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 .
DETAILED ACTION
Information Disclosure Statement
The information disclosure statement(s) submitted on 1/30/2025 and 5/13/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) is/are being considered by the examiner.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
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Claims 1-2, 4-5, 8-9, 11-12, 15-16 and 19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 4-6, 9, 11-12, 17 and 19 of U.S. Patent No. 12250439 B2 (hereinafter “Pat’439”). Although the claims at issue are not identical, they are not patentably distinct from each other.
Instance Application
Pat’439
1. A method, comprising:
capturing, by a device, image data associated with a scene;
capturing, by the device, multispectral data associated with the scene;
identifying, by the device, one or more objects depicted by the image data;
performing, by the device and after identifying the one or more objects, a lookup operation in a data structure to determine representative optical properties of the one or more objects;
identifying, by the device and based on at least one of the image data or the multispectral data, captured optical properties of the one or more objects;
performing, by the device, a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties;
performing, by the device, color spectral analysis on the multispectral data to generate illumination data that identifies one or more of one or more illumination sources within the scene, one or more locations of the one or more illumination sources, or one or more illumination properties of the one or more illumination sources;
generating, by the device, a color corrected image based on performing the color adjustment correction and the color spectral analysis; and
providing, by the device and for display, the color corrected image.
1. A method, comprising:
obtaining, by a system, image data associated with a scene;
obtaining, by the system, multispectral data associated with the scene;
identifying, by the system, one or more objects depicted by the image data;
performing, by the system and after identifying the one or more objects, a lookup operation in a data structure to determine representative optical properties of the one or more objects, wherein the data structure includes information identifying:
a first optical property associated with at least a portion of an object, of the one or more objects, being of a first type of material, and a second optical property associated with at least the portion of the object being of a second type of material that is an alternative to the first type of material for the portion of the object;
identifying, by the system and based on at least one of the image data or the multispectral data, captured optical properties of the one or more objects;
… generating, by the system, a color corrected image by performing a color adjustment correction on the image data based on the illumination data and a difference between the representative optical properties and the captured optical properties; …
processing the multispectral data to generate illumination data that identifies one or more of one or more illumination sources that illuminate the one or more objects within the scene, one or more locations of the one or more illumination sources that illuminate the one or more objects within the scene, or one or more illumination properties of the one or more illumination sources that illuminate the one or more objects within the scene;
generating, by the system, a color corrected image by performing a color adjustment correction on the image data based on the illumination data and a difference between the representative optical properties and the captured optical properties; and
providing, by the system, the color corrected image to a user device for display by the user device.
2. The method of claim 1, wherein the multispectral data includes light information in one or more of a visible spectrum or a non-visible spectrum.
1… obtaining, by the system, multispectral data associated with the scene… (multispectral includes a visible spectrum or a non-visible spectrum).
4. The method of claim 1, wherein the one or more objects are identified based on processing the image data using an object detection technique.
4. The method of claim 1, wherein identifying the one or more objects depicted by the image data comprises:
processing the image data using an image processing technique to identify the one or more objects.
5. The method of claim 1, wherein identifying the one or more objects depicted by the image data comprises:
processing the image data using a machine learning model to identify the one or more objects.
5. The method of claim 1, wherein the one or more objects are identified using the multispectral data.
6. The method of claim 1, wherein identifying the one or more objects depicted by the image data comprises:
causing the scene to be illuminated;
obtaining additional multispectral data when the scene is illuminated; and
processing the additional multispectral data using a spectral composition analysis technique to identify the one or more objects.
8. A device, comprising:
one or more memories; and
one or more processors, coupled to the one or more memories, configured to:
capture image data associated with a scene;
capture multispectral data associated with the scene;
identify one or more objects depicted by the image data;
perform, after the one or more objects being identified, a lookup operation in a data structure to determine representative optical properties of the one or more objects;
identify, based on at least one of the image data or the multispectral data, captured optical properties of the one or more objects;
perform a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties;
perform color spectral analysis on the multispectral data to generate illumination data that identifies one or more of: one or more illumination sources within the scene, one or more locations of the one or more illumination sources, or one or more illumination properties of the one or more illumination sources;
generate a color corrected image based on the color adjustment correction and the color spectral analysis; and
provide, for display, the color corrected image.
