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 .
2. This Office Action is sent in response to Applicant’s Communication received on August 22, 2025 for application number 19/307,555. This Office hereby acknowledges receipt of the following and placed of record in file: Specification, Drawings, Abstract, Oath/Declaration and Claims.
3. Claims 1-20 are presented for examination.
Information Disclosure Statement
4. The information disclosure statement (IDS) submitted on May 29, 2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Remarks
5. Examiner notes that this application also discloses only subject matter disclosed in first prior application no 17/654,754, filed on March 14, 2022 and names the inventor or at least one joint inventor named in the prior applications. Accordingly, this application may constitute a continuation or division. Said prior application was granted a patent, U.S. Patent No. 12,407,804 B2. Examiner revised claims 1-12 of U.S. Patent No. 12,407,804 B2 but couldn’t find any grounds of rejection of Double Patenting type.
Claim Rejections - 35 USC § 102
6. 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.
7. 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.
8. Claims 1-4, 6 and 9-18 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by ORTIZ EGEA et al.(US 2019/0355136 A1)(hereinafter Ortiz Egea).
Regarding claim 1, Ortiz Egea discloses a computing device [See Ortiz Egea: Fig. 9 and par. 18, 55-57 regarding Computing system 900] comprising:
a logic machine[See Ortiz Egea: Fig. 9 and par. 18, 55-57 regarding Computing system 900 includes a logic machine 902 ]; and
a storage machine storing instructions executable by the logic machine to[See Ortiz Egea: Fig. 9 and par. 58-62 regarding Computing system 900 includes a storage machine 904 configured to hold instructions executable by the logic machine 902 to implement the methods and processes described herein.]:
receive input of a first depth image and a first active brightness image, the first depth image and first active brightness image corresponding to a first frame of depth data acquired by a time-of-flight (ToF) camera[See Ortiz Egea: at least Figs. 1-5 and par. 13-15, 17, 19-23, 28, 30-38 regarding The active light signal emitted from the ToF illuminator 116 may be temporally modulated in different modulation frequencies for different IR image captures...In the illustrated example, IR light 122′ is measured by a sensor 106 of sensor array 104, thus providing phase information useable with the knowledge of the camera's configuration to determine the world space position of a locus of subject 102. The ToF controller machine 118 is configured to generate a depth image 128 based on a plurality of captured IR images. The ToF camera 100 is configured to generate key-frame depth images based on sets of key-frame IR images acquired for the key frame. The key-frame sets of IR images are acquired for a plurality of different modulation frequencies of IR light emitted from the ToF illuminator 112…...See also in par. 28 about dynamic range of the active brightness of the key-frame IR images..( (Thus, depth images comprise active brightness information for each of the modulation frequencies. Accordingly, input for a first depth image and active brightness image is received.)],
receive input of a second depth image and a second active brightness image, the second depth image and the second active brightness image corresponding to a second frame of depth data acquired by the ToF camera[See Ortiz Egea: at least Figs. 1-5 and par. 13-15, 17, 19-23, 28, 30-38 regarding The active light signal emitted from the ToF illuminator 116 may be temporally modulated in different modulation frequencies for different IR image captures...In the illustrated example, IR light 122′ is measured by a sensor 106 of sensor array 104, thus providing phase information useable with the knowledge of the camera's configuration to determine the world space position of a locus of subject 102. The ToF controller machine 118 is configured to generate a depth image 128 based on a plurality of captured IR images. The ToF camera 100 is configured to generate key-frame depth images based on sets of key-frame IR images acquired for the key frame. The key-frame sets of IR images are acquired for a plurality of different modulation frequencies of IR light emitted from the ToF illuminator 112…...See also in par. 28 about dynamic range of the active brightness of the key-frame IR images..( (Thus, depth images comprise active brightness information for each of the modulation frequencies. Accordingly, input for a second depth image and active brightness image is received.)],
based at least upon a comparison between the first active brightness image and the second active brightness image, determine an interframe 2D translation[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding The ToF controller machine 118 is configured to determine a positional translation 406 of these features from the set of key-frame IR images for the modulation frequency (K) to the set of P-frame IR images. In one example, the positional translation 406 includes a horizontal and vertical shift that is applied to the sets of key-frame IR images for the other modulation frequencies based on the change in position of the tracked features such that the key-frame IR images are registered to the P-frame IR images…In particular, for each P-frame, the positions of the features identified for the P-frame are compared to the positions of the features in the key frame to determine the positional translation that is applied to the plurality of key-frame IR images for the other modulation frequencies. The P-frame depth image is generated based on the set of P-frame IR images for the P-frame and the positional translation of the identified features from the prior key frame to the P-frame. In some implementations, positional translation may be intermediately tracked from P-frame to P-frame.],
based at least upon the interframe 2D translation, apply a correction to the first depth image to obtain an interframe-generated depth image, and output the interframe-generated depth image[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding The ToF controller machine 118 is configured to produce a sparse solution space from the plurality of sets of key-frame IR images 400 from which the phase wrapping of the phase difference of the IR light is determined and the key-frame depth image 402 is generated and to generate the P-frame depth image 408 also based on the translated and cropped key-frame IR images for the other modulation frequencies].
