DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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
2. 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 final rejection. 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on May 11, 2026 has been entered.
Priority
3. Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Europe on February 25, 2022. It is noted, however, that applicant has not filed a certified copy of the EO22158997.1 application as required by 37 CFR 1.55.
Response to Amendment
4. The amendment filed May 11, 2026 has been entered. Claims 1-2,4,6-7 and 9-10 remain pending in the application. Applicant’s amendments to the Claims have overcome the objection and 35 U.S.C. 112(b) rejection previously set forth in the Final Office Action mailed December 11, 2025.
Response to Arguments
5. Applicant's arguments filed May 11, 2026 have been fully considered but they are not persuasive.
6. Applicant argues that Furukawa et al. ("Towards Internet-Scale Multi-View Stereo") -- cited in IDS, hereinafter referred to as Furukawa, in view of Park et al. ("3D Modeling of Optically Challenging Objects"), hereinafter referred to as Park, fail to disclose “predefined one or more criteria that at least are based on what the camera virtually can view from its corresponding position in a coordinate system of said set of points”. The Applicant argues that no virtual location of the camera is utilized in Park and that Park only uses patches and constraint tests to find conflicting points.
Examiner replies that both Furukawa and Park teach detecting conflicting points based on a camera’s viewpoint. The camera’s viewpoint depends on the camera position so both teach a criteria based on what the camera virtually can see from the camera’s position.
Furukawa teaches in Paragraph 1 obtaining camera poses from each point through the SFM algorithm and Figure 5 teaches the green and red cameras detecting the green and red points in order to use visibility filter. The visibility filter is taught in Section 3.2 to detect conflicting points. Furthermore, Section 3.2 discusses detecting points closer to the camera. Thus, a position of the camera is also determined.
Park also teaches in Section 5.5 and Figures 12 and 13 detecting conflicting points based on the camera’s viewpoint and therefor its position as well.
7. Applicant argues that the blocking of light is in reference to the real world whereas Park’s method is directed to a virtual space. The Applicant asserts that even if real-world surfaces are coherent and block light, it does not automatically apply to Park’s method in virtual space.
Examiner replies that Park Section 1, Paragraph 4 teaches creating 3D models of real-world objects through range measurements. Thus, the range measurements are done on real-world surface and not objects in a virtual space. Section 3 also teaches that their method is run through scanning objects from multiple viewpoints. The range measurement is done to a real world object. Thus, the range measurement results in Park teach a coherent surface that block light and results in 3D data points to be filtered out through the visibility test taught by Park.
8. Applicant argues that Furukawa and Park cannot be combined because Furukawa uses 2D image data to create 3D data of an object whereas Park uses range data to obtain the 3D data of an object. The Applicant asserts that a person of ordinary skill (POSITA) in the art would understand mixing 3D data of different origins would not produce coherent results. Thus, a POSITA would not consider combining the methods of Park and Furukawa.
Examiner replies in response to applicant's argument that Furukawa and Park cannot be combined because they obtain 3D data through different means, the test for obviousness is not whether the features of a secondary reference may be bodily incorporated into the structure of the primary reference; nor is it that the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Both Furukawa’s and Park’s methods deal with 3D data points. Furthermore, Park’s Introduction Paragraph 4 states the method aims to eliminate false measurements. Thus, Furukawa can be combined with Park using the above motivation.
Claim Rejections - 35 USC § 112
9. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
10. Claims 1-2,4,6-7 and 9-10 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Regarding claim 1, lines 7-9 disclose “based on what the camera virtually can view from its corresponding position in a coordinate system of said set of points”. However, the camera is understood to be a real world camera since the camera conducts 3D imaging of a real world object as claimed in claim 1, lines 2-3. The Examiner is unclear as to what the Applicant intends by “virtually” in “what the camera virtually can view”. Does the Applicant intend there to be a different camera than the camera claimed in lines 2-3? Additionally, does the Applicant intend the camera in lines 7-9 to be a virtual camera in a virtual space virtually viewing the set of points of a 3D virtual object?
Or is the camera in lines 7-9 intended to be the same camera as claimed in lines 2-3, and that “virtually” does not mean a virtual space but is used as an adjective to emphasize what the camera is able to see in the imaging of the real world object?
Thus, claim 1 is unclear and will be examined as best understood.
Claim 10 is rejected for the same reasons as above.
