bedroom, a bathroom, and a garage.
Tiramani discloses wherein the interior environment comprises one or more of a kitchen, a living room, a utility room, an office, a bedroom, a bathroom, and a garage. (Tiramani; spec [0061]; “The core modules 11 are a series of connectable modules 10 which are, generally, indoor rooms such as, but not limited to, bedrooms, bathrooms, recreation rooms, study, living rooms, dining rooms, play rooms, libraries, kitchens, laundry rooms, single garages, double garages, triple garages, great rooms, artist's studios, offices, and storage rooms.”)
It would be obvious for a person having ordinary skill in the art to use the Tiramani’s modules for the interior environment for Jovanovic’s electrical device combined with Davies’ tracking system that uses a hash table that AfterAcademy Tech discloses to get the room axis. A person possessing ordinary skill in the art would be motivated to accurately measures a different variety of rooms.
Conclusion
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/IRVING NMN SHI/Examiner, Art Unit 2611
/TAMMY PAIGE GODDARD/Supervisory Patent Examiner, Art Unit 2611
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