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 .
Response to Amendment
Applicant’s amendment filed on 08/06/2026 has been entered and made of record.
Status of Claims
Claims 1-20 are pending in this application.
Response to Arguments
This office action is responsive to Applicant’s arguments/remarks made in an amendment received 08/06/2026.
Applicant’s amendments to the Specification and Claims overcome the objections to the Specification and Claims previously set forth in the Non-Final Office Action mailed 05/14/2026 and are therefore withdrawn.
Applicant's arguments regarding rejections under 35 U.S.C 103 have been fully considered but they are not persuasive. Applicant argues, in summary, that Zhang and Lee do not, together or individually, teach “comparing the predicted pose generated by the VPS model to the ostensible pose data.” The Examiner respectfully disagrees.
On page 10, the Applicant argues:
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In response to Applicant’s argument, the Examiner directs the Applicant to Lee, Pg. 9 on which Table VI demonstrates the rotational performance comparison of the proposed semantic-based VPS and smartphone IMU. For example, in table VI, loc. 1.1, the VPS model had a yaw deviation of -4° and the smartphone IMU had a yaw deviation of -27°. This table demonstrates comparing pose information predicted, or estimated, by the proposed VPS model to pose information received by a smartphone, or a client device.
The Examiner also directs Applicant to Lee, Pg. 6, right col., section 3.1.1, lines 8-10, which states “𝐱̂ is the estimated candidate pose with the highest likelihood. The chosen candidate pose stores the latitude, longitude, altitude, yaw, pitch, and roll.” Section 3.1.1 describes the approach of the VPS model to predict, or estimate, the pose data given a candidate’s likelihood. The candidate with the maximum likelihood is then chosen as the candidate pose, which in turn is what the VPS model yields in terms of pose data.
On page 11, the Applicant argues:
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Applicant’s argument is persuasive in that Zhang does not teach the “comparing” portion. However, the argument does not overcome the rejection because the Examiner relies on Lee to teach “comparing the predicted pose generated by the VPS model to the ostensible pose data”, which is a reference already applied in the rejection of the claim.
The additional citations from Lee do not introduce a new reference but rather identify further disclosure in the same reference that teaches the claimed limitation of “comparing the predicted pose generated by the VPS model to the ostensible pose data”. Therefore, the rejections under 35 U.S.C 103 for claims 1-20 are maintained.
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, 2, 5, 8-12, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 2021/0023452 A1, hereafter "Zhang") in view of Lee et al. (“Semantic 3D Map Change Detection and Update based on Smartphone Visual Positioning System”, 2021, hereafter, “Lee”).
Regarding claim 1, Zhang discloses a method comprising receiving, by an online system, ostensible pose data, wherein the ostensible pose data is data that ostensibly describes a pose of a client device (See Zhang, ¶ [0077], game server 120 at 606 receives the client device 120 location information, game server 120 needs to verify the location information accurately corresponds to the physical location of the client device 120. Examiner considers client device location information needing verification as “ostensible pose data”);
receiving ostensible image data, wherein the ostensible image data ostensibly describes an image captured by the client device while the client device is at the pose described by the ostensible pose data (See Zhang, ¶ [0071], The player captures image data of the landmark 510 with the client device 120. Examiner considers captured image data as “ostensible image data”);
determining whether the ostensible pose data and the ostensible image data are valid (See Zhang, ¶ [0077], verify the location information accurately corresponds to the physical location of the client device 120; ¶ [0079], verify that image data captured by the client device 120 positively matches the landmark); by determining whether the ostensible pose data and the ostensible image data match (See Zhang, Fig. 5, [0073], image data of the landmark 510 at the expected position 530, the captured image data would be parallel to an expected perspective plane 535) by:
[generating a predicted pose corresponding to the ostensible image data by applying a visual positioning system (VPS) model to the ostensible image data, wherein the VPS model is a model that maps an image captured by a client device to possible poses that could correspond to that image; and]
[comparing the predicted pose generated by the VPS model to the ostensible pose data; and]
[responsive to determining that the ostensible pose data and the ostensible image data are not valid,] performing a disciplinary action with regards to the client device (See Zhang, ¶ [0074], reject the client device 120 location. Examiner considers reject the client device location as “disciplinary action”).
