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 .
Notice to Applicants
This communication is in response to the application filed on 11/02/2023.
Claim 1-21 are pending.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “a spatial mode sorter that defines a configurable basis comprising a set of spatial modes on to which an incoming optical signal is projected” in claim 21 and “a control module configured to: receive respective optical signals … process information … configure the spatial mode sorter … provide an estimated measurement characterizing the distribution of one or more optical sources” in claim 21.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1, 3-8, 11-13, 15, 16, 18, 20 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Grace et al. (NPL - Approaching Quantum Limited Super-Resolution Imaging without Prior Knowledge of the Object Location) (hereafter, "Grace") in view of Dutton et al. (NPL - Attaining the quantum limit of superresolution in imaging an object’s length via predetection spatial-mode sorting) (hereafter, "Dutton").
Regarding claim 1, Grace teaches a method for imaging a distribution of one or more optical sources, the method comprising ([page 4, right column, lines 45-49] The total joint probability distribution for both stages is simply the product of the outcome distribution for the first stage (Eq. 9) and that of the second stage conditioned on the first (Eq. 12); [page 2, left column, lines 17-20] We analyzed the performance of our proposed receiver for two sub-Rayleigh estimation tasks, that of estimating the separation between two point sources): receiving respective optical signals from a spatial mode sorter during each of two or more detection intervals of time ([page 3, left column, lines 10-14] A binary SPADE (BSPADE) device, a special case of SPADE, couples a single "target" spatial mode to one detector and aggregates the complement of this mode into a second detector, resulting in two possible single-photon detection outcomes); after each of the two or more detection intervals of time ([page 2, right column, lines 8-14] For weakly radiating incoherent sources from which less than one photon is detected per coherence time interval, an intensity resolving measurement of the optical field can be modeled as generating i.i.d. single photon detection events with Poisson temporal statistics), processing information based at least in part on ([page 5, left column, lines 6-8] Eq. 13 takes into account the entire two-stage data set and can be used as a likelihood function to calculate the ML estimator
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; [page 9, right column, lines 30-37] The ultimate goal of the two-stage receiver design is to estimate the parameter of interest Ɵ, which can be achieved by numerical evaluation of Eq. 14. For the adaptive version of the two stage receiver, however, a further requirement is a calculation of the two-stage receiver estimator variance ... to optimize the measurement allocation parameter α): (1) the respective optical signal received in the corresponding detection interval of time, and (2) a first set of models comprising a set of distributions related to one or more optical sources, each model corresponding to a different number of optical sources in the distribution ([page 3, left column, lines 14-20] For a set of n such outcomes, the two integers n and k form a sufficient statistic, where k is the number of photons recorded by the target detector. For i.i.d. q-BSPADE measurements, where the qth spatial mode is designated as the target mode, the joint probability distribution governing n recorded photons is given by the binomial distribution), and configuring the spatial mode sorter based at least in part on the processing; and providing an estimated measurement characterizing the distribution of one or more optical sources based at least in part on the processed information ([page 3, left column, lines 14-18] For a set of n such outcomes, the two integers n and k form a sufficient statistic, where k is the number of photons recorded by the target detector. For i.i.d. q-BSPADE measurements, where the qth spatial mode is designated as the target mode; [page 6, left column, lines 5-7 & 12-14] FIG. 2. A. Variance of approximated ML estimator for two stage receiver as a function of the allocation parameter
α
when N = 100,000 ... C. Adaptive method for optimizing the design parameter
α
, with the an iteration of the optimization loop shown at time t); wherein the processing after at least one of the two or more detection intervals of time includes ([page 2, right column, lines 8-14] For weakly radiating incoherent sources from which less than one photon is detected per coherence time interval, an intensity resolving measurement of the optical field can be modeled as generating i.i.d. single photon detection events with Poisson temporal statistics).
Grace does not expressly teach computing an eigen-projection.
However, Dutton teaches computing an eigen-projection ([page 4, left column, lines 8-12 & 17-20] The symmetric logarithmic derivative L is given by the implicit relation ∂ρ1/∂θ = 1/2[ρ1L + Lρ1] and can be expressed as
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, where Dj and |ej> are the eigenvalues and eigenvectors of ρ … We calculate the QFI numerically by first expressing ρ1 in the {|φk} basis and then calculate the eigenbasis of ρ1 and the eigenvectors which span ∂ρ1/∂θ, then L, and finally the QFI).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method and device of Grace to incorporate the step/system of using projection operators onto its eigenbasis to calculate the symmetric logarithmic derivative (SLD) taught by Dutton.
