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 .
Information Disclosure Statement
The information disclosure statement(s) (IDS) was/were submitted on 14 April 2025. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Response to Arguments
Applicant’s arguments, see Response to Restriction Requirement mailed 9 March 2026, filed 7 May 2026, with respect to the Restriction Requirement have been fully considered and are persuasive. The Restriction Requirement of the claims has been withdrawn.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-3 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Kotov (US 2025/0115699 A1).
Regarding Claim 1, Kotov discloses a system, comprising: a polarization camera [Kotov: FIG. 13: 12; and ¶ [0153] The combined light 310 is received and detected by the hyperspectral infrared camera 12]; a first circularly polarized light source [Kotov: ¶ [0151] The light emitter 302 includes at least two circularly polarized emitters 304a, 304b that emit circularly polarized light 306a, 306b. The circularly polarized emitters 304a, 304b can be configured to emit circularly polarized light having opposite polarization. For example, a first circularly polarized emitter 304a can be a helical left emitter that emits light 306a that is circularly polarized left and a second circularly polarized emitter 304b can be a helical right emitter that emits light 306b that is circularly polarized right]; and a second circularly polarized light source [Kotov: ¶ [0151]], wherein the polarization camera is configured to capture a first image of an object illuminated with the first circularly polarized light source [Kotov: FIG. 13; and ¶ [0153]] and to capture a second image of the object illuminated with the second circularly polarized light source [Kotov: FIG. 13; and ¶ [0153]].
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Kotov (US 2025/0116599 A1) FIG. 13
Regarding Claim 2, Kotov discloses all the limitations of Claim 1, and is analyzed as previously discussed with respect to that claim.
Furthermore, Kotov discloses wherein the polarization camera and the first and second circularly polarized light sources are mounted on or within a housing [Kotov: FIG. 14].
Regarding Claim 3, Kotov discloses all the limitations of Claim 1, and is analyzed as previously discussed with respect to that claim.
Furthermore, Kotov discloses further comprising a controller having a processor and a non-transitory computer readable storage medium [Kotov: ¶ [0006]: The system can also include an image processing device in communication with the hyperspectral infrared camera and with the at least one environmental sensor and having a processor and memory ] storing instructions that, when executed by the processor, cause the processor to determine a 3D surface mesh of the object based on the first and second images [Kotov: ¶ [0116] The 3D vision and navigation systems of the present disclosure based on polarization-based black-body imaging can utilize the coarse depth map with surface normal from polarized IR light of the objects. In this way, as discussed in further detail below, distance calculations to the object can be performed based on passive capture measurements. Polarization data about the objects also makes it possible to acquire spatiotemporal variations in electro-magnetic field vectors and accurately determine the distance to an object].
Claim Rejections - 35 USC § 103
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) 4-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Kotov as applied to claims 1-3 above, and further in view of Barbour et al. (US 20238/0048725 A1).
Regarding Claims 4 and 11, Kotov disclose(s) all the limitations of Claims 3, respectively, and is/are analyzed as previously discussed with respect to those claims.
Furthermore, Kotov discloses herein determining the 3D surface mesh includes performing polarimetric image decomposition on each of the first and second images to decompose each of the first and second images into a linearly polarized component, and a circularly polarized component [Kotov: ¶ [0101]: The systems and methods of the present disclosure include thermal cameras modified for wavelength-specific acquisition of three-dimensional (3D) images with circular and linear polarization contrast across the infrared (IR) part of the electromagnetic spectrum, including black-body emission].
Kotov may not explicitly disclose decomposition on each of the first and second images to decompose each of the first and second images into an unpolarized component.
However, Barbour discloses decomposition on each of the first and second images to decompose each of the first and second images into an unpolarized component [Barbour: ¶ [0075] where s0 in the amount of unpolarized EM radiation 104 (e.g., preferential to a 0-degree polarization), s1 is the amount of EM radiation 104 preferential to a 90-degree polarization, s2 is the amount of EM radiation 104 preferential to a 45-degree polarization, and s3 is the amount of EM radiation 104 preferential to a right-handed circular polarization].
It would have been obvious to one having ordinary skill in the art before the effective filing date to combine the processing of Kotov with the decomposition of Barbour in order to provide improved image evaluation.
Regarding Claims 5, 12-14, and 20, Kotov in view of Barbour disclose(s) all the limitations of Claims 4 and 11, respectively, and is/are analyzed as previously discussed with respect to those claims.