Claims 2, 4, 6-7, and 9 are rejected by dependency on claim 1. Claims 1-2, 4, 6-7, and 9-10 will be examined as best understood by the Examiner.
Claim Rejections - 35 USC § 103
11. 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.
12. The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
13. Claim(s) 1,2,4 and 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Furukawa et al. ("Towards Internet-Scale Multi-View Stereo") -- cited in IDS, hereinafter referred to as Furukawa, in view of Park et al. ("3D Modeling of Optically Challenging Objects") -- cited in IDS, hereinafter referred to as Park.
14. Regarding claim 1, Furukawa teaches a method for removing erroneous points from a set of points of a three dimensional (3D) virtual object (Section 1, Paragraph 6 teaches removing filtering out reconstruction errors in a reconstruction of 3D points) provided by 3D imaging of a corresponding real world object by means of a camera with image sensor (Section 1, Paragraph 6 teaches reconstructing objects in a photo with 3D points; Section 2, Paragraph 1 teaches processing input images from a camera. Image sensors are inherent to digital cameras), wherein the method comprises:
obtaining said set of points (Section 2, Paragraph 1 teaches processing input images and obtaining a set of points from them);
identifying, for a respective point of the set of points, conflicting points in the set of points, wherein a conflicting point is a point of the set of points that cannot validly coexist with the respective point according to predefined one or more criteria (Section 3.2, Paragraph 1 teaches a visibility filter that checks for conflicts between a point and points in reconstructions from other clusters; Figure 5 shows in the visibility filter where a red point is identified to conflict with the green point. The visibility filter is one criteria, the red point can be considered the conflicting point, and the green point can be considered the respective point) that at least are based on what the camera virtually can view from its corresponding position in a coordinate system of said set of points (Section 2, Paragraph 1 teaches obtaining the camera poses from each point through the SFM algorithm. Figure 5 shows the green and red cameras viewing and generating the respective green and red points in the visibility filter section, which is one predefined criteria. There is a conflict between the red point ‘P’ and the green point based on the green camera’s position and viewpoint),
and removing, from the set of points, based on said identification for the respective point of the set of points, one or more points of the set of points that have been involved in conflicts a greater number of times than other points of the set of points involved in conflicts (Section 3.2, Paragraph 2 teaches calculating a conflict count for each point and removing the point if the conflict count is greater than a threshold. This removes points involved in more conflicts that other points since it meets a threshold and other points may not), wherein the respective point is considered involved in a conflict for each conflicting point in the set of points for the respective point or each time the respective point itself is identified as a conflicting point to another point of the set of points (Section 3.2, Paragraph 1 teaches checking for conflicts between a point and points in reconstructions from other clusters. Teaches incrementing a count each time it conflicts with another reconstruction).
However, Furukawa fails to teach wherein a conflicting point is a point of the set that cannot validly coexist with the respective point based on assumption that the points of the set of points are connected by coherent surface with closest neighboring surface points and which coherent surface would block light.
Park teaches wherein a conflicting point is a point of the set that cannot validly coexist with the respective point based on assumption that the points of the set of points are connected by coherent surface with closest neighboring surface points and which coherent surface would block light (Section 5.1 teaches assuming there is a planar patch for a surface. The planar patch indicates a continuous surface which requires all points within a specific distance, defined by equation 1, to have similar normals. Equation 3 shows the fitting error allowed for variance between normals of each point in the plane. Conflicting points would be identified if the error goes beyond the fitting error. Equation 1 selects the closest neighboring surface points and Equation 3 would prove a coherent surface. A coherent surface as disclosed in Park, identified by equations 1 and 3, can be understood to reflect, scatter, or block light).
Park discloses in Section 1 Paragraph 4 and Section 5.1 Paragraph 1-2 that the data of the points for the surface is obtained using a range measurement from a range sensor. Range sensors are known to detect points by detecting reflecting light. This proves that the coherent surface detected in Park Section 5.1 does block some light in order for the surface points to be detected for the surface test. The claim limitation also does not require the entire coherent surface to block light. Thus, if the range sensor is able to detect data used for this surface test, then it proves that parts of the surface does block light.