Regarding claim 11, Ortiz Egea discloses a depth camera camera [See Ortiz Egea: at least Fig. 1 and par. 13-15 regarding time-of-flight (ToF) camera that determines the depth of a subject.], comprising:
a time-of-flight (ToF) image sensor configured to acquire depth image data at two or more illumination light modulation frequencies[See Ortiz Egea: at least Figs. 1-2 and par. 13-15, 17, 19-23 regarding ToF illuminator for acquiring a set of key-frame infrared (IR) images for each of a plurality of different modulation frequencies for a key frame… Each key frame depth image is generated based on a set of IR images for each of a plurality of different modulation frequencies… The ToF controller machine 118 is configured to repeatedly activate the ToF illuminator 112 and synchronously address the sensors 106 of sensor array 104 to acquire IR images. The active light signal emitted from the ToF illuminator 116 may be temporally modulated in different modulation frequencies for different IR image captures… ];
a logic machine[See Ortiz Egea: Fig. 9 and par. 18, 55-57 regarding Computing system 900 includes a logic machine 902 ]; and
a storage machine storing instructions executable by the logic machine to [See Ortiz Egea: Fig. 9 and par. 58-62 regarding Computing system 900 includes a storage machine 904 configured to hold instructions executable by the logic machine 902 to implement the methods and processes described herein.]
receive input of a first depth image and a first active brightness image, the first depth image and first active brightness image corresponding to a first frame of depth data acquired by the ToF image sensor[See Ortiz Egea: at least Figs. 1-5 and par. 13-15, 17, 19-23, 28, 30-38 regarding The active light signal emitted from the ToF illuminator 116 may be temporally modulated in different modulation frequencies for different IR image captures...In the illustrated example, IR light 122′ is measured by a sensor 106 of sensor array 104, thus providing phase information useable with the knowledge of the camera's configuration to determine the world space position of a locus of subject 102. The ToF controller machine 118 is configured to generate a depth image 128 based on a plurality of captured IR images. The ToF camera 100 is configured to generate key-frame depth images based on sets of key-frame IR images acquired for the key frame. The key-frame sets of IR images are acquired for a plurality of different modulation frequencies of IR light emitted from the ToF illuminator 112…...See also in par. 28 about dynamic range of the active brightness of the key-frame IR images..( (Thus, depth images comprise active brightness information for each of the modulation frequencies. Accordingly, input for a first depth image and active brightness image is received.)],
receive input of a second depth image and a second active brightness image, the second depth image and the second active brightness image corresponding to a second frame of depth data acquired by the ToF image sensor[See Ortiz Egea: at least Figs. 1-5 and par. 13-15, 17, 19-23, 28, 30-38 regarding The active light signal emitted from the ToF illuminator 116 may be temporally modulated in different modulation frequencies for different IR image captures...In the illustrated example, IR light 122′ is measured by a sensor 106 of sensor array 104, thus providing phase information useable with the knowledge of the camera's configuration to determine the world space position of a locus of subject 102. The ToF controller machine 118 is configured to generate a depth image 128 based on a plurality of captured IR images. The ToF camera 100 is configured to generate key-frame depth images based on sets of key-frame IR images acquired for the key frame. The key-frame sets of IR images are acquired for a plurality of different modulation frequencies of IR light emitted from the ToF illuminator 112…...See also in par. 28 about dynamic range of the active brightness of the key-frame IR images..( (Thus, depth images comprise active brightness information for each of the modulation frequencies. Accordingly, input for a second depth image and active brightness image is received.)],