However, Zhang fails to teach generating a predicted pose corresponding to the ostensible image data by applying a VPS model to the ostensible image data, wherein the VPS model is a model that maps an image captured by a client device to possible poses that could correspond to that image;
comparing the predicted pose generated by the VPS model to the ostensible pose data; and
responsive to determining that the ostensible pose data and the ostensible image data are not valid, [performing a disciplinary action with regards to the client device.]
Lee, working in the same field of endeavor, teaches generating a predicted pose (See Lee, Pg. 6, right col., section 3.11, lines 8-10, d 𝐱̂ is the estimated candidate pose with the highest likelihood. The chosen candidate pose stores the latitude, longitude, altitude, yaw, pitch, and roll. Examiner considers the estimated candidate pose to correspond to the predicted pose) corresponding to the ostensible image data (See Lee, Pg. 5, right col., section 3.9, lines 1-3, the candidate images are compared to the smartphone image. The matching algorithm calculates the score of each candidate image. Examiner considers the ostensible image data to be the smartphone image) by applying a visual positioning system (VPS) model to the ostensible image data (See Lee, Pg. 2, left col., par. 3, visual positioning system (VPS) identifies edges within the smartphone image and matches with edges captured from pre-surveyed images, pose-tagged edges are stored in a searchable index), wherein the VPS model is a model that maps an image captured by a client device to possible poses that could correspond to that image (See Lee, Pg. 3, left col., section 2, par. 3, The segmented smartphone image is de-rectified (Sect. 3.7) and matched with the candidate images using multiple metrics to calculate the similarity scores (Sect. 3.9). The scores of each method are combined to calculate the likelihood of each candidate. (Sect. 3.10). The chosen pose is determined by the candidate with the maximum likelihood among all the candidates (Sect. 3.11). The smartphone image, or image captured by a client device, is used to generate a likelihood of all the candidates and to determine the corresponding pose of the image out of all possible candidate images) and;
comparing (See Lee, Pg. 9, TABLE IV. Rotational performance comparison of the proposed semantic-based VPS and smartphone IMU) the predicted pose generated by the VPS model (See Lee, Pg. 6, right col., section 3.1.1, lines 8-10, d 𝐱̂ is the estimated candidate pose with the highest likelihood. The chosen candidate pose stores the latitude, longitude, altitude, yaw, pitch, and roll. Examiner considers the estimated candidate pose to correspond to the predicted pose) to the ostensible pose data (See Lee, Pg. 4, right col., section 3.6, lines 1-4, Candidate poses are distributed around the initial estimated pose. The initial rough estimation of the pose is calculated by the smartphone GNSS receiver and IMU when capturing an image with the smartphone. Examiner considers the initial estimated pose as the ostensible pose data) and
responsive to determining that the ostensible pose data and the ostensible image data are not valid (See Lee, Pg. 8. right col., par. 3, there should be no discrepancies between the smartphone image and the candidate image at ground truth. Examiner considers ground truth as a pose data), [performing a disciplinary action with regards to the client device.]
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference to generate a predicted pose corresponding to the ostensible image data by applying a visual positioning system (VPS) model to the ostensible image data, compare the predicted pose generated by the VPS model to the ostensible image data, and be responsive to determining ostensible pose data and the ostensible image data are not valid based on the method of Lee’s reference. The suggestion/motivation would have been for accurate and robust pose estimation and to match the segmented generated images with the segmented smartphone images as suggested by Lee in the Abstract.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with Zhang to obtain the invention as specified in claim 1.