9. A system, comprising:
one or more memories; and
one or more processors communicatively coupled to the one or more memories, configured to:
obtain image data associated with a scene;
obtain multispectral data associated with the scene;
identify one or more objects depicted by the image data;
perform, based on the one or more objects being identified, a lookup operation in a data structure to determine representative optical properties of the one or more objects, wherein the data structure includes information identifying: a first optical property associated with at least a portion of an object, of the one or more objects, being of a first type of material, and a second optical property associated with at least the portion of the object being of a second type of material that is an alternative to the first type of material for the portion of the object;
{… perform, based on the illumination data and a difference between the representative optical properties of the one or more objects and captured optical properties of the one or more objects, a color adjustment correction on the image data to generate a color corrected image;…}
generate, based on the multispectral data, illumination data that identifies one or more of:
an illumination source that illuminates the one or more objects within the scene, a location of the illumination source, or one or more illumination properties of the illumination source;
perform, based on the illumination data and a difference between the representative optical properties of the one or more objects and captured optical properties of the one or more objects, a color adjustment correction on the image data to generate a color corrected image; and
provide the color corrected image to a user device for display by the user device.
9. The device of claim 8, wherein the multispectral data includes light information in one or more of a visible spectrum or a non-visible spectrum.
9… obtain multispectral data associated with the scene… (multispectral includes a visible spectrum or a non-visible spectrum).
11. The device of claim 8, wherein the one or more processors, to identify the one or more objects, are configured to:
process the image data using an object detection technique; and
identify the one or more objects based on the image data being processed using the object detection technique.
11. The system of claim 9, wherein the one or more processors, to identify the one or more objects depicted by the image data, are configured to:
process the image data using at least one of: an image processing technique to identify the one or more objects; or a machine learning model to identify the one or more objects.
12. The device of claim 8, wherein the one or more processors, to identify the one or more objects, are configured to:
identify the one or more objects using the multispectral data.
12. The system of claim 9, wherein the one or more processors, to identify the one or more objects depicted by the image data, are configured to: obtain additional multispectral data with the scene; and process the additional multispectral data to identify the one or more objects.
15. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device, cause the device to:
capture image data associated with a scene;
capture multispectral data associated with the scene;
identify one or more objects depicted by the image data;
perform, after the one or more objects being identified, a lookup operation in a data structure to determine representative optical properties of the one or more objects;
identify, based on at least one of the image data or the multispectral data, captured optical properties of the one or more objects;
perform a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties;
perform color spectral analysis on the multispectral data to generate illumination data that identifies one or more of: one or more illumination sources within the scene, one or more locations of the one or more illumination sources, or one or more illumination properties of the one or more illumination sources;
generate a color corrected image based on the color adjustment correction and the color spectral analysis; and
provide, for display, the color corrected image.
17. A non-transitory computer-readable medium storing a set of instructions, the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a system, cause the system to:
obtain image data associated with a scene;
{… generate, based on multispectral data associated with the scene, illumination data…}
identify one or more objects depicted by the image data;
perform, based on the one or more objects being identified, a lookup operation in a data structure to determine representative optical properties of the one or more objects, wherein the data structure includes information identifying: a first optical property associated with at least a portion of an object, of the one or more objects, being of a first type of material, and a second optical property associated with at least the portion of the object being of a second type of material that is an alternative to the first type of material for the portion of the object;
generate, based on multispectral data associated with the scene, illumination data that identifies one or more of: an illumination source that illuminates the one or more objects within the scene, a location of the illumination source, or one or more illumination properties of the illumination sources;
perform, based on the illumination data and a difference between the representative optical properties of the one or more objects and captured optical properties of the one or more objects, a color adjustment correction on the image data to generate a color corrected image; and
{… generate, based on multispectral data associated with the scene, illumination data that identifies one or more of: an illumination source that illuminates the one or more objects within the scene, a location of the illumination source, or one or more illumination properties of the illumination sources;…}
{… perform, based on the illumination data and a difference between the representative optical properties of the one or more objects and captured optical properties of the one or more objects, a color adjustment correction on the image data to generate a color corrected image;…}
provide the color corrected image.
