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 .
Claims 1-19 are pending regarding this application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 01/08/2025 is considered and attached.
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 2-3 are 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 2 recites “the camera” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. As such, claim 2 is rejected for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 3 recites “the camera” in lines 2-3. There is insufficient antecedent basis for this limitation in the claim. As such, claim 3 is rejected for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
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 (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 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.
Claims 1-3, 10-14, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over Magana-Loaiza et al. (U.S. Publication No. 2023/0375399 A1), hereinafter Magana-Loaiza in view of Larkin et al. (U.S. Publication No. 2010/0213389 A1), hereinafter Larkin.
Regarding claim 1, Magana-Loaiza teaches a method of super-resolving light sources (Magana-Loaiza, see FIG. 2) comprising the steps of:
obtaining a respective photon number distribution for each pixel of a spatial image obtained by a photon-number-resolving device (Magana-Loaiza teaches “a smart single-pixel camera with photon-number resolution enables the identification of the light sources illuminating an arbitrary object” in para. [0040] and FIG. 2, wherein the “smart quantum camera [has] the capability to identify photon statistics at each pixel” in para. [0062]. See para. [0066] wherein the photon statistics at each pixel include determining a photon-number distribution);
representing at least one of a plurality of light sources as spatial distributions of mode structures via a point-spread function (Magana-Loaiza teaches determining “inferred spatial distributions based on the experimental pixel-by-pixel imaging” as shown in FIGs. 15E and 15F which represent at least one light source(s) as shown in para. [0075]. The graphs depicted in FIGs. 15E and 15F are interpreted as equivalent to the claimed point-spread function. See also that, “at each pixel, the mean photon number for each source is provided by the Gaussian point spread function, which is then used to create the appropriate distinguishable probability distribution as given in equation (5), creating a 128×128 grid of photon number distributions” in para. [0082]. See also para. [0076] and para. [0081] wherein the graphs further represent mode structures); and
finding a configuration of light sources that best represents an observed plurality of photon number distributions of at least one of the plurality of pixels, (Magana-Loaiza teaches that “this approach enables the analytical description of the photon-number distribution pth−coh(n) associated to the detection of an arbitrary number of indistinguishable light sources” in para. [0066], wherein the method involves using “probability distributions for the identification of the multiple light sources using a supervised neural network” as shown in para. [0073]. Here, “a method for identification of light source types” is provided (see para. [0006]-[0008]). See also para. [0067]-[0072]).
While Magana-Loaiza teaches determining a number of specific types of light sources, Magana-Loaiza fails to specifically teach wherein the configuration includes at least one of a number or spatial position of the light sources in an object plane, since the total number of light sources (N) is an arbitrary number in the method of Magana-Loaiza.
However, Larkin teaches wherein the configuration includes at least one of a number or spatial position of the light sources in an object plane (While Magana-Loaiza teaches determining a number of specific types of light sources, and determining a configuration of multiple light sources that best represents an observed plurality of photon number distributions of at least one of the plurality of pixels, Larkin additionally teaches “a multi-variate probability distribution, which is rooted in a statistical analysis traditionally used to describe relationships between multiple statistical variables, may be used to localize the position in space and time of a light source, such as a diffraction-limited, sub-resolution point source of light” in para. [0045], wherein the probability distribution is a probability distribution of photons collected by the imaging system as shown in para. [0044]. Magana-Loaiza’s teaching of determining the types of light sources can be combined with Larkin’s teaching of determining a location of a light source to teach the above subject matter).
Magana-Loaiza and Larkin are both considered to be analogous to the claimed invention because they are in the same field of utilizing photon number distributions to determine aspects of light source(s). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Magana-Loaiza to incorporate the teachings of Larkin and include “wherein the configuration includes at least one of a number or spatial position of the light sources in an object plane”. The motivation for doing so would have been that “the PEDS technique can improve measurement efficiency and thereby extend experiment duration and/or improve temporal resolution, especially when the number of photons available to perform localization/tracking is limited”, as suggested by Larkin in para. [0059]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Magana-Loaiza with Larkin to obtain the invention specified in claim 1.
Regarding claim 2, Magana-Loaiza and Larkin teach the method of claim 1, further comprising the step of:
determining intensity information of each light source imaged by the camera (Magana-Loaiza teaches “the contour plot in FIG. 15A shows the combined intensity profile of the three partially distinguishable sources” in para. [0074]).
