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 .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Drawings
The drawings with 7 Sheets of Figs. 1-12 received on 9/3/2024 are acknowledged and accepted.
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 5,13, rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 5 recites “a second loss” in line 5. It is not clear whether there are two loss functions or if there is one loss function as claim 5 is dependent on claim 4 which does not recite a first loss function and claim 4 is dependent on claim 1 which also does not recite a first loss function. Claim 3 is reciting a first loss function. From the specification it appears that there are first and second loss functions. For the purposes of examination, first and second loss functions are taken to be present.
Claim 13 recites “a second loss” in line 5. It is not clear whether there are two loss functions or if there is one loss function as claim 13 is dependent on claim 12 which does not recite a first loss function and claim 12 is dependent on claim 9 which also does not recite a first loss function. Claim 11 is reciting a first loss function. From the specification it appears that there are first and second loss functions. For the purposes of examination, first and second loss functions are taken to be present and claims 5,13 are reciting a second loss.
Claim Rejections - 35 USC § 102
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.
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) 1-20, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Supikov et al (US 2023/0043791 A1, of record).
Regarding Claim 1, Supikov teaches (fig 1, 6A-8) a method for extracting aberration of a holographic optical system (hologram imaging arrangement, para 49) based on image optimization, comprising:
generating a first Computer-Generated Hologram (CGH) dataset (“the SLM control 102 may have a diffraction image pattern generator 652 that generates the diffraction pattern images upon receiving a target holographic image (or data therefore) 650 during a run-time”, para 76, diffraction image is the CGH, “a set 702 of at least three diffraction (or Fresnel lens) pattern images (or focal planes) f0, f1, and f2 respectively have three different focal lengths z0, z1, and z”, para 84, fig 7) by optimizing (“the holographic diffraction image or pattern is generated based on a constrained optimization problem solved by using computer generated hologram (CGH) algorithms”, para 33) each of images included in an image dataset (“during the calibration stage, light may be propagated from the SLM with at least three different focal lengths. By displaying at least three different images each at a different focal length from the SLM”, para 44); and
extracting an aberration correction map (“the disclosed system and method of holographic image processing with phase error compensation models phase aberrations during a calibration stage”, para 43, “phase error map unit 620”, para 68) of the holographic optical system (hologram imaging arrangement, para 49) using the first CGH dataset.
Regarding Claim 2, Supikov teaches the method of claim 1,
wherein generating the first CGH dataset (“a set 702 of at least three diffraction (or Fresnel lens) pattern images (or focal planes) f0, f1, and f2 respectively have three different focal lengths z0, z1, and z”, para 84, fig 7) comprises generating the first CGH dataset by optimizing CGHs generated from the respective images included in the image dataset through Stochastic Gradient Descent (SGD) (“using the diffraction pattern images 702 to form an SLM optical field. The SLM optical field is then input in a convolutional Fresnel propagation model that generates a sensor optical field for each or individual input image. A version of the sensor optical field, such as in the form of a model intensity image, may be input to the gradient descent-type converger unit 708 as the intensity model image”, para 85).
Regarding Claim 3, Supikov teaches the method of claim 2,
wherein generating the first CGH dataset (“a set 702 of at least three diffraction (or Fresnel lens) pattern images (or focal planes) f0, f1, and f2 respectively have three different focal lengths z0, z1, and z”, para 84, fig 7) comprises minimizing a first loss between images included in the image dataset and images reconstructed from the first CGH dataset corresponding to the respective images included in the image dataset (“The multiple or all corresponding pairs of model and captured intensity images may be input to a loss function to be minimized by the gradient descent-type operation, and may result in phase error values of phase error image 714”, para 85).
Regarding Claim 4, Supikov teaches the method of claim 1,
wherein extracting the aberration correction map includes extracting a local aberration correction map dataset using the first CGH dataset (“The multiple or all corresponding pairs of model and captured intensity images may be input to a loss function to be minimized by the gradient descent-type operation, and may result in phase error values of phase error image 714”, para 85); and
calculating a global aberration correction map based on the extracted local aberration correction map dataset (“The average phase errors over the multiple focal lengths are used to populate a phase error map that can be used during run-time”, para 85, phase error map is a global aberration correction map) (“the estimated pixel level noise values may be averaged, and then the computed mean noise may be subtracted from all or selected pixels on the average image Iia. The average background noise is subtracted from all pixel locations”, para 102, fig 8) (“The gradient descent algorithm can output an average deviation across the multiple focal planes as a phase error for a pixel location of the SLM lens relative to all (or substantially all) pixel locations on a display represented by the camera sensor array, para 45).