The suggestion/motivation for doing so would have been to improve estimation fidelity by reducing the error associated with the length estimate ([page 1, lines 13-15] we quantify improvement in terms of the actual mean-square error of the length estimate using predetection mode sorting. We consider the effect of imperfect mode sorting and show that the performance improvement over direct detection is robust over a range of sub-Rayleigh lengths). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predicted results. Therefore, it would have been obvious to combine Grace and Dutton to obtain the invention as specified in claim 1.
Regarding claim 3, the combination of Grace and Dutton teaches all the limitations of claim 1 above. Grace teaches wherein the first set of models includes a set of spatial distributions for the optical sources ([page 3, left column, lines 14-20] For a set of n such outcomes, the two integers n and k form a sufficient statistic, where k is the number of photons recorded by the target detector. For i.i.d. q-BSPADE measurements, where the qth spatial mode is designated as the target mode, the joint probability distribution governing n recorded photons is given by the binomial distribution).
Regarding claim 4, the combination of Grace and Dutton teaches all the limitations of claim 3 above. Grace teaches wherein the first set of models further includes a set of Bayesian prior probability distributions for the set of spatial distributions ([page 8, right column, lines 40-45] our sequential estimation procedure could have easily been formulated in the Bayesian framework, and moving to a Bayesian estimation scheme will likely be fruitful for incorporating multiple adaptive measurements as well as partial prior information on object parameters; [page 11, right column, lines 10-13] If some a priori knowledge of θ is available, a Bayesian updating scheme could be used instead which takes into account both the prior information and the acquired data to perform the optimization).
Regarding claim 5, the combination of Grace and Dutton teaches all the limitations of claim 4 above. Grace teaches wherein the set of Bayesian prior probability distributions for the set of spatial distributions includes a set of Gaussian distributions ([page 3, left column, lines 51-53 and 57-59 & page 3, right column, lines 1-6] In a well calibrated imaging context, the static misalignment Ɛ is likely to be best described as a random variable with a known prior probability distribution p(Ɛ) ... In the present analysis, we assume for simplicity that Ɛs is the result of unbiased, symmetric misalignment processes inherent to the imaging receiver and model it as a Gaussian distributed random parameter with zero mean and real-valued variance σ2/s. For a given imaging trial, a distribution p(Ɛp) can be constructed using any available a priori information on the optimal object-plane alignment position ɸ and/or the results of any preliminary measurements).
Regarding claim 6, the combination of Grace and Dutton teaches all the limitations of claim 5 above. Grace teaches wherein a set of hyper-parameters for the Gaussian distributions are based at least in part on a result of an expectation maximization calculation that is based at least in part on the processed information ([page 7, right column, lines 48-49] The maximum likelihood estimator Ɵ̂ML can then be calculated using Eq. 14 in order to adaptively optimize α; [page 5, left column, lines 6-8] Eq. 13 takes into account the entire two-stage data set and can be used as a likelihood function to calculate the ML estimator
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).
Regarding claim 7, the combination of Grace and Dutton teaches all the limitations of claim 1 above. Grace teaches wherein the first set of models includes a set of brightness distributions for the optical sources ([page 2, right column, lines 27-30 and 37-40] the single-photon outcome probability density is given by the image-plane intensity distribution
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… Over n i.i.d. single photon arrival events, the joint probability density for the set of direct detection arrival positions becomes
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).
Regarding claim 8, the combination of Grace and Dutton teaches all the limitations of claim 7 above. Grace teaches wherein the first set of models further includes a set of Bayesian prior probability distributions for the set of brightness distributions ([page 11, right column, lines 10-13] If some a priori knowledge of θ is available, a Bayesian updating scheme could be used instead which takes into account both the prior information and the acquired data to perform the optimization; [page 2, right column, lines 46-49] A spatial mode demultiplexing (SPADE) measurement detects the intensity of the optical field after it is projected onto a discrete, orthonormal basis of spatial modes in the optical domain).