Regarding claim 3, Magana-Loaiza and Larkin teach the method of claim 1, further comprising the step of:
determining location information of each light source imaged by the camera (While Magana-Loaiza teaches determining a number of specific types of light sources, and determining a configuration of multiple light sources that best represents an observed plurality of photon number distributions of at least one of the plurality of pixels, Larkin additionally teaches “a multi-variate probability distribution, which is rooted in a statistical analysis traditionally used to describe relationships between multiple statistical variables, may be used to localize the position in space and time of a light source, such as a diffraction-limited, sub-resolution point source of light” in para. [0045], wherein the probability distribution is a probability distribution of photons collected by the imaging system as shown in para. [0044]. Magana-Loaiza’s teaching of determining the types of light sources can be combined with Larkin’s teaching of determining a location of a light source to teach the above subject matter).
Similar motivations as applied to claim 1 can be applied here to claim 3.
Regarding claim 10, Magana-Loaiza and Larkin teach the method of claim 1,
wherein the photon-number-resolving device is a photon number resolving camera (Magana-Loaiza teaches “a smart quantum camera with the capability to identify photon statistics at each pixel” in para. [0062]. See also FIGs. 13A and 13B).
Regarding claim 11, Magana-Loaiza and Larkin teach the method of claim 1,
wherein the photon-number-resolving device is a photon number resolving detector using raster scanning (Magana-Loaiza teaches “we image our target object onto a digital micro-mirror device (DMD) that is used to implement raster scanning. This is implemented by selectively turning on and off groups of pixels in our DMD. The light reflected of the DMD is measured by a single-proton detector that allows us to perform photon-number-resolving detection” in para. [0071]).
Regarding claim 12, Magana-Loaiza teaches a method of super-resolving light sources (Magana-Loaiza, see FIGs. 1 and 2) comprising the steps of:
obtaining photon number distributions for each pixel of a spatial image by a photon-number-resolving device (Magana-Loaiza teaches “a smart single-pixel camera with photon-number resolution enables the identification of the light sources illuminating an arbitrary object” in para. [0040] and FIG. 2, wherein the “smart quantum camera [has] the capability to identify photon statistics at each pixel” in para. [0062]. See para. [0066] wherein the photon statistics at each pixel include determining a photon-number distribution); and
resolving (Magana-Loaiza teaches determining “inferred spatial distributions based on the experimental pixel-by-pixel imaging” as shown in FIGs. 15E and 15F which represent multiple light sources as shown in para. [0075]. The graphs depicted in FIGs. 15E and 15F are interpreted as equivalent to the claimed point-spread function. See also that, “at each pixel, the mean photon number for each source is provided by the Gaussian point spread function, which is then used to create the appropriate distinguishable probability distribution as given in equation (5), creating a 128×128 grid of photon number distributions” in para. [0082]. See also that “the contour plot in FIG. 15A shows the combined intensity profile of the three partially distinguishable sources” in para. [0074]. See also para. [0082] and para. [0044] regarding resolving the intensities of the multiple light sources).
Magana-Loaiza fails to teach resolving positions of imaged light sources via analysis of joint spatial, photon-number-resolving data, and a point-spread function of the imaging system.
However, Larkin teaches resolving positions of imaged light sources via analysis of joint spatial, photon-number-resolving data, and a point-spread function of the imaging system (While Magana-Loaiza teaches resolving intensities of imaged light sources via analysis of joint spatial, photon-number-resolving data, and a point-spread function of the imaging system, Larkin additionally teaches “a multi-variate probability distribution, which is rooted in a statistical analysis traditionally used to describe relationships between multiple statistical variables, may be used to localize the position in space and time of a light source, such as a diffraction-limited, sub-resolution point source of light” in para. [0045], wherein the probability distribution is a probability distribution of photons collected by the imaging system as shown in para. [0044], wherein “the distribution of each photon in space is approximated based on a point spread function (PSF) of the optical system embodied in the instrumentation” as shown in para. [0032]. Magana-Loaiza’s teaching of determining the types of light sources can be combined with Larkin’s teaching of determining a location of a light source to teach the above subject matter).