Regarding Claim 6, Supikov teaches the method of claim 4,
wherein calculating the global aberration correction map comprises calculating the global aberration correction map by averaging multiple local aberration correction maps (“The average phase errors over the multiple focal lengths are used to populate a phase error map that can be used during run-time”, para 85, phase error map is a global aberration correction map) (“the estimated pixel level noise values may be averaged, and then the computed mean noise may be subtracted from all or selected pixels on the average image Iia. The average background noise is subtracted from all pixel locations”, para 102, fig 8) (“The gradient descent algorithm can output an average deviation across the multiple focal planes as a phase error for a pixel location of the SLM lens relative to all (or substantially all) pixel locations on a display represented by the camera sensor array, para 45).
Regarding Claim 7, Supikov teaches the method of claim 4,
wherein calculating the global aberration correction map comprises calculating the global aberration correction map by applying a scale factor to multiple local aberration correction maps (“Each per-pixel phase error value in the phase error map may be wrapped to have a value of 0 to 2π to cover 360 degrees (a wavelength) so that negative sign values are not necessary. When required, the per-pixel phase error wrapped to 0 to 2π may be scaled by a known factor”, para 130).
Regarding Claim 8, Supikov teaches the method of claim 4,
wherein calculating the global aberration correction map comprises calculating the global aberration correction map by applying a weighted scale factors to multiple local aberration correction maps (“the loss function is the mean of mean square errors over output at each focal plane, new gradient is the mean of gradients of mean square errors at each plane, and new phase error is a weighted sum of the new gradient and the previous phase error that was used, thereby outputting a new phase error value for each focal plane”, para 74).
Regarding Claim 9, Supikov teaches the apparatus for extracting aberration of a holographic optical system (hologram imaging arrangement, para 49) based on image optimization, comprising:
memory (“Also at SLM control 102, the phase error map 622 may be placed in a memory 654 on or accessible to the SLM control 102”, para 82) in which at least one program is recorded; and
a processor for executing the program (“an example system 1100 for generating holographic images is arranged in accordance with at least some implementations of the present disclosure. System 1100 includes processor circuitry 1104”, fig 11, para 133),
wherein the program performs generating a first Computer-Generated Hologram (CGH) dataset (“the SLM control 102 may have a diffraction image pattern generator 652 that generates the diffraction pattern images upon receiving a target holographic image (or data therefore) 650 during a run-time”, para 76, diffraction image is the CGH, “a set 702 of at least three diffraction (or Fresnel lens) pattern images (or focal planes) f0, f1, and f2 respectively have three different focal lengths z0, z1, and z”, para 84, fig 7) by optimizing (“the holographic diffraction image or pattern is generated based on a constrained optimization problem solved by using computer generated hologram (CGH) algorithms”, para 33) each of images included in an image dataset (“during the calibration stage, light may be propagated from the SLM with at least three different focal lengths. By displaying at least three different images each at a different focal length from the SLM”, para 44), and
extracting an aberration correction map (“the disclosed system and method of holographic image processing with phase error compensation models phase aberrations during a calibration stage”, para 43, “phase error map unit 620”, para 68) of the holographic optical system (hologram imaging arrangement, para 49) using the first CGH dataset.
Regarding Claim 10, Supikov teaches the apparatus of claim 9,
wherein, when generating the first CGH dataset (“a set 702 of at least three diffraction (or Fresnel lens) pattern images (or focal planes) f0, f1, and f2 respectively have three different focal lengths z0, z1, and z”, para 84, fig 7), the program generates the first CGH dataset by optimizing CGHs generated from the respective images included in the image dataset through Stochastic Gradient Descent (SGD) (“using the diffraction pattern images 702 to form an SLM optical field. The SLM optical field is then input in a convolutional Fresnel propagation model that generates a sensor optical field for each or individual input image. A version of the sensor optical field, such as in the form of a model intensity image, may be input to the gradient descent-type converger unit 708 as the intensity model image”, para 85).