Regarding claim 2, Zhang in view of Lee discloses the method of claim 1, wherein the pose data comprises (global navigation satellite system (GNSS) data, magnetometer data, or inertial measurement unit (IMU) data (See Zhang, ¶ [0037], The client 120 may also include other various sensors for recording data from the client 120 including but not limited to movement sensors, accelerometers, gyroscopes, other inertial measurement units (IMUs)).
Regarding claim 3, Zhang in view of Lee discloses the method of claim 1, further comprising: transmitting an instruction to the client device to collect pose data and image data over a period of time (See Zhang, ¶ [0037], client 120 can be configured to periodically send player input and other updates to the game server).
Regarding claim 4, Zhang in view of Lee discloses the method of claim 3, wherein the received ostensible pose data and the received ostensible image data consists of pose data and image data from a set of timestamps within the period of time (See Zhang, ¶ [0023], Consequently, at various times (e.g., when a player requests access to access controlled features, periodically during gameplay, when the player is within proximity of certain location, etc.) additional steps may be taken to verify that the player with their client device is in fact at the corresponding real world location).
Regarding claim 5, Zhang in view of Lee discloses the method of claim 4, further comprising: randomly selecting the set of timestamps within the period of time (See Zhang, ¶ [0079], the game server 110 generates a set of verification instructions with one or more verification instructions generated randomly).
Regarding claim 8, Zhang discloses the method of claim 1, wherein determining whether the ostensible pose data and the ostensible image data match (See Zhang, Fig. 5, [0073], image data of the landmark 510 at the expected position 530, the captured image data would be parallel to an expected perspective plane 535) comprises:
[generating a set of candidate poses based on the ostensible image data and the VPS model;]
and comparing (See Zhang, ¶ [0078], client device's 120 location compared to locations of all stored landmarks) [the set of candidate poses to the pose described by the ostensible pose data.]
However, Zhang fails to teach generating a set of candidate poses based on the ostensible image data and the VPS model;
and [comparing] the set of candidate poses to the pose described by the ostensible pose data.
Lee, working in the same field of endeavor, teaches generating a set of candidate poses based on the ostensible image data and the VPS model (See Lee, Fig.1 Flowchart of proposed semantic-based VPS based on segmented smartphone images and segmented generated images; Eqn. (3). Examiner considers Eqn. (3) as is shown in Fig. 1 to “generating a set of candidate poses”);
and [comparing] the set of candidate poses to the pose described by the ostensible pose data (See Lee, Pg. 4, right col., par. 2, Candidate poses are distributed around the initial estimated pose.)
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference for generating a set of candidate poses based on the ostensible image data and the VPS model and comparing the set of candidate poses to the pose described by the ostensible pose data based on the method of Lee’s reference. The suggestion/motivation would have been for accurate and robust pose estimation as suggested by Lee in the Abstract.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with Zhang to obtain the invention as specified in claim 8.
Regarding claim 9, Zhang discloses the method of claim 8, wherein comparing (See Zhang, ¶ [0078], client device's 120 location compared to locations of all stored landmarks) [the set of candidate poses to the pose of the ostensible pose data comprises:]
computing a difference (See Zhang, ¶ [0080], the landmark recognition model may calculate relative distances between identified points. Examiner considers the relative distances as “a difference) [between each candidate pose and the pose of the ostensible pose data;]
and responsive to the difference exceeding a threshold (See Zhang, ¶ [0094], above a threshold amount), determining that the ostensible pose data and the ostensible image data do not match (See Zhang, ¶ [0080], landmark recognition model may determine the image data to not match the appropriate landmark).
However, Zhang fails to teach [comparing] the set of candidate poses to the pose of the ostensible pose data comprises:
[computing a difference] between each candidate pose and the pose of the ostensible pose data.
