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) submitted on 4/21/2025 and 4/23/2026 is/are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement(s) has/have been considered by the examiner.
Drawings
Figures 1 and 11A and 11B is objected to as depicting a block diagram without “readily identifiable” descriptors of each block, as required by 37 CFR 1.84(n). Rule 84(n) requires “labeled representations” of graphical symbols, such as blocks; and any that are “not universally recognized may be used, subject to approval by the Office, if they are not likely to be confused with existing conventional symbols, and if they are readily identifiable.” In the case of figure 1 and 11A and 11B, the blocks are not readily identifiable per se and therefore require the insertion of text that identifies the function of that block. That is, each vacant block should be provided with a corresponding label identifying its function or purpose.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
The following title is suggested: Correction for image blur using two machine learning models.
Claim Objections
Claim 7 objected to because of the following informalities: “the first map” in line 2 should be “a first map”.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
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.
Claims 8 and 9 are generally narrative and indefinite, failing to conform with current U.S. practice. They appear to be a literal translation into English from a foreign document and are replete with grammatical and idiomatic errors. Specifically in claim 8 “an area that does not include a pixel corresponds to the position of the luminance-saturated pixel” and in claim 9 “an area including a pixel corresponds to the position of the saturated pixel” are limitations that are particularly hard to ascertain the bounds of claims. Therefore they will not be examined as to prior art because of the idiomatic errors.
Nonstatutory 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.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1, 2, 10 and 12-13 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 and claim 2 of U.S. Patent No. 12211184B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the ‘184 patent is more narrow than the instant application, as shown in the chart below. For clarity claim 2 is italicized, claim 10 is bolded, and claim 12 is bolded and italicized in the chart below with corresponding limitations matching horizontally.
Instant Application 18/982612
US Patent No 12211184B2
1.+2+12
An image processing method comprising: acquiring first model output generated based on a first image by a first machine learning model;
acquiring second model output generated based on the first image by a second machine learning model which is different from the first machine learning model; and generating an estimated image by using the first model output and the second model output,
wherein the estimated image is generated based on a comparison based on the second model output and one of the first image and first model output.
wherein the first machine learning model and the second machine learning model are models each of which is configured to perform blur correction of the first image,
wherein a blur correction effect of the first machine learning model and a blur correction effect of the second machine learning model are different from each other.
wherein the blur correction effect of the first machine learning model for a saturated area of the first image is smaller than the blur correction effect of the second machine learning model for the saturated area.
1.
An image processing method comprising:
acquiring first model output generated based on a captured image by a first machine learning model;
acquiring second model output generated based on the captured image by a second machine learning model which is different from the first machine learning model; and
generating an estimated image by using the first model output and the second model output,
based on a comparison based on the second model output and one of the captured image and first model output,
wherein the first machine learning model and the second machine learning model are models each of which is configured to
estimate a correction component of blur in the captured image,
wherein a blur correction effect on a high-luminance object of the first machine learning model is smaller than a blur correction effect on the high-luminance object of the second machine learning model.
13
wherein the first model output and the second model output are residual components that indicate an image obtained by blur correction on the first image or a difference between the image obtained by blur correction and the first image.
1
…generating an estimated image by using the first model output and the second model output, based on a comparison based on the second model output and one of the captured image and first model output, wherein the first machine learning model and the second machine learning model are models each of which is configured to estimate a correction component of blur in the captured image…
1+2+ 10+ 12
An image processing method comprising:
acquiring first model output generated based on a first image by a first machine learning model;
acquiring second model output generated based on the first image by a second machine learning model which is different from the first machine learning model; and generating an estimated image by using the first model output and the second model output,
wherein the estimated image is generated based on a comparison based on the second model output and one of the first image and first model output.
wherein the first machine learning model and the second machine learning model are models each of which is configured to perform blur correction of the first image,
wherein a blur correction effect of the first machine learning model and a blur correction effect of the second machine learning model are different from each other.
wherein the first machine learning model and the second machine learning model are models each of which is configured to perform processing that is at least partly the same as processing performed by the other.
wherein the blur correction effect of the first machine learning model for a saturated area of the first image is smaller than the blur correction effect of the second machine learning model for the saturated area.