Regarding Claim 11, Supikov teaches the apparatus of claim 10,
wherein, when generating the first CGH dataset (“a set 702 of at least three diffraction (or Fresnel lens) pattern images (or focal planes) f0, f1, and f2 respectively have three different focal lengths z0, z1, and z”, para 84, fig 7), the program minimizes a first loss between images included in the image dataset and images reconstructed from the first CGH dataset corresponding to the respective images included in the image dataset (“The multiple or all corresponding pairs of model and captured intensity images may be input to a loss function to be minimized by the gradient descent-type operation, and may result in phase error values of phase error image 714”, para 85). .
Regarding Claim 12, Supikov teaches the apparatus of claim 9,
wherein extracting the aberration correction map, the program performs
extracting a local aberration correction map dataset using the first CGH dataset (“The multiple or all corresponding pairs of model and captured intensity images may be input to a loss function to be minimized by the gradient descent-type operation, and may result in phase error values of phase error image 714”, para 85); and
calculating a global aberration correction map based on the extracted local aberration correction map dataset (“The average phase errors over the multiple focal lengths are used to populate a phase error map that can be used during run-time”, para 85, phase error map is a global aberration correction map) (“the estimated pixel level noise values may be averaged, and then the computed mean noise may be subtracted from all or selected pixels on the average image Iia. The average background noise is subtracted from all pixel locations”, para 102, fig 8) (“The gradient descent algorithm can output an average deviation across the multiple focal planes as a phase error for a pixel location of the SLM lens relative to all (or substantially all) pixel locations on a display represented by the camera sensor array, para 45).
Regarding Claim 14, Supikov teaches the apparatus of claim 12,
wherein when calculating the global aberration correction map, the program calculates the global aberration correction map by averaging multiple local aberration correction maps (“The average phase errors over the multiple focal lengths are used to populate a phase error map that can be used during run-time”, para 85, phase error map is a global aberration correction map) (“the estimated pixel level noise values may be averaged, and then the computed mean noise may be subtracted from all or selected pixels on the average image Iia. The average background noise is subtracted from all pixel locations”, para 102, fig 8) (“The gradient descent algorithm can output an average deviation across the multiple focal planes as a phase error for a pixel location of the SLM lens relative to all (or substantially all) pixel locations on a display represented by the camera sensor array, para 45).
Regarding Claim 15, Supikov teaches the apparatus of claim 12,
wherein when calculating the global aberration correction map the program calculates the global aberration correction map by applying a scale factor to multiple local aberration correction maps (“Each per-pixel phase error value in the phase error map may be wrapped to have a value of 0 to 2π to cover 360 degrees (a wavelength) so that negative sign values are not necessary. When required, the per-pixel phase error wrapped to 0 to 2π may be scaled by a known factor”, para 130).
Regarding Claim 16, Supikov teaches the apparatus of claim 12,
wherein when calculating the global aberration correction map the program calculates the global aberration correction map by applying a weighted scale factors to multiple local aberration correction maps (“the loss function is the mean of mean square errors over output at each focal plane, new gradient is the mean of gradients of mean square errors at each plane, and new phase error is a weighted sum of the new gradient and the previous phase error that was used, thereby outputting a new phase error value for each focal plane”, para 74).