Lee, working in the same field of endeavor, teaches [comparing] the set of candidate poses to the pose of the ostensible pose data (See Lee, Pg. 4, right col., par. 2, Candidate poses are distributed around the initial estimated pose) comprises:
[computing a difference] between each candidate pose and the pose of the ostensible pose data (See Lee, Pg. 4, right col., par. 2, Candidate poses are distributed around the initial estimated pose).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference to compare between the set of candidate poses and the pose of the ostensible pose data based on the method of Lee’s reference. The suggestion/motivation would have been for accurate and robust pose estimation as suggested by Lee in the Abstract.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with Zhang to obtain the invention as specified in claim 9.
Regarding claim 10, Zhang discloses [the method of claim 1, further comprising: responsive to determining that the ostensible pose data and the ostensible image data do match],
providing pose-based services to the client device (See Zhang, ¶ [0096], game server 110 may receive the confirmation receipt and proceed with providing the game content specific to the location of the client device 120).
However, Zhang fails to teach the method of claim 1, further comprising: responsive to determining that the ostensible pose data and the ostensible image data do match.
Lee, working in the same field of endeavor, teaches responsive to determining that the ostensible pose data and the ostensible image data are not valid (See Lee, Pg. 8. right col., par. 3, there should be no discrepancies between the smartphone image and the candidate image at ground truth. Examiner considers ground truth as a pose data).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference to be responsive to determining ostensible pose data and the ostensible image data are not valid based on the method of Lee’s reference. The suggestion/motivation would have been for accurate and robust pose estimation and to match the segmented generated images with the segmented smartphone images (See Lee, Abstract).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with Zhang to obtain the invention as specified in claim 10.
Regarding claim 11, Zhang discloses a non-transitory computer-readable medium storing instructions that, when executed by a computing system, cause the computing system to perform steps comprising (See Zhang, ¶ [0055], non-transitory computer-readable storage medium):
receiving, by an online system, ostensible pose data, wherein the ostensible pose data is data that ostensibly describes a pose of a client device (See Zhang, ¶ [0077], game server 120 at 606 receives the client device 120 location information, game server 120 needs to verify the location information accurately corresponds to the physical location of the client device 120. Examiner considers client device location information needing verification as “ostensible pose data”);
receiving, by an online system, ostensible pose data, wherein the ostensible pose data is data that ostensibly describes a pose of a client device; receiving ostensible image data, wherein the ostensible image data ostensibly describes an image captured by the client device while the client device is at the pose described by the ostensible pose data (See Zhang, ¶ [0071], The player captures image data of the landmark 510 with the client device 120. Examiner considers captured image data as “ostensible image data”);
determining whether the ostensible pose data and the ostensible image data are valid (See Zhang, ¶ [0077], verify the location information accurately corresponds to the physical location of the client device 120; ¶ [0079], verify that image data captured by the client device 120 positively matches the landmark); by determining whether the ostensible pose data and the ostensible image data match (See Zhang, Fig. 5, [0073], image data of the landmark 510 at the expected position 530, the captured image data would be parallel to an expected perspective plane 535) by:
[generating a predicted pose corresponding to the ostensible image data by applying a visual positioning system (VPS) model to the ostensible image data, wherein the VPS model is a model that maps an image captured by a client device to possible poses that could correspond to that image; and]
[comparing the predicted pose generated by the VPS model to the ostensible pose data; and]
[responsive to determining that the ostensible pose data and the ostensible image data are not valid,] performing a disciplinary action with regards to the client device (See Zhang, ¶ [0074], reject the client device 120 location. Examiner considers reject the client device location as “disciplinary action”).
However, Zhang fails to teach generating a predicted pose corresponding to the ostensible image data by applying a visual positioning system (VPS) model to the ostensible image data, wherein the VPS model is a model that maps an image captured by a client device to possible poses that could correspond to that image;
comparing the predicted pose generated by the VPS model to the ostensible pose data; and
responsive to determining that the ostensible pose data and the ostensible image data are not valid, [performing a disciplinary action with regards to the client device.]