2
An image processing method comprising:
acquiring first model output generated based on a captured image by a first machine learning model;
acquiring second model output generated based on the captured image by a second machine learning model which is different from the first machine learning model; and
generating an estimated image by using the first model output and the second model output, based on a comparison based on the second model output and one of the captured image and first model output,
wherein the first machine learning model and the second machine learning model are models each of which is configured to estimate a correction component of blur in the captured image,
and to perform processing that is at least partly the same as processing performed by the other,
wherein a blur correction effect on a high-luminance object of the first machine learning model is smaller than a blur correction effect on the high-luminance object of the second machine learning model.
Claims 3-5, 15-16 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12211184B2 in view of Schelten (Pub. No. US20150131898A1).
Regarding claim 3, claim 1 of Patent No 12211184B2 does not explicitly disclose nor fairly suggest, whereas Schelten discloses wherein the comparison includes a difference, a ratio, or a correlation. (Schelten ¶48-49 and 55; a difference between the blurred input image and the convolution of the blur kernel with filter responses.)
Therefore it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify claim 1 of Patent No 12211184B2 using the teachings of Schelten by using a difference between the input image and a model output to pick the blur kernel as in Schelten in order to ensure that specific features have accurate reconstructions, like edges (Schelten ¶55).
Regarding claim 4, claim 1 of Patent No 12211184B2 does not explicitly disclose nor fairly suggest, whereas Schelten discloses wherein the estimated image is generated by using a first map which is generated based on the comparison. (Schelten Fig. 4 and ¶50 and 54-55; the observed blurred image can inform which blur kernel to utilize i.e. the comparison creates a map indicating which blur kernel to use to create a deblurred image.)
Therefore it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify claim 1 of Patent No 12211184B2 using the teachings of Schelten by using a map to inform which blur kernel to pick, as in Schelten, in order to ensure that specific features have accurate reconstructions, like edges (Schelten ¶55).
Regarding claim 5, claim 1 of Patent No 12211184B2, in combination with Schelten discloses the claim limitations with respect to claim 4 as described above. The combination further discloses: wherein in the generation of the estimated image, an area where the first model output or the second model output is used is determined based on the first map. (Schelten Fig. 4 and ¶50 and 54-55; the observed blurred image can inform which blur kernel to utilize i.e. the comparison creates a map indicating which blur kernel to use to create a deblurred image. Wherein it would have been obvious to utilize using a map to inform which blur kernel to pick in order to ensure that specific features have accurate reconstructions, like edges.)
Regarding claim 15, claim 1 of Patent No 12211184B2 does not explicitly disclose nor fairly suggest, whereas Schelten discloses An image processing system comprising: the image processing apparatus according to claim 11, (Schelten Fig. 7 and ¶65 software to implement the method is stored in memory.) a control apparatus which is communicable with the image processing apparatus: wherein the control apparatus includes at least one processor or circuit configured to execute a task of: (Schelten Fig. 7 and ¶63, and 66; one or more processors is disclosed and an input/output controller is disclosed.) a transmitting task configured to transmit a request regarding execution of processing for the first image, (Schelten Fig. 7 and ¶66; the input/output controller receives and processes input indicating when to apply deblurring.) wherein the image processing apparatus includes at least one processor or circuit configured to execute a plurality of tasks of: (Schelten Fig. 7 and ¶63; one or more processors is disclosed) a receiving task configured to receive the request; and wherein the image processing system executes a processing for the first image corresponding to the request. (Schelten Fig. 7 and ¶66; the input/output controller receives and processes input indicating when to apply deblurring.)
Therefore it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify claim 1 of Patent No 12211184B2 using the teachings of Schelten by implementing the method on the system of Schelten in order to provide a hardware implementation of the method.