based at least upon a comparison between the first active brightness image and the second active brightness image, determine an interframe 2D translation[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding The ToF controller machine 118 is configured to determine a positional translation 406 of these features from the set of key-frame IR images for the modulation frequency (K) to the set of P-frame IR images. In one example, the positional translation 406 includes a horizontal and vertical shift that is applied to the sets of key-frame IR images for the other modulation frequencies based on the change in position of the tracked features such that the key-frame IR images are registered to the P-frame IR images…In particular, for each P-frame, the positions of the features identified for the P-frame are compared to the positions of the features in the key frame to determine the positional translation that is applied to the plurality of key-frame IR images for the other modulation frequencies. The P-frame depth image is generated based on the set of P-frame IR images for the P-frame and the positional translation of the identified features from the prior key frame to the P-frame. In some implementations, positional translation may be intermediately tracked from P-frame to P-frame.],
based at least upon the interframe 2D translation, apply a correction to the first depth image to obtain an interframe-generated depth image, and output the interframe-generated depth image[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding The ToF controller machine 118 is configured to produce a sparse solution space from the plurality of sets of key-frame IR images 400 from which the phase wrapping of the phase difference of the IR light is determined and the key-frame depth image 402 is generated and to generate the P-frame depth image 408 also based on the translated and cropped key-frame IR images for the other modulation frequencies].
.
Regarding claim 16, Ortiz Egea discloses a method for reducing motion blur in three-dimensional (3D) depth data[See Ortiz Egea: at least Fig. 1 and par. 13-15, 28 regarding method for time-of-flight (ToF) camera that determines the depth of a subject to provide de-noising operations among other operations], the method comprising:
receiving input of a first depth image and a first active brightness image, the first depth image and first active brightness image corresponding to a first frame of depth data acquired by a time-of-flight (ToF) camera[See Ortiz Egea: at least Figs. 1-5 and par. 13-15, 17, 19-23, 28, 30-38 regarding The active light signal emitted from the ToF illuminator 116 may be temporally modulated in different modulation frequencies for different IR image captures...In the illustrated example, IR light 122′ is measured by a sensor 106 of sensor array 104, thus providing phase information useable with the knowledge of the camera's configuration to determine the world space position of a locus of subject 102. The ToF controller machine 118 is configured to generate a depth image 128 based on a plurality of captured IR images. The ToF camera 100 is configured to generate key-frame depth images based on sets of key-frame IR images acquired for the key frame. The key-frame sets of IR images are acquired for a plurality of different modulation frequencies of IR light emitted from the ToF illuminator 112…...See also in par. 28 about dynamic range of the active brightness of the key-frame IR images..( (Thus, depth images comprise active brightness information for each of the modulation frequencies. Accordingly, input for a first depth image and active brightness image is received.)],
receiving input of a second depth image and a second active brightness image, the second depth image and the second active brightness image corresponding to a second frame of depth data acquired by the ToF camera[See Ortiz Egea: at least Figs. 1-5 and par. 13-15, 17, 19-23, 28, 30-38 regarding The active light signal emitted from the ToF illuminator 116 may be temporally modulated in different modulation frequencies for different IR image captures...In the illustrated example, IR light 122′ is measured by a sensor 106 of sensor array 104, thus providing phase information useable with the knowledge of the camera's configuration to determine the world space position of a locus of subject 102. The ToF controller machine 118 is configured to generate a depth image 128 based on a plurality of captured IR images. The ToF camera 100 is configured to generate key-frame depth images based on sets of key-frame IR images acquired for the key frame. The key-frame sets of IR images are acquired for a plurality of different modulation frequencies of IR light emitted from the ToF illuminator 112…...See also in par. 28 about dynamic range of the active brightness of the key-frame IR images..( (Thus, depth images comprise active brightness information for each of the modulation frequencies. Accordingly, input for a second depth image and active brightness image is received.)],