Lee, working in the same field of endeavor, teaches generating a predicted pose (See Lee, Pg. 6, right col., section 3.11, lines 8-10, d 𝐱̂ is the estimated candidate pose with the highest likelihood. The chosen candidate pose stores the latitude, longitude, altitude, yaw, pitch, and roll. Examiner considers the estimated candidate pose to correspond to the predicted pose) corresponding to the ostensible image data (See Lee, Pg. 5, right col., section 3.9, lines 1-3, the candidate images are compared to the smartphone image. The matching algorithm calculates the score of each candidate image. Examiner considers the ostensible image data to be the smartphone image) by applying a visual positioning system (VPS) model to the ostensible image data (See Lee, Pg. 2, left col., par. 3, visual positioning system (VPS) identifies edges within the smartphone image and matches with edges captured from pre-surveyed images, pose-tagged edges are stored in a searchable index), wherein the VPS model is a model that maps an image captured by a client device to possible poses that could correspond to that image (See Lee, Pg. 3, left col., section 2, par. 3, The segmented smartphone image is de-rectified (Sect. 3.7) and matched with the candidate images using multiple metrics to calculate the similarity scores (Sect. 3.9). The scores of each method are combined to calculate the likelihood of each candidate. (Sect. 3.10). The chosen pose is determined by the candidate with the maximum likelihood among all the candidates (Sect. 3.11). The smartphone image, or image captured by a client device, is used to generate a likelihood of all the candidates and to determine the corresponding pose of the image out of all possible candidate images) and;
comparing (See Lee, Pg. 9, TABLE IV. Rotational performance comparison of the proposed semantic-based VPS and smartphone IMU) the predicted pose generated by the VPS model (See Lee, Pg. 6, right col., section 3.1.1, lines 8-10, d 𝐱̂ is the estimated candidate pose with the highest likelihood. The chosen candidate pose stores the latitude, longitude, altitude, yaw, pitch, and roll. Examiner considers the estimated candidate pose to correspond to the predicted pose) to the ostensible pose data (See Lee, Pg. 4, right col., section 3.6, lines 1-4, Candidate poses are distributed around the initial estimated pose. The initial rough estimation of the pose is calculated by the smartphone GNSS receiver and IMU when capturing an image with the smartphone. Examiner considers the initial estimated pose as the ostensible pose data) and,
responsive to determining that the ostensible pose data and the ostensible image data are not valid (See Lee, Pg. 8. right col., par. 3, there should be no discrepancies between the smartphone image and the candidate image at ground truth. Examiner considers ground truth as a pose data), [performing a disciplinary action with regards to the client device.]
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference to generate a predicted pose corresponding to the ostensible image data by applying a visual positioning system (VPS) model to the ostensible image data, compare the predicted pose generated by the VPS model to the ostensible image data, and be responsive to determining ostensible pose data and the ostensible image data are not valid based on the method of Lee’s reference. The suggestion/motivation would have been for accurate and robust pose estimation and to match the segmented generated images with the segmented smartphone images as suggested by Lee in the Abstract.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with Zhang to obtain the invention as specified in claim 11.
Regarding claim 12, Zhang in view of Lee discloses the non-transitory computer-readable medium of claim 11, wherein the pose data comprises global navigation satellite system (GNSS) data, magnetometer data, or inertial measurement unit (IMU) data. (See Zhang, ¶ [0037], The client 120 may also include other various sensors for recording data from the client 120 including but not limited to movement sensors, accelerometers, gyroscopes, other inertial measurement units (IMUs)).
Regarding claim 13, Zhang in view of Lee discloses the non-transitory computer-readable medium of claim 11, the steps further comprising: transmitting an instruction to the client device to collect pose data and image data over a period of time (See Zhang, ¶ [0037], client 120 can be configured to periodically send player input and other updates to the game server).