Regarding claim 16, claim 1 of Patent No 12211184B2 does not explicitly disclose nor fairly suggest, whereas Schelten discloses A non-transitory computer-readable storage medium storing a computer program that causes a computer to execute an image processing method according to claim 1. (Schelten Fig. 7 and ¶64; memory storing the method is disclosed.)
Therefore it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify claim 1 of Patent No 12211184B2 using the teachings of Schelten by saving the method on the non-transitory memory of Schelten in order to provide a computer implementation of the method.
Claim 6 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12211184B2 in view of Schelten (Pub. No. US20150131898A1) and Haung (Huang PH, Lin YM, Yang HL, Lai SH. Image deblurring by exploiting inherent bi-level regions. In2009 16th IEEE International Conference on Image Processing (ICIP) 2009 Nov 7 (pp. 1321-1324). IEEE.) .
Regarding claim 6, claim 1 of Patent No 12211184B2 in combination with Schelten discloses the claim limitations with respect to claim 4 as described above. The combination additionally discloses wherein the first image includes a plurality of color components, (Schelten ¶7 and 67; the image to be deblurred is described as being taken from a phone and ¶67 mentions an RGB camera system which would include multiple color images.). The combination does not explicitly disclose nor fairly suggest, whereas Huang discloses wherein the first map is common to the plurality of color components. (Huang Section 3.3. Combining estimates from best-N regions; a blur kernel that is common to color channels is disclosed (see also Section 3.2. Bi.-level region searching ¶3-4).)
Therefore it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the method of the combination of claim 1 of Patent No. 12211184B2 Schelten with the teachings of Huang by including a kernel that is common among all color channels in order to avoid over smoothing (Huang Section 3.2. Bi.-level region searching ¶4).
Claim 7 rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 12211184B2 in view of Xiao (Xiao L, Gregson J, Heide F, Heidrich W. Stochastic blind motion deblurring. IEEE Transactions on Image Processing. 2015 May 13;24(10):3071-85.).
Regarding claim 7, claim 1 of Patent No 12211184B2 does not explicitly disclose nor fairly suggest, whereas Xiao discloses wherein the first map is generated based on a position of a luminance-saturated pixel of the first image and a value is obtained by the comparison, (Xiao Section IV. Algorithmic Extensions – C. Saturated or Missing Data – Found on p. 3078; unreliable pixels (MvI) are masked separately from reliable pixels (MuI). Unreliable pixels include saturated pixels. and wherein the estimated image is generated by using the first map. (Xiao Section IV. Algorithmic Extensions – C. Saturated or Missing Data – Found on p. 3078; unreliable pixels are deblurred separately from reliable pixels.)
Therefore it would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify claim 1 of Patent No 12211184B2 using the teachings of Xiao by calculating the estimated image for pixels with reliable and unreliable pixels separately in order to avoid ringing artifacts throughout the image (Xiao Section IV. Algorithmic Extensions – C. Saturated or Missing Data – Found on p. 3078).
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, 10, and 13-16 is/are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Schelten (Pub. No. US20150131898A1).
Regarding claim 14, Schelten discloses An image processing apparatus comprising: at least one processor or circuit configured to execute a plurality of tasks including: (Schelten Fig. 7 and ¶63-64; one or more processors performing software tasks is disclosed.) acquire a first model output generated based on a first image by a first machine learning model; (Schelten Fig. 3 ¶38; a blurred image (element 300) is passed into a first machine learning model (element 306) which outputs a restored image (element 308).) acquire a second model output generated based on the first image by a second machine learning model which is different from the first machine learning model; and (Schelten Fig. 3 and ¶38 and ¶51-52; a blurred image is cascaded through two layers of machine learning models to produce a restored image.) generate an estimated image by using the first model output and the second model output, (Schelten Fig. 3 and ¶38 and ¶51-52; a blurred image is cascaded through two layers of machine learning models (output 1 comes out of element 306 and output two comes out of element 314) to produce a restored image (element 316).) wherein the first machine learning model and the second machine learning model are models each of which is configured to perform blur correction of the first image, (Schelten Fig. 3 and ¶46 and ¶51; sharpened restored images are produced as 308 (output of first model) and 316 (output of second model) corresponding to input blurred image (element 300).) wherein a blur correction effect of the first machine learning model and a blur correction effect of the second machine learning model are different from each other. (Schelten ¶48 and claims 8 and 9; the second blur kernel prediction computes a refined blur kernel estimate. Meaning the two kernels would be different. Revised estimates of blur present in the blurred image are calculated ( i.e. the cascaded models produce different amounts of blur).)