Magana-Loaiza and Larkin are both considered to be analogous to the claimed invention because they are in the same field of utilizing photon number distributions to determine aspects of light source(s). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Magana-Loaiza to incorporate the teachings of Larkin and include “resolving positions of imaged light sources via analysis of joint spatial, photon-number-resolving data, and a point-spread function of the imaging system”. The motivation for doing so would have been that “the PEDS technique can improve measurement efficiency and thereby extend experiment duration and/or improve temporal resolution, especially when the number of photons available to perform localization/tracking is limited”, as suggested by Larkin in para. [0059]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Magana-Loaiza with Larkin to obtain the invention specified in claim 12.
Regarding claim 13, Magana-Loaiza and Larkin teach the method of claim 12,
wherein the photon-number-resolving device is a photon number resolving camera (Magana-Loaiza teaches “a smart quantum camera with the capability to identify photon statistics at each pixel” in para. [0062]. See also FIGs. 13A and 13B).
Regarding claim 14, Magana-Loaiza and Larkin teach the method of claim 12,
wherein the photon-number-resolving device is a photon number resolving detector using raster scanning (Magana-Loaiza teaches “we image our target object onto a digital micro-mirror device (DMD) that is used to implement raster scanning. This is implemented by selectively turning on and off groups of pixels in our DMD. The light reflected of the DMD is measured by a single-proton detector that allows us to perform photon-number-resolving detection” in para. [0071]).
Regarding claim 16, Magana-Loaiza teaches a super-resolution system for super-resolving light sources (Magana-Loaiza, see FIGs. 1 and 2) comprising:
a photon-number-resolving device (Magana-Loaiza teaches “a smart single-pixel camera with photon-number resolution enables the identification of the light sources illuminating an arbitrary object” in para. [0040] and FIG. 2);
obtain photon number distributions for each pixel of a spatial image from the photon-number-resolving device (Magana-Loaiza teaches “a smart single-pixel camera with photon-number resolution enables the identification of the light sources illuminating an arbitrary object” in para. [0040] and FIG. 2, wherein the “smart quantum camera [has] the capability to identify photon statistics at each pixel” in para. [0062]. See para. [0066] wherein the photon statistics at each pixel include determining a photon-number distribution); and
resolve (Magana-Loaiza teaches determining “inferred spatial distributions based on the experimental pixel-by-pixel imaging” as shown in FIGs. 15E and 15F which represent multiple light sources as shown in para. [0075]. The graphs depicted in FIGs. 15E and 15F are interpreted as equivalent to the claimed point-spread function. See also that, “at each pixel, the mean photon number for each source is provided by the Gaussian point spread function, which is then used to create the appropriate distinguishable probability distribution as given in equation (5), creating a 128×128 grid of photon number distributions” in para. [0082]. See also that “the contour plot in FIG. 15A shows the combined intensity profile of the three partially distinguishable sources” in para. [0074]. See also para. [0082] and para. [0044] regarding resolving the intensities of the multiple light sources).
Magana-Loaiza fails to teach a processor and resolving positions of imaged light sources via analysis of joint spatial, photon-number-resolving data, and a point-spread function of the imaging system.
However, Larkin teaches a processor (Larkin teaches “the methods described herein can be implemented by computer program instructions supplied to the processor of any type of computer to produce a machine with a processor that executes the instructions to implement the functions/acts specified herein” in para. [0041]);
resolving positions of imaged light sources via analysis of joint spatial, photon-number-resolving data, and a point-spread function of the imaging system (While Magana-Loaiza teaches resolving intensities of imaged light sources via analysis of joint spatial, photon-number-resolving data, and a point-spread function of the imaging system, Larkin additionally teaches “a multi-variate probability distribution, which is rooted in a statistical analysis traditionally used to describe relationships between multiple statistical variables, may be used to localize the position in space and time of a light source, such as a diffraction-limited, sub-resolution point source of light” in para. [0045], wherein the probability distribution is a probability distribution of photons collected by the imaging system as shown in para. [0044], wherein “the distribution of each photon in space is approximated based on a point spread function (PSF) of the optical system embodied in the instrumentation” as shown in para. [0032]. Magana-Loaiza’s teaching of determining the types of light sources can be combined with Larkin’s teaching of determining a location of a light source to teach the above subject matter).