16. The non-transitory computer-readable medium of claim 15, wherein the multispectral data includes light information in one or more of a visible spectrum or a non-visible spectrum.
17… {… generate, based on multispectral data associated with the scene, illumination data…}
(multispectral includes a visible spectrum or a non-visible spectrum).
19. The non-transitory computer-readable medium of claim 15, wherein the one more instructions, to cause the device to identify the one or more objects, cause the device to:
identify the one or more objects using the multispectral data.
19. The non-transitory computer-readable medium of claim 17, wherein the one or more instructions, that cause the system to perform the color adjustment correction on the image data, cause the system to: identify, based on at least one of the image data or the multispectral data, the captured optical properties of the one or more objects; and perform the color adjustment correction on the image data based on the captured optical properties of the one or more objects being identified.
Claims 3, 6-7, 10, 13-14, 17-18 and 20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 9 and 17 of U.S. Patent No. 12250439 B2 (hereinafter “Pat’439”) in view of Okada et al (US 20180309940 A1).
Regarding claim 3, claim 1 of Pat’439 teaches everything as claimed in claim 1, but fails to teach feature of claim 3. However, in the same field of endeavor Okada teaches the feature as presented in the art rejection below. Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Okada in claim 1 of Pat’439 for optimizing image acquisition time yielding a predicted result.
Regarding claims 6-7, claim 1 of Pat’439 teaches everything as claimed in claim 1, but fails to teach features of claim 6-7. However, in the same field of endeavor Okada teaches the feature as presented in the art rejection below. Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Okada in claim 1 of Pat’439 for optimizing color image quality of objects yielding a predicted result.
Regarding claims 10 and 13-14, claims 10 and 13-14 reciting features corresponding to claims 3 and 6-7 are also rejected for the same reasons above, respectively.
Regarding claim 18, claim 17 of Pat’439 teaches everything as claimed in claim 1, but fails to teach feature of claim 18. However, in the same field of endeavor Okada teaches the feature as presented in the art rejection below. Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Okada in claim 18 of Pat’439 for optimizing color image quality of detected objects yielding a predicted result.
Regarding claims 17 and 20, claims 17 and 20 reciting features corresponding to claims 3 and 6 are also rejected for the same reasons above, respectively.
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 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.
Claim(s) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Okada et al (US 20180309940 A1) in views of Lim (US 20150049211 A1) and Tokuse (US 20080152228 A1).
Regarding claim 1, Okada teaches A method, comprising:
capturing, by a device, image data associated with a scene (Fig. 5; paras. 0085, 0113; capturing a visible and near infrared image from a CMOS imaging apparatus 10 and capturing a far infrared image from a far infrared imaging apparatus 20);
capturing, by the device, multispectral data associated with the scene (Fig. 5; paras. 0085, 0113; capturing a visible and near infrared image from a CMOS imaging apparatus 10 and capturing a far infrared image from a far infrared imaging apparatus 20);
identifying, by the device, one or more objects depicted by the image data (Figs. 10, 11; paras. 0117-0112; identifying objects [face, animal, leaves or road sign] captured in the far infrared image and the visible and near infrared image);
performing, by the system and after identifying the one or more object, a lookup operation in a data structure to determine representative optical properties of the one or more objects (para. 0119, 0101; determining “flesh color is designated as a hue saturation objective value… flesh color which is a storage color of a face (the color for which a human is stored as an image) is reactivated”),
identifying, by the device and based on at least one of the image data or the multispectral data, captured optical properties of the one or more objects (Figs. 5, 11; paras. 0115-0117, 0101; identifying brightness of the objects or identifying current captured optical properties [luminance, hue and saturation] of the one or more objects being incorrected);
performing, by the device, a color adjustment correction on the image data based on
generating, by the device, a color corrected image based on performing the color adjustment correction an
providing, by the device and for display, the color corrected image (paras. 0062, 0104).