Furukawa and Park are considered analogous to the claimed invention as because both are in the same field of 3D modeling an object and eliminating false measurements. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of removing erroneous points taught by Furukawa with the identification of a conflicting point based on coherent surface assumptions taught by Park in order to eliminate false measurements generated by optically challenging surfaces (Park, Introduction Paragraph 4)
15. Regarding claim 2, Furukawa in view of Park teaches the limitations of claim 1. Furukawa further teaches wherein points involved in conflicts multiple times that exceed a predefined threshold are removed from the set of points (Section 3.2, Paragraphs 1-2 teach calculating a conflict count for each point and removing the point if the conflict count is greater than a threshold. They also teach an example of removing points with conflict counts that are three and above, which is a predefined threshold).
16. Regarding claim 4, Furukawa in view of Park teaches the limitations of claim 1. Furukawa further teaches the method wherein the method further comprises;
obtaining, for the respective point of the set of points, a respective camera direction corresponding to direction of light emission from a corresponding point of the real world object towards the camera, which light emission was sensed by the image sensor during said 3D imaging (Section 2, Paragraph 1 teaches obtaining the camera poses from each point through the SFM algorithm. Figure 5 shows the camera direction, or direction of light emission, from the green point on the object to the green camera which is used in the visibility filtering process. Sensing light emission by a sensor is inherent to a digital camera);
wherein the conflicting point cannot validly coexist with the respective point based on at least a camera direction (Figure 5 shows the camera direction from the green point on the object to the green camera. Also shows a red point ‘P’ that conflicts with the green point and cannot validly coexist based on the camera direction of the green camera. The red point ‘P’ is the conflicting point and the green point is the respective point).
17. Regarding claim 9, Furukawa teaches a non-transitory computer readable storage medium comprising instructions that, when executed by one or more processors, causes one or more devices (Section 4, Paragraph 1 teaches running the algorithm on a PC with processors. PCs are known to have memories which is a non-transitory computer readable storage medium) to perform the method according to claim 1 (See rejection for claim 1 above).
18. Regarding claim 10, Furukawa teaches a device for removing erroneous points from a set of points of a three dimensional (3D) virtual object provided by 3D imaging of a corresponding real world object (Section 1, Paragraph 6 teaches removing filtering out reconstruction errors in a reconstruction of 3D points) by means of a camera with image sensor (Section 1, Paragraph 6 teaches reconstructing objects in a photo with 3D points; Section 2, Paragraph 1 teaches processing input images from a camera. Image sensors are inherent to cameras), wherein said one or more devices are configured to:
obtain said set of points (Section 2, Paragraph 1 teaches processing input images and obtaining a set of points from them);
identify, for a respective point, conflicting points in the set of points, wherein a conflicting point is a point of the set of points that cannot validly coexist with the respective point according to predefined one or more criteria (Section 3.2, Paragraph 1 teaches a visibility filter that checks for conflicts between a point and points in reconstructions from other clusters; Figure 5 shows in the visibility filter where a red point is identified to conflict with the green point. The visibility filter is one criteria, the red point can be considered the conflicting point, and the green point can be considered the respective point) that at least are based on what the camera virtually can view from its corresponding position in a coordinate system of said set of points (Section 2, Paragraph 1 teaches obtaining the camera poses from each point through the SFM algorithm. Figure 5 shows the green and red cameras viewing and generating the respective green and red points in the visibility filter section, which is one predefined criteria. There is a conflict between the red point ‘P’ and the green point based on the green camera’s position and viewpoint),
and remove, from the set of points, based on said identification for respective point of the set of points, one or more points of the set of points that have been involved in conflicts a greater number of times than other points of the set of points involved in conflicts (Section 3.2, Paragraph 2 teaches calculating a conflict count for each point and removing the point if the conflict count is greater than a threshold. This removes points involved in more conflicts that other points since it meets a threshold and other points may not), wherein the respective point of the set of points is considered involved in a conflict for each conflicting point in the set of points for the respective point or each time the respective point itself is identified as the conflicting point to another point of the set of points (Section 3.2, Paragraph 1 teaches checking for conflicts between a point and points in reconstructions from other clusters. Teaches incrementing a count each time it conflicts with another reconstruction).
However, Furukawa fails to teach wherein a conflicting point is a point of the set that cannot validly coexist with the respective point based on assumption that the points of the set of points are connected by coherent surface with closest neighboring surface points and which coherent surface would block light.