Regarding Claim 17, Supikov teaches (fig 1, 6A-9) a method (“for compensating for aberration of a holographic optical system (hologram imaging arrangement 100, para 49), comprising:
transforming an image into a Computer-Generated Hologram (CGH) (“The SLM control 102 may generate diffraction pattern image data using computer generated hologram (CGH) algorithms as discussed herein for displaying a corresponding hologram (or holographic image 118)”, para 51, “Process 900 may include “obtain target holographic image” 902’, para 127, “Process 900 may include “determine initial diffraction pattern image data” 904, and as mentioned above, may involve converting the target image into amplitude and phase profile channels, and inputting the phase values into a CGH algorithm to output initial phase values of the diffraction pattern”, para 128);
passing the CGH through a holographic display system (“The SLM control 102 may generate diffraction pattern image data using computer generated hologram (CGH) algorithms as discussed herein for displaying a corresponding hologram (or holographic image 118)”, para 51); and
generating an image by applying a previously detected aberration correction map to an image output from the holographic display system (“When the phase error map is to be used, process 900 may include “modify phases of the initial diffraction pattern image by using a phase error map to generate a phase-corrected diffraction pattern image” 906.”, para 130),
wherein the aberration correction map is a global aberration correction map of the holographic optical system extracted using a first CGH dataset in which respective images included in an image set are optimized.
Regarding Claim 18, Supikov teaches the method of claim 17,
wherein the global aberration correction map is an average of local aberration correction maps (“The average phase errors over the multiple focal lengths are used to populate a phase error map that can be used during run-time”, para 85, phase error map is a global aberration correction map) (“the estimated pixel level noise values may be averaged, and then the computed mean noise may be subtracted from all or selected pixels on the average image Iia. The average background noise is subtracted from all pixel locations”, para 102, fig 8) (“The gradient descent algorithm can output an average deviation across the multiple focal planes as a phase error for a pixel location of the SLM lens relative to all (or substantially all) pixel locations on a display represented by the camera sensor array, para 45).
Regarding Claim 19, Supikov teaches the method of claim 17,
wherein the global aberration correction map is calculated by applying a scale factor to multiple local aberration correction maps (“Each per-pixel phase error value in the phase error map may be wrapped to have a value of 0 to 2π to cover 360 degrees (a wavelength) so that negative sign values are not necessary. When required, the per-pixel phase error wrapped to 0 to 2π may be scaled by a known factor”, para 130).
Regarding Claim 20, Supikov teaches the method of claim 17,
wherein the global aberration correction map is calculated by applying a weighted scale factors to multiple local aberration correction maps (“the loss function is the mean of mean square errors over output at each focal plane, new gradient is the mean of gradients of mean square errors at each plane, and new phase error is a weighted sum of the new gradient and the previous phase error that was used, thereby outputting a new phase error value for each focal plane”, para 74).
Allowable Subject Matter
Claims 5,13 would be allowable if rewritten to overcome the rejection(s) under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), 2nd paragraph, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Claim 5 is allowable for at least the reason: “extracting the local aberration correction map dataset includes inputting a first CGH included in the first CGH dataset to an actual optical system and generating a second CGH in which optical aberration is corrected;
calculating a second loss between a numerical reconstructed image of the first CGH and an optical reconstructed image of the second CGH; optimizing the second CGH through Stochastic Gradient Descent (SGD) based on the second loss; and extracting a local aberration correction map based on the first CGH and the second CGH, and generating the local aberration correction map dataset comprises generating the local aberration correction map dataset by repeatedly performing generating the second CGH, calculating the second loss, optimizing the second CGH, and extracting the local aberration correction map for each of the first CGHs included in the first CGH dataset.”
Claim 13 is allowable for at least the reason: “wherein, when extracting the local aberration correction map dataset, the program performs inputting a first CGH included in the first CGH dataset to an actual optical system and generating a second CGH in which optical aberration is corrected; calculating a second loss between a numerical reconstructed image of the first CGH and an optical reconstructed image of the second CGH; optimizing the second CGH through Stochastic Gradient Descent (SGD) based on the second loss; and extracting a local aberration correction map based on the first CGH and the second CGH, and the program generates the local aberration correction map dataset by repeatedly performing generating the second CGH, calculating the second loss, optimizing the second CGH, and extracting the local aberration correction map for each of the first CGHs included in the first CGH dataset.”
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lee et al (US 2024/0192638) teaches a gradient descent based hologram optimization method.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JYOTSNA V DABBI whose telephone number is (571)270-3270. The examiner can normally be reached M-Fri: 9:00am-5:00pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, STEPHONE ALLEN can be reached at 571-272-2434. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/JYOTSNA V DABBI/Primary Examiner, Art Unit 2872 7/22/2026
Prosecution Insights