Regarding claim 11, the combination of Grace and Dutton teaches all the limitations of claim 1 above. Dutton teaches wherein the eigen-projection is the eigenvectors of a symmetric logarithmic derivative operator ([page 4, left column, lines 2-12] The total photon number N = n̄K. In the above model, n̄ can also be interpreted as the probability that a temporal mode of the collected light (over all spatial modes) has one photon ... The symmetric logarithmic derivative L is given by the implicit relation ∂ρ1/∂θ = 1/2[ρ1L + Lρ1] and can be expressed as
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, where Dj and |ej> are the eigenvalues and eigenvectors of ρ).
Regarding claim 12, the combination of Grace and Dutton teaches all the limitations of claim 11 above. Dutton teaches wherein the symmetric logarithmic derivative operator is based at least in part on a set of one or more operators constructed from a single-parameter inference setting ([page 4, left column, lines 2-12] The total photon number N = n̄K. In the above model, n̄ can also be interpreted as the probability that a temporal mode of the collected light (over all spatial modes) has one photon ... The symmetric logarithmic derivative L is given by the implicit relation ∂ρ1/∂θ = 1/2[ρ1L + Lρ1] and can be expressed as
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, where Dj and |ej> are the eigenvalues and eigenvectors of ρ).
Regarding claim 13, the combination of Grace and Dutton teaches all the limitations of claim 1 above. Dutton teaches wherein the eigen-projection is the eigenvectors of an operator constructed from a Bayesian inference setting ([page 4, left column, lines 2-12 & 17-20] The total photon number N = n̄K. In the above model, n̄ can also be interpreted as the probability that a temporal mode of the collected light (over all spatial modes) has one photon ... The symmetric logarithmic derivative L is given by the implicit relation ∂ρ1/∂θ = 1/2[ρ1L + Lρ1] and can be expressed as
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, where Dj and |ej> are the eigenvalues and eigenvectors of ρ … We calculate the QFI numerically by first expressing ρ1 in the {|φk} basis and then calculate the eigenbasis of ρ1 and the eigenvectors which span ∂ρ1/∂θ, then L, and finally the QFI).
Regarding claim 15, the combination of Grace and Dutton teaches all the limitations of claim 1 above. Dutton teaches wherein the eigen-projection comprises a Personick eigen-projection ([page 4, left column, lines 2-12 & 17-20] The total photon number N = n̄K. In the above model, n̄ can also be interpreted as the probability that a temporal mode of the collected light (over all spatial modes) has one photon ... The symmetric logarithmic derivative L is given by the implicit relation ∂ρ1/∂θ = 1/2[ρ1L + Lρ1] and can be expressed as
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, where Dj and |ej> are the eigenvalues and eigenvectors of ρ, i.e.,
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. We calculate the QFI numerically by first expressing ρ1 in the {|φk} basis and then calculate the eigenbasis of ρ1 and the eigenvectors which span ∂ρ1/∂θ, then L, and finally the QFI).
Regarding claim 16, the combination of Grace and Dutton teaches all the limitations of claim 1 above. Dutton teaches wherein the processed information includes a second set of models ([page 3, left column, lines 14-18] For a set of n such outcomes, the two integers n and k form a sufficient statistic, where k is the number of photons recorded by the target detector. For i.i.d. q-BSPADE measurements, where the qth spatial mode is designated as the target mode; [page 6, left column, lines 5-7 & 12-14] FIG. 2. A. Variance of approximated ML estimator for two stage receiver as a function of the allocation parameter
α
when N = 100,000 ... C. Adaptive method for optimizing the design parameter
α
, with the an iteration of the optimization loop shown at time t).
Regarding claim 18, the combination of Grace and Dutton teaches all the limitations of claim 1 above. Grace teaches wherein the processing after at one of the two or more detection intervals of time includes ([page 2, right column, lines 8-14] For weakly radiating incoherent sources from which less than one photon is detected per coherence time interval, an intensity resolving measurement of the optical field can be modeled as generating i.i.d. single photon detection events with Poisson temporal statistics).
Grace does not expressly teach computing a quantum Fisher information matrix associated with the respective optical signals.
However, Dutton teaches computing a quantum Fisher information matrix associated with the respective optical signals ([page 3, right column, lines 43-50] we wish to calculate the quantum Fisher information (QFI), the optimal performance attainable by any receiver. We follow a procedure similar to that in [3], but model the extended object as a collection of M equally spaced point emitters spanning the total angular length θ, each radiating incoherently but within a narrow band of W Hz around a center wavelength λ, and then take the limit of M →∞).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method and device of Grace to incorporate the step/system of calculating the quantum Fisher information with estimating multiple parameters simultaneously taught by Dutton.