Regarding claim 1, it is the corresponding method claim to claim 14 and is rejected for similar reasons.
Regarding claim 10, Schelten discloses the claim limitations with regards to claim 1 as described above. Schelten further discloses wherein the first machine learning model and the second machine learning model are models each of which is configured to perform processing that is at least partly the same as processing performed by the other. (Schelten Fig. 3 and ¶38-39; a blurred image is cascaded through two layers of machine learning models to produce a restored image. The first model being interpreted as consisting of elements 300-308 and the second model being interpreted as consisting of elements 300-316. The two models share elements 300-308.)
Regarding claim 13, Schelten discloses the claim limitations with regards to claim 1 as described above. Schelten further discloses wherein the first model output and the second model output are residual components that indicate an image obtained by blur correction on the first image or a difference between the image obtained by blur correction and the first image. (Schelten Fig. 3 and Fig. 5 and ¶5; a restored image (blur correction of the input image) is created from the first and second model outputs.)
Regarding claim 15, Schelten discloses the claim limitations with regards to claim 11 as described above. An image processing system comprising: the image processing apparatus according to claim 11, (Schelten Fig. 7 and ¶65 software to implement the method is stored in memory.) a control apparatus which is communicable with the image processing apparatus: wherein the control apparatus includes at least one processor or circuit configured to execute a task of: (Schelten Fig. 7 and ¶63, and 66; one or more processors is disclosed and an input/output controller is disclosed.) a transmitting task configured to transmit a request regarding execution of processing for the first image, (Schelten Fig. 7 and ¶66; the input/output controller receives and processes input indicating when to apply deblurring.) wherein the image processing apparatus includes at least one processor or circuit configured to execute a plurality of tasks of: (Schelten Fig. 7 and ¶63; one or more processors is disclosed) a receiving task configured to receive the request; and wherein the image processing system executes a processing for the first image corresponding to the request. (Schelten Fig. 7 and ¶66; the input/output controller receives and processes input indicating when to apply deblurring.)
Regarding claim 16, Schelten discloses the claim limitations with regards to claim 1 as described above. Schelten further discloses A non-transitory computer-readable storage medium storing a computer program that causes a computer to execute an image processing method according to claim 1. (Schelten Fig. 7 and ¶64; memory storing the method is disclosed.)
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.
Claim(s) 2-5 is/are rejected under 35 U.S.C. 103 as being unpatentable over of Schelten (Pub. No. US20150131898A1).
Regarding claim 2, Schelten discloses the claim limitations with regards to claim 1 as described above. Although not the same embodiment, Schelten discloses wherein the estimated image is generated based on a comparison based on the second model output and one of the first image and first model output. (Schelten Fig. 4 and ¶50 and 54-55; two layers of machine learning models each produce a blur kernel and a restored image. The final restored image is produced by comparing the different blur kernels (first and second model outputs) with the input blurred image.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to utilized a machine learning predictor for the blur kernel as part of the first and second models in order to ensure that specific features have accurate reconstructions, like edges (Schelten ¶55).
Regarding claim 3, Schelten discloses the claim limitations with regards to claim 2 as described above. Schelten further discloses wherein the comparison includes a difference, a ratio, or a correlation. (Schelten ¶48-49 and 55; a difference between the blurred input image and the convolution of the blur kernel with filter responses. Wherein it would have been obvious to utilize a machine learning predictor for the blur kernel as part of the first and second models in order to ensure that specific features have accurate reconstructions, like edges.)