Regarding claim 14, Zhang in view of Lee discloses the non-transitory computer-readable medium of claim 13, wherein the received ostensible pose data and the received ostensible image data consists of pose data and image data from a set of timestamps within the period of time (See Zhang, ¶ [0023], Consequently, at various times (e.g., when a player requests access to access controlled features, periodically during gameplay, when the player is within proximity of certain location, etc.) additional steps may be taken to verify that the player with their client device is in fact at the corresponding real world location).
Regarding claim 15, Zhang in view of Lee discloses the non-transitory computer-readable medium of claim 14, the steps further comprising: randomly selecting the set of timestamps within the period of time (See Zhang, ¶ [0079], the game server 110 generates a set of verification instructions with one or more verification instructions generated randomly).
Regarding claim 18, Zhang discloses the non-transitory computer-readable medium of claim 11, wherein determining whether the ostensible pose data and the ostensible image data match (See Zhang, Fig. 5, [0073], image data of the landmark 510 at the expected position 530, the captured image data would be parallel to an expected perspective plane 535) comprises:
[generating a set of candidate poses based on the ostensible image data and the VPS model;]
and comparing (See Zhang, ¶ [0078], client device's 120 location compared to locations of all stored landmarks) [the set of candidate poses to the pose described by the ostensible pose data.]
However, Zhang fails to teach generating a set of candidate poses based on the ostensible image data and the VPS model;
and [comparing] the set of candidate poses to the pose described by the ostensible pose data.
Lee, working in the same field of endeavor, teaches generating a set of candidate poses based on the ostensible image data and the VPS model (See Lee, Fig.1 Flowchart of proposed semantic-based VPS based on segmented smartphone images and segmented generated images; Eqn. (3). Examiner considers Eqn. (3) as is shown in Fig. 1 to “generating a set of candidate poses”);
and [comparing] the set of candidate poses to the pose described by the ostensible pose data (See Lee, Pg. 4, right col., par. 2, Candidate poses are distributed around the initial estimated pose.)
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference for generating a set of candidate poses based on the ostensible image data and the VPS model and comparing the set of candidate poses to the pose described by the ostensible pose data based on the method of Lee’s reference. The suggestion/motivation would have been for accurate and robust pose estimation as suggested by Lee in the Abstract.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with Zhang to obtain the invention as specified in claim 18.
Regarding claim 19, Zhang discloses the non-transitory computer-readable medium of claim 18, wherein comparing (See Zhang, ¶ [0078], client device's 120 location compared to locations of all stored landmarks) [the set of candidate poses to the pose of the ostensible pose data comprises:]
computing a difference (See Zhang, ¶ [0080], the landmark recognition model may calculate relative distances between identified points. Examiner considers the relative distances as “a difference) [between each candidate pose and the pose of the ostensible pose data;]
and responsive to the difference exceeding a threshold (See Zhang, ¶ [0094], above a threshold amount), determining that the ostensible pose data and the ostensible image data do not match (See Zhang, ¶ [0080], landmark recognition model may determine the image data to not match the appropriate landmark).
However, Zhang fails to teach [comparing] the set of candidate poses to the pose of the ostensible pose data comprises:
[computing a difference] between each candidate pose and the pose of the ostensible pose data.
Lee, working in the same field of endeavor, teaches [comparing] the set of candidate poses to the pose of the ostensible pose data (See Lee, Pg. 4, right col., par. 2, Candidate poses are distributed around the initial estimated pose) comprises:
[computing a difference] between each candidate pose and the pose of the ostensible pose data (See Lee, Pg. 4, right col., par. 2, Candidate poses are distributed around the initial estimated pose).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference to compare between the set of candidate poses and the pose of the ostensible pose data based on the method of Lee’s reference. The suggestion/motivation would have been for accurate and robust pose estimation as suggested by Lee in the Abstract.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with Zhang to obtain the invention as specified in claim 19.
Regarding claim 20, Zhang discloses [the non-transitory computer-readable medium of claim 11, the steps further comprising: responsive to determining that the ostensible pose data and the ostensible image data do match],
providing pose-based services to the client device (See Zhang, ¶ [0096], game server 110 may receive the confirmation receipt and proceed with providing the game content specific to the location of the client device 120).