Magana-Loaiza and Larkin are both considered to be analogous to the claimed invention because they are in the same field of utilizing photon number distributions to determine aspects of light source(s). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Magana-Loaiza to incorporate the teachings of Larkin and include “a processor; and resolving positions of imaged light sources via analysis of joint spatial, photon-number-resolving data, and a point-spread function of the imaging system”. The motivation for doing so would have been that “the PEDS technique can improve measurement efficiency and thereby extend experiment duration and/or improve temporal resolution, especially when the number of photons available to perform localization/tracking is limited”, as suggested by Larkin in para. [0059]. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Magana-Loaiza with Larkin to obtain the invention specified in claim 16.
Regarding claim 17, Magana-Loaiza and Larkin teach the super-resolution system of claim 16,
wherein the photon- number-resolving device is a photon number resolving camera (Magana-Loaiza teaches “a smart quantum camera with the capability to identify photon statistics at each pixel” in para. [0062]. See also FIGs. 13A and 13B).
Regarding claim 18, Magana-Loaiza and Larkin teach the super-resolution system of claim 16,
wherein the photon- number-resolving device is a photon number resolving detector using raster scanning (Magana-Loaiza teaches “we image our target object onto a digital micro-mirror device (DMD) that is used to implement raster scanning. This is implemented by selectively turning on and off groups of pixels in our DMD. The light reflected of the DMD is measured by a single-proton detector that allows us to perform photon-number-resolving detection” in para. [0071]).
Claims 4-6 and 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Magana-Loaiza et al. (U.S. Publication No. 2023/0375399 A1), hereinafter Magana-Loaiza in view of Larkin et al. (U.S. Publication No. 2010/0213389 A1), hereinafter Larkin and Burenkov et al. (“Full statistical mode reconstruction of a light field via a photon-number-resolved measurement”), hereinafter Burenkov.
Regarding claim 4, Magana-Loaiza and Larkin teach the method of claim 1, further comprising the step of:
determining a mode structure of each pixel (Magana-Loaiza teaches determining a mode structure in para. [0076] and [0081]).
Magana-Loaiza and Larkin fail to teach via a mode reconstruction algorithm applied to each pixel.
However, Burenkov teaches determining a mode structure via a mode reconstruction algorithm applied to each pixel (Burenkov teaches using a reconstruction algorithm to determine mode types of photons wherein the light source is unknown as shown in Section IV and Appendix B).
Magana-Loaiza, Larkin, and Burenkov are all considered to be analogous to the claimed invention because they are in the same field of utilizing photon number distributions to determine aspects of light source(s). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Magana-Loaiza (as modified by Larkin) to incorporate the teachings of Burenkov and include “determining a mode structure via a mode reconstruction algorithm applied to each pixel”. The motivation for doing so would have been that “not including all modes present in a reconstruction leads to significant errors in the entire set of recovered parameters. In the most general case, it is useful to establish a method to identify an a priori unknown mode structure based on a series of reconstructions, a situation typical for the experimental data”, as suggested by Burenkov in Appendix B. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Magana-Loaiza and Larkin with Burenkov to obtain the invention specified in claim 4.
Regarding claim 5, Magana-Loaiza, Larkin, and Burenkov teach the method of claim 4, wherein the mode reconstruction algorithm includes:
identifying a set of correlated and uncorrelated optical modes (Burenkov teaches calculating a joint probability distribution wherein a set of correlated and uncorrelated optical modes are identified as shown in Appendix A and Section II).
Similar motivations as applied to claim 4 can be applied here to claim 5.
Regarding claim 6, Magana-Loaiza, Larkin, and Burenkov teach the method of claim 5,
wherein the mode reconstruction algorithm includes identifying overall optical losses for conjugated fields (Burenkov teaches a mode reconstruction algorithm wherein “reconstruction of RPD is based on photon-number distribution given by 𝑃u(𝑀) from (1). The modes that are identified through an RPD analysis unambiguously define a mode structure of the overall CFs to within transmittance losses” as shown in Section II, such that “this method identifies the overall optical losses for conjugated fields” as shown in Section I).
Similar motivations as applied to claim 4 can be applied here to claim 6.
Regarding claim 8, Magana-Loaiza and Larkin teach the method of claim 1.
However, Magana-Loaiza fails to teach further comprising calculating a joint probability distribution using the equation:
PNG
media_image1.png
170
360
media_image1.png
Greyscale
where ns, ni are the number of photons detected in signal and idler arms, respectively, underlying modes have probability distributions
p
μ
n
for mean photon numbers
μ
,
L
n
,
k
η
=
η
η
1
-
η
k
-
n
k
!