based at least upon a comparison between the first active brightness image and the second active brightness image, determine an interframe 2D translation[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding The ToF controller machine 118 is configured to determine a positional translation 406 of these features from the set of key-frame IR images for the modulation frequency (K) to the set of P-frame IR images. In one example, the positional translation 406 includes a horizontal and vertical shift that is applied to the sets of key-frame IR images for the other modulation frequencies based on the change in position of the tracked features such that the key-frame IR images are registered to the P-frame IR images…In particular, for each P-frame, the positions of the features identified for the P-frame are compared to the positions of the features in the key frame to determine the positional translation that is applied to the plurality of key-frame IR images for the other modulation frequencies. The P-frame depth image is generated based on the set of P-frame IR images for the P-frame and the positional translation of the identified features from the prior key frame to the P-frame. In some implementations, positional translation may be intermediately tracked from P-frame to P-frame.],
based at least upon the interframe 2D translation, apply a correction to the first depth image to obtain an interframe-generated depth image, and outputting the interframe-generated depth image[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding The ToF controller machine 118 is configured to produce a sparse solution space from the plurality of sets of key-frame IR images 400 from which the phase wrapping of the phase difference of the IR light is determined and the key-frame depth image 402 is generated and to generate the P-frame depth image 408 also based on the translated and cropped key-frame IR images for the other modulation frequencies].
Regarding claims 2, 14 and 17, Ortiz Egea discloses all of the limitations of claims 1, 11 and 16, and are analyzed as previously discussed with respect to those claims. Further on, Ortiz Egea discloses wherein the first active brightness image comprises a first intraframe-corrected active brightness image, and the second active brightness image comprises a second intraframe-corrected active brightness image[See Ortiz Egea: at least Figs. 1-5 and par. 27-38 regarding The plurality of sets of key-frame IR images 400 acquired for the key frame (KF1) may be used to generate a key-frame depth image 402. In the illustrated example, each set only includes a single image, although each set may include more images. The ToF controller machine 118 is configured to produce a sparse solution space from the plurality of sets of key-frame IR images 400 from which the phase wrapping of the phase difference of the IR light is determined and the key-frame depth image 402 is generated. Because, the depth information can be determined from the plurality of sets of key-frame IR images 400, the key-frame depth image 402 can be generated without using information from any other frames (e.g., other key frames or P-frames). Additionally, the ToF controller machine 118 may be configured to generate a key-frame IR intensity image based on the plurality of key-frame IR images 400. The key-frame IR intensity image may include, for each sensor of the sensor array, an IR light intensity value…Further, in Fig. 5 shows a first and a second key-frame IR intensity images].
Regarding claims 3 and 15, Ortiz Egea discloses all of the limitations of claims 1 and 11, and are analyzed as previously discussed with respect to those claims. Further on, Ortiz Egea discloses wherein the first depth image comprises a first intraframe-corrected depth image, and the second depth image comprises a second intraframe-corrected depth image[See Ortiz Egea: at least Figs. 1-5 and par. 27-38 regarding The plurality of sets of key-frame IR images 400 acquired for the key frame (KF1) may be used to generate a key-frame depth image 402. In the illustrated example, each set only includes a single image, although each set may include more images. The ToF controller machine 118 is configured to produce a sparse solution space from the plurality of sets of key-frame IR images 400 from which the phase wrapping of the phase difference of the IR light is determined and the key-frame depth image 402 is generated. Because, the depth information can be determined from the plurality of sets of key-frame IR images 400, the key-frame depth image 402 can be generated without using information from any other frames (e.g., other key frames or P-frames)…Further, in Fig. 5 shows a first and a second key-frame depth images].