However, Zhang fails to teach the computer-readable medium of claim 11, the steps further comprising: responsive to determining that the ostensible pose data and the ostensible image data do match.
Lee, working in the same field of endeavor, teaches responsive to determining that the ostensible pose data and the ostensible image data are not valid (See Lee, Pg. 8. right col., par. 3, there should be no discrepancies between the smartphone image and the candidate image at ground truth. Examiner considers ground truth as a pose data).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference to be responsive to determining ostensible pose data and the ostensible image data are not valid based on the method of Lee’s reference. The suggestion/motivation would have been for accurate and robust pose estimation and to match the segmented generated images with the segmented smartphone images (See Lee, Abstract).
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lee with Zhang to obtain the invention as specified in claim 20.
Claims 6 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 2021/0023452 A1, hereafter "Zhang") in view of Lee et al. (Semantic 3D Map Change Detection and Update based on Smartphone Visual Positioning System, 2021, hereafter, “Lee”) and further in view Baldwin (US 11,507,646 B1, hereafter “Baldwin”).
Regarding claim 6, Zhang discloses the method of claim 1, wherein determining whether the ostensible image data is valid comprises: comparing the image of the ostensible image data to a set of images stored by the online system (See Zhang, ¶ [0070], In order to verify the client device's 120 location, the game server 110 accesses the game database 115 for a stored landmark corresponding to the client device's location; ¶ [0071], The client device 120 determines whether or not the captured image data from the initial position 520 matches the landmark 510 data from the set of verification instructions);
[and responsive to determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid].
However, Zhang fails to teach wherein determining whether the ostensible image data is valid comprises:
responsive to determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid.
Lee, working in the same field of endeavor, teaches the method of claim 1, wherein determining whether the ostensible image data is valid (See Lee, Pg. 2, left column, par. 3, visual positioning system (VPS) identifies edges within the smartphone image and matches with edges captured from pre-surveyed images, pose-tagged edges are stored in a searchable index. Examiner considers smartphone images as “an image captured by a client device”).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference to determine whether the ostensible image data is valid based on the method of Lee’s reference. The suggestion/motivation would have been to match the segmented generated images with the segmented smartphone image as suggested by Lee in the Abstract.
However, Zhang and Lee fail to teach responsive to determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid.
Baldwin working in the same field of endeavor, teaches responsive to determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid (See Baldwin, Col. 25, Ln. 43-45, spoofing attempts may be identified when a camera captures “matching” images).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference for determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid based on the method of Baldwin’s reference. The suggestion/motivation would have been to spot unauthorized attempts at authentication as suggested by Baldwin at Col. 2, Ln. 13.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Baldwin with Zhang and Lee to obtain the invention as specified in claim 6.
Regarding claim 16, Zhang discloses the non-transitory computer-readable medium of claim 11 , wherein determining whether the ostensible image data is valid comprises: comparing the image of the ostensible image data to a set of images stored by the online system (See Zhang, ¶ [0070], In order to verify the client device's 120 location, the game server 110 accesses the game database 115 for a stored landmark corresponding to the client device's location; ¶ [0071], The client device 120 determines whether or not the captured image data from the initial position 520 matches the landmark 510 data from the set of verification instructions);
[and responsive to determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid].
However, Zhang fails to teach wherein determining whether the ostensible image data is valid comprises:
responsive to determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid.
Lee, working in the same field of endeavor, teaches the method of claim 1, wherein determining whether the ostensible image data is valid (See Lee, Pg. 2, left column, par. 3, visual positioning system (VPS) identifies edges within the smartphone image and matches with edges captured from pre-surveyed images, pose-tagged edges are stored in a searchable index. Examiner considers smartphone images as “an image captured by a client device”).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference to determine whether the ostensible image data is valid based on the method of Lee’s reference. The suggestion/motivation would have been to match the segmented generated images with the segmented smartphone image as suggested by Lee in the Abstract.