/
[
k
-
n
!
n
!
]
are loss probability factors that compute a probability that
n
≤
k
photons are measured given transmittance η and
k
initial photons, and Pc and Pu are correlated and uncorrelated parts of the joint probability distribution, respectively.
However, Burenkov teaches further comprising calculating a joint probability distribution using the equation:
PNG
media_image1.png
170
360
media_image1.png
Greyscale
where ns, ni are the number of photons detected in signal and idler arms, respectively, underlying modes have probability distributions
p
μ
n
for mean photon numbers
μ
,
L
n
,
k
η
=
η
η
1
-
η
k
-
n
k
!
/
[
k
-
n
!
n
!
]
are loss probability factors that compute a probability that
n
≤
k
photons are measured given transmittance η and
k
initial photons, and Pc and Pu are correlated and uncorrelated parts of the joint probability distribution, respectively (Burenkov teaches the exact above formula (see formula (1) in Section II). Burenkov additionally teaches the loss probability factors exactly as recited above in Appendix A).
Magana-Loaiza, Larkin, and Burenkov are all considered to be analogous to the claimed invention because they are in the same field of utilizing photon number distributions to determine aspects of light source(s). Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified the teachings of Magana-Loaiza (as modified by Larkin) to incorporate the teachings of Burenkov and include “further comprising calculating a joint probability distribution using the equation:
PNG
media_image1.png
170
360
media_image1.png
Greyscale
where ns, ni are the number of photons detected in signal and idler arms, respectively, underlying modes have probability distributions
p
μ
n
for mean photon numbers
μ
,
L
n
,
k
η
=
η
η
1
-
η
k
-
n
k
!
/
[
k
-
n
!
n
!
]
are loss probability factors that compute a probability that
n
≤
k
photons are measured given transmittance η and
k
initial photons, and Pc and Pu are correlated and uncorrelated parts of the joint probability distribution, respectively”. The motivation for doing so would have been that “not including all modes present in a reconstruction leads to significant errors in the entire set of recovered parameters. In the most general case, it is useful to establish a method to identify an a priori unknown mode structure based on a series of reconstructions, a situation typical for the experimental data”, as suggested by Burenkov in Appendix B. Therefore, it would have been obvious to one of ordinary skill at the time the invention was filed to combine Magana-Loaiza and Larkin with Burenkov to obtain the invention specified in claim 8.
Regarding claim 9, Magana-Loaiza, Larkin, and Burenkov teach the method of claim 8, further comprising the step of:
minimizing an error of nonlinear parametric fit of the joint probability distribution to determine the number and type of light sources imaged by the camera (Burenkov teaches “a nonlinear parametric fit could be employed on a measured JPD, to minimize ∑[(√𝑥𝑗−√𝑓𝑗)/𝜎𝑗]2, a typical scoring function, where 𝑥𝑗 is the original vector, 𝑓𝑗 is the fit vector, and 𝜎𝑗 is the uncertainties vector.” In Section II, wherein this process may be applied to a reconstruction of an unknown source wherein “the number and the type of modes is defined with no prior information” as shown in Section IV and Appendix B).
Similar motivations as applied to claim 8 can be applied here to claim 9.
Allowable Subject Matter
Claims 7, 15, and 19 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.
The following is a statement of reasons for the indication of allowable subject matter.
The best prior art of record is Magana-Loaiza, Larkin, and Burenkov. Prior art applied alone or in combination with fails to anticipate or render obvious claims 7, 15, and 19.
Claim 7
Regarding claim 7, dependent upon claim 1, Magana-Loaiza and Larkin teach the method of claim 1.
Magana-Loaiza further teaches determining the mean photon number at each pixel for each source.
However, neither Magana-Loaiza, nor Larkin, nor Burenkov, nor the combination, teaches identifying a number of sources by increasing number of sources in a fit model until the fit model returns one of the sources with extracted mean number of photons per unit of time per pixel that is below a user defined threshold.
Similar analysis is applicable to claims 15 and 19.
Conclusion
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/Kyla Guan-Ping Tiao Allen/
Examiner, Art Unit 2661
/COURTNEY JOAN NELSON/Primary Examiner, Art Unit 2661
Prosecution Insights