Motivation for this combination has been stated in claim 1.
With respect to claim 20, arguments analogous to those presented for claim 1, are applicable.
With respect to claim 21, arguments analogous to those presented for claim 1, are applicable.
Claim 2 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Grace et al. (NPL - Approaching Quantum Limited Super-Resolution Imaging without Prior Knowledge of the Object Location) (hereafter, "Grace") in view of Dutton et al. (NPL - Attaining the quantum limit of superresolution in imaging an object’s length via predetection spatial-mode sorting) (hereafter, "Dutton") and further in view of Tsang et al. (NPL - Quantum Theory of Superresolution for Two Incoherent Optical Point Sources) (hereafter, "Tsang").
Regarding claim 2, the combination of Grace and Dutton teaches all the limitations of claim 1 above. The combination of Grace and Dutton does not expressly teach wherein the configuring after each of the two or more detection intervals of time configures the spatial mode sorter to project the respective optical signals onto a basis of the computed eigen-projection.
However, Tsang teaches wherein the configuring after each of the two or more detection intervals of time configures the spatial mode sorter to project the respective optical signals onto a basis of the computed eigen-projection ([page 4, right column, lines 12-28] we propose a discrimination in terms of the Hermite-Gaussian spatial modes to estimate the separation. Consider the basis {|ϕq>; q = 0, 1, …} with eigenkets given by
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(4.2), where Hq is the Hermite polynomial [37]. The POVM for each coherence time interval can be expressed as projections
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(4.3)).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method and device of Grace and Dutton to incorporate the step/system of configuring Hermite-Gaussian (HG) spatial modes based on eigenkets for POVM for each coherence time interval taught by Tsang.
The suggestion/motivation for doing so would have been to improve the localization precision ([page 1, lines 2-5] In the context of statistical image processing, violation of the criterion is especially detrimental to the estimation of the separation between the sources, and modern far-field superresolution techniques rely on suppressing the emission of close sources to enhance the localization precision; [page 6, right column, lines 36-39] Binary SPADE actually works less well when the sources are far apart, and the two methods can complement each other to enhance the localization precision). Further, one skilled in the art could have combined the elements as described above by known method with no change in their respective functions, and the combination would have yielded nothing more than predicted results. Therefore, it would have been obvious to combine Grace and Dutton with Tsang to obtain the invention as specified in claim 2.
Regarding claim 19, the combination of Grace and Dutton teaches all the limitations of claim 1 above. Grace teaches wherein the processing after at least one of the two or more detection intervals of time includes ([page 2, right column, lines 8-14] For weakly radiating incoherent sources from which less than one photon is detected per coherence time interval, an intensity resolving measurement of the optical field can be modeled as generating i.i.d. single photon detection events with Poisson temporal statistics).
Grace does not expressly teach computing a modified quantum Fisher information matrix derived in a Bayesian inference setting and associated with the respective optical signals.
However, Tsang teaches computing a modified quantum Fisher information matrix derived in a Bayesian inference setting and associated with the respective optical signals ([page 3, left column, lines 37-44] The ultimate performance of any quantum measurement and any unbiased estimator can be quantified using the quantum Cramér-Rao bound
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where K is the quantum Fisher information matrix in terms of ρ⊗M; [page 14, right column, lines 19-24] A Bayesian version of the Cramér-Rao bound can also be used to bound [11] the global or minimax error of any biased or unbiased estimator; the Fisher information still plays a decisive role in the Bayesian bound and its significance as a precision measure remains strong in Bayesian and minimax statistics).
It would have been obvious before the effective filing date of the claimed invention to one having ordinary skill in the art to modify the method and device of Grace and Dutton to incorporate the step/system of using Bayesian Cramér-Rao bounds and Quantum Fisher Information (QFI) for precision limits taught by Tsang.
Motivation for this combination has been stated in claim 2.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL C. CHANG whose telephone number is (571)270-1277. The examiner can normally be reached Monday-Thursday and Alternate Fridays 8:00-5:00.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Chan S. Park can be reached at (571) 272-7409. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DANIEL C CHANG/Examiner, Art Unit 2669 /CHAN S PARK/Supervisory Patent Examiner, Art Unit 2669