Park teaches wherein a conflicting point is a point of the set that cannot validly coexist with the respective point based on assumption that the points of the set of points are connected by coherent surface with closest neighboring surface points and which coherent surface would block light (Section 5.1 teaches assuming there is a planar patch for a surface. The planar patch indicates a continuous surface which requires all points within a specific distance, defined by equation 1, to have similar normals. Equation 3 shows the fitting error allowed for variance between normals of each point in the plane. Conflicting points would be identified if the error goes beyond the fitting error. Equation 1 selects the closest neighboring surface points and Equation 3 would prove a coherent surface. A coherent surface disclosed by Park, identified by equations 1 and 3, can be understood to reflect, scatter, or block light).
Park discloses in Section 5.1 Paragraph 1-2 that the data of the points for the surface is obtained using a range measurement from a range sensor. Range sensors are known to detect points by detecting reflecting light. This proves that the coherent surface detected in Park Section 5.1 does block some light in order for the surface points to be detected for the surface test. The claim limitation also does not require the entire coherent surface to block light. Thus, if the range sensor is able to detect data used for this surface test, then it proves that parts of the surface does block light.
Furukawa and Park are considered analogous to the claimed invention as because both are in the same field of 3D modeling an object and eliminating false measurements. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the device for removing erroneous points taught by Furukawa with the identification of a conflicting point based on coherent surface assumptions taught by Park in order to eliminate false measurements generated by optically challenging surfaces (Park, Introduction Paragraph 4).
19. Claim(s) 6-7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Furukawa et al. ("Towards Internet-Scale Multi-View Stereo") -- cited in IDS, hereinafter referred to as Furukawa, in view of Park et al. ("3D Modeling of Optically Challenging Objects") -- cited in IDS, hereinafter referred to as Park, as applied to claim 1 and 4 above, and further in view of Homma (U.S. Patent Application Publication No. 2020/0340800 A1).
20. Regarding claim 6, Furukawa in view of Park teaches the limitations of claim 1. However, Furukawa and Park fail to teach the method wherein the identification of conflicting points for respective point is limited to points of the set that are present within a certain distance from the respective point.
Homma teaches the method wherein the identification of conflicting points for respective point is limited to points of the set of points that are present within a certain distance from the respective point (Paragraph 36, Figure 8 teaches detecting conflicting points that exist in a blind spot area in respect to the respective point, Ap. Only points in the blind spot area are identified as erroneous or conflicting. This can be considered a certain distance from the respective point because only points in the blind spot region bounded by sides with length Sd1, Sd2, and Hy are considered).
Furukawa, Park, and Homma are considered analogous to the claimed invention as because both are in the same field of eliminating erroneous points. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of deleting erroneous points taught by Furukawa in view of Park with the limiting identification of conflicting points to a certain distance taught by Homma in order to identify false measurements in blind spot areas (Homma Paragraph 5).
21. Regarding claim 7, Furukawa in view of Park teaches the limitations of claim 4. However, Furukawa and Park fail to teach the method wherein the 3D imaging is based on light triangulation comprising illumination of said real world object by a light source, wherein said light emission is reflected light from a surface of said real world object and resulting from said illumination.
Homma teaches the method wherein the 3D imaging is based on light triangulation comprising illumination of said real world object by a light source, wherein said light emission is reflected light from a surface of said real world object and resulting from said illumination (Paragraph 4 teaches projecting light and using triangulation to get data indicating a 3D shape of the object; Paragraph 26 and Figure 2 teach a diagram depicting light triangulation using a light source 6 to project light, and an image sensor 13 to receive the reflected light. The reflected light can be seen through marker L2 which results from the illumination from the light source 6).
Furukawa, Park, and Homma are considered analogous to the claimed invention as because both are in the same field of creating a three-dimensional model of an object and eliminating erroneous points. Thus, it would have been obvious to a person holding ordinary skill in the art before the effective filing date to modify the method of deleting erroneous points and use of a camera taught by Furukawa in view of Park with the light triangulation method taught by Homma in order to identify false measurements in blind spot areas (Homma Paragraph 5).
Conclusion
22. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
- Thyagharajan et al. (U.S. Patent Application Publication No. 2019/0236797 A1) teaches measuring 3D features through depth sensing and minimizing erroneous points and noise.
23. Any inquiry concerning this communication or earlier communications from the examiner should be directed to CHRISTINE Y AHN whose telephone number is (571)272-0672. The examiner can normally be reached M-F 9-5pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alicia Harrington can be reached at (571)272-2330. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/CHRISTINE YERA AHN/Examiner, Art Unit 2615
/ALICIA M HARRINGTON/Supervisory Patent Examiner, Art Unit 2615
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