However, Zhang and Lee fail to teach responsive to determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid.
Baldwin working in the same field of endeavor, teaches responsive to determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid (See Baldwin, Col. 25, Ln. 43-45, spoofing attempts may be identified when a camera captures “matching” images).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s reference for determining, based on the comparing, that the image matches one of the set of images, determining that the image data is not valid based on the method of Baldwin’s reference. The suggestion/motivation would have been to spot unauthorized attempts at authentication as suggested by Baldwin at Col. 2, Ln. 13.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Baldwin with Zhang and Lee to obtain the invention as specified in claim 16.
Claims 7 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Zhang (US 2021/0023452 A1, hereafter "Zhang") in view of Lee et al. (Semantic 3D Map Change Detection and Update based on Smartphone Visual Positioning System, 2021, hereafter, “Lee”), further in view of Baldwin (US 11,507,646 B1, hereafter “Baldwin”), and even further in view of Lin et al. (“CloudAR: A Cloud-based Framework for Mobile Augmented Reality”, 2017, hereafter “Lin”).
Regarding claim 7, Zhang in view of Lee and further in view of Baldwin disclose all limitations of claim 6 as mentioned above. However, Zhang in view of Lee and further in view of Baldwin fail to teach applying a locality-sensitive hashing algorithm to the image.
Lin, working in the same field of endeavor, teaches applying a locality-sensitive hashing algorithm to the image. (See Lin, Pg. 7 right col., par. 1, images will be stored and go through Locality Sensitive Hashing (LSH)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s references to compare the image to the set of images by applying a locality-sensitive hashing algorithm to the image based on the method of Lin’s reference. The suggestion/motivation would have been for faster retrieval as suggested by Lin at Pg. 7, Right col., par 1.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lin with Zhang in view of Lee and further in view of Baldwin to obtain the invention as specified in claim 7.
Regarding claim 17, Zhang in view of Lee and further in view of Baldwin disclose all limitations of claim 16 as mentioned above. However, Zhang in view of Lee and further in view of Baldwin fail to teach applying a locality-sensitive hashing algorithm to the image.
Lin, working in the same field of endeavor, teaches applying a locality-sensitive hashing algorithm to the image. (See Lin, Pg. 7 right col., par. 1, images will be stored and go through Locality Sensitive Hashing (LSH)).
Thus, it would have been obvious to one of ordinary skills in the art before the effective filing date of the claimed invention to modify Zhang’s references to compare the image to the set of images by applying a locality-sensitive hashing algorithm to the image based on the method of Lin’s reference. The suggestion/motivation would have been for faster retrieval as suggested by Lin at Pg. 7, Right col., par 1.
Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predictable results.
Therefore, it would have been obvious to combine Lin with Zhang in view of Lee and further in view of Baldwin to obtain the invention as specified in claim 17.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Xu et al. (A Critical Analysis of Image-based Camera Pose Estimation Techniques) discloses methods for determining a camera’s pose from images. Traditional techniques can provide reliable data with good image conditions, and newer learning-based methods give more flexibility but need more training data. The technique chosen for pose estimation can depend on image quality and the complexity of the scene.
Speigel et al. (US 20200401617 A1) discloses a system for image-based self-localization of an image taken with a device to determine the location of said image. The image is compared to geotagged reference images with known locations. The geolocation of the user is determined based on the location of the captured image.
Bao et al. (US 20220319046 A1) discloses a visual positioning method that uses an image and a 3D point cloud map. The system identifies a region in the 3D point cloud that corresponds to a feature from the image to then determine the position of the imaging device.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/JASMIN MARCELINO HERNAND/Examiner, Art Unit 2676
/Henok Shiferaw/Supervisory Patent Examiner, Art Unit 2676
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