Regarding claims 4 and 12, Ortiz Egea discloses all of the limitations of claims 1 and 11, and are analyzed as previously discussed with respect to those claims. Further on, Ortiz Egea discloses wherein the instructions executable to determine the interframe 2D translation comprise instructions executable to extract one or more features from the first active brightness image, extract one or more features from the second active brightness image[See Ortiz Egea: at least Figs. 1-7b and par.30-38 regarding the ToF controller machine 118 may identify one or more features of the imaged scene based on the plurality of sets of key-frame IR images 400. Any suitable number of features may be identified and/or tracked from frame to frame. In this example, the number of different features is represented by (j)… The position of the identified features may be tracked from the key frame to the P-frame to determine a positional translation that may be used to determine a phase wrapping applied to P-frame IR images acquired for the P-frame to generate a P-frame depth image… The ToF controller machine 118 is configured to identify the features of the imaged scene based on the set of P-frame IR images 404 ..], and
determine the interframe 2D translation further based on a comparison of the one or more features extracted from the first active brightness image with the one or more features extracted from the second active brightness image [See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding the ToF controller machine is configured to dynamically adjust the number of successive P-frames between key frames. The number of successive P-frames between key frames may be dynamically adjusted based on any suitable operating parameter or condition. In one example, the ToF controller machine 118 is configured to dynamically adjust the number of successive P-frames between key frames based on an amount of positional translation of one or more features identified in key-frame IR images and P-frame IR images. For example, if the identified feature(s) change position by less than a threshold amount between the key frame and the current P-frame, then the next frame is designated as a P-frame and another P-frame depth image is generated. If the identified feature(s) change position by greater than or equal to the threshold amount between the key frame and the current P-frame, then the next frame is designated as a key frame and a key-frame depth image is generated…].
Regarding claims 6 and 13, Ortiz Egea discloses all of the limitations of claims 4 and 12, and are analyzed as previously discussed with respect to those claims. Further on, Ortiz Egea discloses wherein the instructions are executable to form a reference feature map from the one or more features extracted from the first active brightness image, form a current feature map from the one or more features extracted from the second active brightness image, and determine the interframe 2D translation based upon a comparison between the reference feature map and the current feature map[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding the ToF controller machine is configured to dynamically adjust the number of successive P-frames between key frames. The number of successive P-frames between key frames may be dynamically adjusted based on any suitable operating parameter or condition. In one example, the ToF controller machine 118 is configured to dynamically adjust the number of successive P-frames between key frames based on an amount of positional translation of one or more features identified in key-frame IR images and P-frame IR images. For example, if the identified feature(s) change position by less than a threshold amount between the key frame and the current P-frame, then the next frame is designated as a P-frame and another P-frame depth image is generated. If the identified feature(s) change position by greater than or equal to the threshold amount between the key frame and the current P-frame, then the next frame is designated as a key frame and a key-frame depth image is generated… In the above described approach, a P-frame depth image is generated based on the positional translation of feature(s) identified in IR images of a key frame and a P-frame…].
Regarding claim 9, Ortiz Egea discloses all of the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Further on, Ortiz Egea discloses wherein the instructions are executable to obtain the interframe-generated depth image based upon the interframe 2D translation and further based on a scalar[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding Referring to Fig. 4, the ToF controller machine 118 is configured to determine a positional translation 406 of these features from the set of key-frame IR images for the modulation frequency (K) to the set of P-frame IR images. In one example, the positional translation 406 includes a horizontal and vertical shift that is applied to the sets of key-frame IR images for the other modulation frequencies based on the change in position of the tracked features such that the key-frame IR images are registered to the P-frame IR images. After the ToF controller machine 118 applies the positional translation to the sets of key-frame IR images, the ToF controller machine 118 may be configured to crop the key-frame IR images to match the P-frame IR images. The ToF controller machine 118 is configured to generate a P-frame depth image 408 based at least on the set of P-frame IR images acquired for the single modulation frequency (K) and the positional translation of the features of the scene. More particularly, the ToF controller machine 118 may be configured to generate the P-frame depth image 408 also based on the translated and cropped key-frame IR images for the other modulation frequencies…].