But fails to teach
performing, by the device, a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties;
processing, by the device, color spectral analysis on the multispectral data to generate illumination data that identifies one or more of: one or more illumination sources that illuminate the one or more objects within the scene, one or more locations of the one or more illumination sources that illuminate the one or more objects within the scene, or one or more illumination properties of the one or more illumination sources that illuminate the one or more objects;
generating, by the device, a color corrected image based on performing… the color spectral analysis.
However, in the same field of endeavor Lim teaches
processing, by the device, color spectral analysis on the multispectral data to generate illumination data that identifies one or more of: one or more illumination sources that illuminate the one or more objects within the scene, one or more locations of the one or more illumination sources that illuminate the one or more objects within the scene, or one or more illumination properties of the one or more illumination sources that illuminate the one or more objects; generating, by the device, a color corrected image based on performing… the color spectral analysis (Figs. 7, 8; paras. 0189-0195, 0166-0172).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Lim in Okada to have processing, by the device, color spectral analysis on the multispectral data to generate illumination data that identifies one or more of: one or more illumination sources that illuminate the one or more objects within the scene, one or more locations of the one or more illumination sources that illuminate the one or more objects within the scene, or one or more illumination properties of the one or more illumination sources that illuminate the one or more objects; generating, by the device, a color corrected image based on performing… the color spectral analysis for determining characteristics of a light source so that image noise generated in images can be eliminated according to a backlighting or front-lighting situation yielding a predicted result.
Moreover, in the same field of endeavor Tokuse teaches
performing, by the device, a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties (Figs. 2, 5; paras. 0104-0114; identifying a face in the image data, performing lookup operation based on the detected face using the LUT of figure 5 stored in face-color storage unit 62 to lookup a corresponding target “face-color information” [Y’/Cb’/Cr’] as claimed “the representative optical properties”; determining differences α,β,θ between the lookup target “face-color information” and the captured optical properties [Y/Cb/Cr] of the image data using formulas 1 and 2; correcting the image data based on the determined differences α,β,θ in the color reproducing unit 44);
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Tokuse in the combination to have performing, by the device, a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties for utilizing optimal color differences for respective faces so that face color can be optimized yielding a predicted result.
Regarding claim 2, the combination of Okada, Lim and Tokuse teaches everything as claimed in claim 1. In addition, Okada teaches wherein the multispectral data includes light information in one or more of a visible spectrum or a non-visible spectrum (Fig. 5; paras. 0085, 0113; capturing a visible and near infrared image from a CMOS imaging apparatus 10).
Regarding claim 3, the combination of Okada, Lim and Tokuse teaches everything as claimed in claim 1. In addition, Okada teaches wherein the image data and the multispectral data are sequentially obtained within a threshold period of time (Figs. 13-14; paras. 0125-0127).
Regarding claim 4, the combination of Okada, Lim and Tokuse teaches everything as claimed in claim 1. In addition, Okada teaches wherein the one or more objects are identified based on processing the image data using an object detection technique (Figs. 10, 11; paras. 0117-0112).
Regarding claim 5, the combination of Okada, Lim and Tokuse teaches everything as claimed in claim 1. In addition, Okada teaches wherein the one or more objects are identified using the multispectral data (Figs. 5, 10, 11; paras. 0117-0112).
Regarding claim 6, the combination of Okada, Lim and Tokuse teaches everything as claimed in claim 1. In addition, Okada teaches wherein identifying the one or more objects comprises: performing, for a particular portion of the scene, another lookup operation based on the multispectral data to identify an object, of the one or more objects, associated with the particular portion of the scene (Figs. 5, 10, 11; paras. 0117-0122).
Regarding claim 7, the combination of Okada, Lim and Tokuse teaches everything as claimed in claim 1. In addition, Okada teaches wherein performing the color adjustment correction on the image data comprises: performing the color adjustment correction for each pixel or group of pixels associated with the one or more objects (paras. 0097-0099, 0142).