Furthermore, Kotov in view of Barbour discloses wherein determining 3D surface mesh further includes: determining a polarimetric constraint based on the linearly polarized components of the first and second images [Barbour: ¶ [0091]: The above-described surface normal vectors or orientations in the x-, y-, and z-directions are merely one example of the attributes of the object 102 that may be determined by the edge processors 108, 212. Other examples of first- and second-order primitives that can be determined by the edge processors 108, 212 include: the shapes and surface anomalies of the object; surface roughness of the object; material analysis of the object; lighting analysis of the object; edges, occlusions, blobs, masks, gradients, and interior volume features of the object; surface/pixel geometry of the object; a frequency distribution of the EM radiation 104 received from the object; color and intensity information of object; EM spectrum information of the object (from any band of the EM spectrum); the degree of linear polarization, angle of polarization, angle of linear polarization, angle of incidence, angle of reflection, angle of refraction, depolarization factor, principal curvatures, mean curvature, Gaussian curvature, synthetic skin or lighting, unpolarized scatter, ellipticity, albedo, the index of refraction, cluster of angles, surface angles, slope vectors, angular relationships, rate of slope, surface scattering, specular/diffuse scattering, propagation scattering of the object; pixel-to-pixel clusters; 3D object or scene detection; distance tracking; scene reconstruction; object mapping; surface characterization; and others. Therefore, the object 102 may be represented by a broad number of parameters and by the family of pXSurface and pXShape representations. The object can also be represented by any other shape-based data, examples being depth values, point cloud sets, mesh sets, etc.]; determining first and second photometrics constraints based on the unpolarized components of the first and second images [Barbour: ¶ [0081] The edge processors 108, 212 may also be configured to determine a Mueller matrix calibration… Many other properties can also be derived from this, such as the ratio of unpolarized to polarized]; determining a surface normal map based on the polarimetric constraint and the first and second photometric constraints [Barbour: ¶ [0088]-[0090]]; and determining the 3D surface mesh based on the surface normal map [Barbour: ¶ [0091]].
Regarding Claims 6 and 16, Kotov in view of Barbour disclose(s) all the limitations of Claims 5 and 12, respectively, and is/are analyzed as previously discussed with respect to those claims.
Furthermore, Kotov in view of Barbour discloses wherein determining the polarimetric constraint includes determining an angle of linear polarization (AoLP) estimation based on the linearly polarized components of the first and second images, respectively [Barbour: ¶ [0091]; and ¶ [0052]: [0052] The first- and second-order primitives may include: …the degree of linear polarization, angle of polarization, angle of linear polarization, angle of incidence, angle of reflection, angle of refraction, depolarization factor, … and others].
Regarding Claims 7 and 17, Kotov in view of Barbour disclose(s) all the limitations of Claims 6 and 16, respectively, and is/are analyzed as previously discussed with respect to those claims.
Furthermore, Kotov in view of Barbour discloses wherein determining the AoLP estimation includes determining a first AoLP estimation based on the linearly polarized component of the first image, determining a second AoLP estimation based on the linearly polarized component of the second image, and fusing the first and second AoLP estimations [Barbour: ¶ [0052]].
Regarding Claims 8, and 18-19, Kotov in view of Barbour disclose(s) all the limitations of Claims 5 and 12, respectively, and is/are analyzed as previously discussed with respect to those claims.
Furthermore, Kotov in view of Barbour discloses wherein determining the first and second photometric constraints includes determining a lighting proxy map and iteratively refining the first and second photometric constraints using the lighting proxy map [Barbour: ¶ [0189]: an output, for example a confidence value of a normal to the surface, from one of the AI learning modules (e.g., the AI learning modules 2514, 2516), can be used to populate a value in the tensor field].
Regarding Claims 9 and 15, Kotov in view of Barbour disclose(s) all the limitations of Claims 5 and 13, respectively, and is/are analyzed as previously discussed with respect to those claims.
Furthermore, Kotov in view of Barbour discloses wherein determining the surface normal map includes convex optimization of the polarimetric constraint and the first and second photometric constraints [Kotov: FIG. 5; and ¶ [0115]-[0116]].
Regarding Claims 10, Kotov in view of Barbour disclose(s) all the limitations of Claims 9 and is/are analyzed as previously discussed with respect to that claim.
Furthermore, Kotov in view of Barbour discloses wherein determining the 3D surface mesh based on the surface normal map includes integrating surface normal of the surface normal map [Barbour: ¶ [0115]: In some examples, using an integration method, SPI surface normals are reconstructed to reveal the underlying concave shape of the metal loss feature (e.g. in the case of metal loss calculation) or to reveal the underlying convex shape of the metal loss feature (e.g. in the case of corrosion blister calculation)].
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JONATHAN R MESSMORE whose telephone number is (571)272-2773. The examiner can normally be reached Monday-Friday 9-5 EST/EDT.
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/JONATHAN R MESSMORE/Primary Examiner, Art Unit 2482
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