Regarding claim 4, Schelten discloses the claim limitations with regards to claim 2 as described above. Schelten further discloses wherein the estimated image is generated by using a first map which is generated based on the comparison. (Schelten Fig. 4 and ¶50 and 54-55 ;the observed blurred image can inform which blur kernel to utilize i.e. the comparison creates a map indicating which blur kernel to use to create a deblurred image. Wherein it would have been obvious to utilize a machine learning predictor for the blur kernel as part of the first and second models in order to ensure that specific features have accurate reconstructions, like edges.)
Regarding claim 5, wherein in the generation of the estimated image, an area where the first model output or the second model output is used is determined based on the first map. (Schelten Fig. 4 and ¶50 and 54-55; the observed blurred image can inform which blur kernel to utilize i.e. the comparison creates a map indicating which blur kernel to use to create a deblurred image. Wherein it would have been obvious to utilize a machine learning predictor for the blur kernel as part of the first and second models in order to ensure that specific features have accurate reconstructions, like edges)
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over of Schelten (Pub. No. US20150131898A1) in view of Huang (Huang PH, Lin YM, Yang HL, Lai SH. Image deblurring by exploiting inherent bi-level regions. In2009 16th IEEE International Conference on Image Processing (ICIP) 2009 Nov 7 (pp. 1321-1324). IEEE.).
Regarding claim 6, Schelten discloses the claim limitations with regards to claim 4 as described above. Schelten further discloses wherein the first image includes a plurality of color components, (Schelten ¶7 and 67; the image to be deblurred is described as being taken from a phone and ¶67 mentions an RGB camera system which would include multiple color images.).
Schelten does not explicitly disclose and wherein the first map is common to the plurality of color components.
Huang, however, discloses wherein the first map is common to the plurality of color components. (Huang Section 3.3. Combining estimates from best-N regions; a blur kernel that is common to color channels is disclosed (see also Section 3.2. Bi.-level region searching ¶3-4).)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the method of Schelten with the teachings of Huang by including a kernel that is common among all color channels in order to avoid over smoothing (Huang Section 3.2. Bi.-level region searching ¶4).
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Schelten (Pub. No. US20150131898A1) in view of Xiao (Xiao L, Gregson J, Heide F, Heidrich W. Stochastic blind motion deblurring. IEEE Transactions on Image Processing. 2015 May 13;24(10):3071-85.).
Regarding claim 7, Schelten discloses the claim limitations with regards to claim 2 as described above. Schelten further discloses and a value is obtained by the comparison, (Schelten ¶48; the objective function computes a value utilizing the comparison.)
Schelten does not explicitly disclose wherein the first map is generated based on a position of a luminance-saturated pixel of the first image or and wherein the estimated image is generated by using the first map.
Xiao, however, discloses wherein the first map is generated based on a position of a luminance-saturated pixel of the first image (Xiao Section IV. Algorithmic Extensions – C. Saturated or Missing Data – Found on p. 3078; unreliable pixels (MvI) are masked separately from reliable pixels (MuI). Un reliable pixels include saturated pixels. and wherein the estimated image is generated by using the first map. (Xiao Section IV. Algorithmic Extensions – C. Saturated or Missing Data – Found on p. 3078; unreliable pixels are deblurred separately from reliable pixels.)
It would have been obvious, before the effective filing date of the claimed invention, to one of ordinary skill in the art to modify the method of Schelten with the teachings of Xiao by calculating the estimated image for pixels with reliable and unreliable pixels separately as in Xiao in order to avoid ringing artifacts throughout the image (Xiao Section IV. Algorithmic Extensions – C. Saturated or Missing Data – Found on p. 3078).
Allowable Subject Matter
Claim 11 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
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MEREDITH TAYLOR whose telephone number is (571)270-5805. The examiner can normally be reached M-Th 7:30-5. Examiner’s email is Meredith.taylor@uspto.gov.
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/MEREDITH TAYLOR/Examiner, Art Unit 2671
/VINCENT RUDOLPH/Supervisory Patent Examiner, Art Unit 2671
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