Regarding claim 10, Ortiz Egea discloses all of the limitations of claim 1, and are analyzed as previously discussed with respect to that claim. Further on, Ortiz Egea discloses wherein the instructions are executable to obtain a plurality of interframe-generated depth images, and output the plurality of interframe-generated depth images [See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding Referring to Figs. 4 and 5, The ToF controller machine 118 is configured to generate a P-frame depth image 408 based at least on the set of P-frame IR images acquired for the single modulation frequency (K) and the positional translation of the features of the scene. More particularly, the ToF controller machine 118 may be configured to generate the P-frame depth image 408 also based on the translated and cropped key-frame IR images for the other modulation frequencies. This approach for generating a P-frame depth image may be repeatedly performed any suitable number of times for successive P-frames between generating key-frame depth images for key frames…].
Regarding claim 18, Ortiz Egea discloses all of the limitations of claim 16, and are analyzed as previously discussed with respect to that claim. Further on, Ortiz Egea discloses further comprising outputting the interframe 2D translation with the interframe-generated depth image[See Ortiz Egea: at least Figs. 1-5 and par. 30-38 regarding The ToF controller machine 118 is configured to produce a sparse solution space from the plurality of sets of key-frame IR images 400 from which the phase wrapping of the phase difference of the IR light is determined and the key-frame depth image 402 is generated and to generate the P-frame depth image 408 also based on the translated and cropped key-frame IR images for the other modulation frequencies…].
Claim Rejections - 35 USC § 103
9. 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.
10. 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.
11. Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over ORTIZ EGEA et al.(US 2019/0355136 A1)(hereinafter Ortiz Egea) in view of ORTIZ EGEA (US 2020/0393245 A1)(hereinafter Ortiz Egea2).
Regarding claim 5, Ortiz Egea discloses all of the limitations of claim 4, and are analyzed as previously discussed with respect to that claim.
Ortiz Egea does not explicitly disclose wherein the instructions are further executable to extract features from an image using one or more of a Sobel edge detection algorithm, a Canny edge detection algorithm, or a sum of squared differences (SSD) threshold.
However, evaluating features in time-of- flight depth images by computing sum of squared differences (SSD) for the image pixels and comparing them was well known in the art at the time of the invention was filed as evident from Ortiz Egea2[See Ortiz Egea2: at least par. 41-58, 86 regarding To obtain a diffusion correction, a signed metric is defined, which also may be used to classify pixel j as foreground or background. The weighting coefficient α corresponding to each kernel pixel i varies as a ratio of a squared norm of the signal value of that kernel pixel i to a squared norm of the signal value of the given (i.e., target) pixel j. .. The weighting coefficient β corresponding to each kernel pixel i varies as a ratio of a squared norm of the signal value of that kernel pixel i to a sum of squared norms of the signal values of all kernel pixels. Thus, α and β are similarly defined, but β is normalized by dividing out the average of the square norm per frequency… For ease of description, any of the above coefficients α, β, and γ are again referred to generically as ξ. An edge may be indicated when either the value or the squared sum of ξ(i, k) divided by the square of the number of pixels of the kernel is above a threshold… The coefficients are computed using only the average of the squared norm of the signal, thereby removing the phase information from the coefficients. The resulting signal value (the ‘active brightness’ herein) is therefore independent of the modulation frequency. By averaging the values obtained at each modulation frequency, further noise reduction is achieved...].
Therefore, it would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to modify Ortiz Egea with Ortiz Egea2 teachings by including “wherein the instructions are further executable to extract features from an image using one or more of a Sobel edge detection algorithm, a Canny edge detection algorithm, or a sum of squared differences (SSD) threshold” because this combination has the benefit of providing a feature analysis to better mitigate the effect of signal diffusion among pixels[See Ortiz Egea2: at least par. 3].
Allowable Subject Matter
12. Claims 7, 8, 19 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims.
Conclusion
13. Any inquiry concerning this communication or earlier communications from the examiner should be directed to ANA J PICON-FELICIANO whose telephone number is (571)272-5252. The examiner can normally be reached Monday-Friday 9:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Christopher Kelley can be reached at 571 272 7331. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ana Picon-Feliciano/Examiner, Art Unit 2482
/CHRISTOPHER S KELLEY/Supervisory Patent Examiner, Art Unit 2482
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