Regarding claim 8, Okada teaches A device (Figs. 5, 22), comprising:
one or more memories (Figs. 5, 22; para. 0155); and
one or more processors communicatively coupled to the one or more memories (Figs. 5, 22; para. 0155), configured to:
capture image data associated with a scene (Fig. 5; paras. 0085, 0113; capturing a visible and near infrared image from a CMOS imaging apparatus 10 and capturing a far infrared image from a far infrared imaging apparatus 20);
capture multispectral data associated with the scene (Fig. 5; paras. 0085, 0113; capturing a visible and near infrared image from a CMOS imaging apparatus 10 and capturing a far infrared image from a far infrared imaging apparatus 20);
identify one or more objects depicted by the image data (Figs. 10, 11; paras. 0117-0112; identifying objects [face, animal, leaves or road sign] captured in the far infrared image and the visible and near infrared image);
perform, based on the one or more object being identified, a lookup operation in a data structure to determine representative optical properties of the one or more objects (para. 0119, 0101; determining “flesh color is designated as a hue saturation objective value… flesh color which is a storage color of a face (the color for which a human is stored as an image) is reactivated”),
perform a color adjustment correction on the image data based on
generate a color corrected image based on the color adjustment correction
provide, for display, the color corrected image (paras. 0062, 0104).
But fails to teach
perform a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties;
perform color spectral analysis on the multispectral data to generate illumination data that identifies one or more of: one or more illumination sources within the scene, one or more locations of the one or more illumination sources, or one or more illumination properties of the one or more illumination sources;
generate a color corrected image based on… the color spectral analysis.
However, in the same field of endeavor Lim teaches
perform color spectral analysis on the multispectral data to generate illumination data that identifies one or more of: one or more illumination sources within the scene, one or more locations of the one or more illumination sources, or one or more illumination properties of the one or more illumination sources; generate a color corrected image based on… the color spectral analysis (Figs. 7, 8; paras. 0189-0195, 0166-0172).
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Lim in Okada to have perform color spectral analysis on the multispectral data to generate illumination data that identifies one or more of: one or more illumination sources within the scene, one or more locations of the one or more illumination sources, or one or more illumination properties of the one or more illumination sources; generate a color corrected image based on… the color spectral analysis for determining characteristics of a light source so that image noise generated in images can be eliminated according to a backlighting or front-lighting situation yielding a predicted result.
Moreover, in the same field of endeavor Tokuse teaches
perform a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties (Figs. 2, 5; paras. 0104-0114; identifying a face in the image data, performing lookup operation based on the detected face using the LUT of figure 5 stored in face-color storage unit 62 to lookup a corresponding target “face-color information” [Y’/Cb’/Cr’] as claimed “the representative optical properties”; determining differences α,β,θ between the lookup target “face-color information” and the captured optical properties [Y/Cb/Cr] of the image data using formulas 1 and 2; correcting the image data based on the determined differences α,β,θ in the color reproducing unit 44);
Therefore, it would have been obvious to one of ordinary skill in this art before the effective filing date of the claimed invention (AIA ) to use the teachings as taught by Tokuse in the combination to have perform a color adjustment correction on the image data based on one or more differences between the representative optical properties and the captured optical properties for utilizing optimal color differences for respective faces so that face color can be optimized yielding a predicted result.
Regarding claims 9-14, claims 9-14 reciting features corresponding to claims 2-7 are also rejected for the same reasons above.
Regarding claim 15, Okada teaches the A non-transitory computer-readable medium storing a set of instructions (Figs. 5, 22; para. 0155), the set of instructions comprising:
one or more instructions that, when executed by one or more processors of a device (Figs. 5, 22; para. 0155), cause the device to: (corresponding features presented in claim 1/8).
Regarding claims 16-20, claims 16-20 reciting features corresponding to claims 2-6 are also rejected for the same reasons above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Quan Pham whose telephone number is (571)272-4438. The examiner can normally be reached Mon-Fri 9am-7pm.
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/Quan Pham/Primary Examiner, Art Unit 2637
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