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 that application claims priority to foreign application with application number KOREA, REPUBLIC OF 10-2024-0150359 dated 10/30/2024.
Copies of certified papers required by 37 CFR 1.55 have been received. Priority is acknowledged under 35 USC 119(e) and 37 CFR 1.78.
Information Disclosure Statement
The IDS(s) dated 01/06/2025 has been considered and placed in the application file.
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 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), 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):
(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.
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). The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f), is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
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), 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:
“an input processing unit” in claim 1-2;
“a neural network processing unit” in claim 1 and 3-7; and
“a scene reconstruction unit” in claim 1 and 8.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f), they 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), applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) (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).
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.
Claim(s) 6-8 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(s) 6-7 recites the limitation "the hidden object". There is insufficient antecedent basis for this limitation in the claim.
Claim(s) 8 recites the limitation “the frequency band” and "the object". There is insufficient antecedent basis for this limitation in the claim.
Appropriate correction is required.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-6 and 8-9 is/are rejected under 35 U.S.C. 103 as obvious over Li et al (Li, Y., Zhang, Y., Ye, J., Xu, F., & Xiong, Z. (2023). Deep non-line-of-sight imaging from under-scanning measurements. Advances in Neural Information Processing Systems, 36, 59095-59106., hereafter referred to as Li) in view of Sun et al (Sun, S., Li, Y., Zhang, Y., & Xiong, Z. (2024). Generalizable non-line-of-sight imaging with learnable physical priors. arXiv preprint arXiv:2409.14011., hereafter referred to as Sun).
Claim 1
Regarding Claim 1, Li teaches An artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device, comprising:
an input processing unit configured to sample a partial video from an original image (Li in §3, p.3, Eq. (3) discloses an under-scanning transient measurement and a corresponding sufficient-scanning transient measurement; ¶ discloses extracting selected scanning points from initial full transient data to generate 32x32, 16x16, 8x8 under-scanning spatial-temporal measurements. Under BRI, the initial full spatial-temporal measurement is the “original image” and the extracted under-scanning measurement is the “partial video”);
(Li in §4.1, pp. 4-5, Figure 2(b) discloses a time-domain transient recovery network having multiple 3D residual blocks that process spatial-temporal transient data).
Li does not explicitly teach all of a neural network processing unit configured to generate a frequency-converted video for the partial video and input the frequency-converted video to time and frequency domain networks to generate a predicted phasor field); and a scene reconstruction unit configured to reconstruct a hidden scene based on the predicted phasor field.
However, Sun teaches a neural network processing unit configured to generate a frequency-converted video for the partial video (Sun in §3.1, p.4, Eq. (2) discloses a Fourier-domain Gaussian illumination phase field; §3.4, p.6, Eq. (9) discloses convolving a transient feature with that illumination phase field according to Eq. (9), thereby using frequency conversion and inverse conversion to generate a spatial-temporal phasor-convolved feature)
and input the frequency-converted video to time and frequency domain networks to generate a predicted phasor field (Sun in §3.4, p.5-6, Figure 4 discloses temporally transforming transient features into the frequency domain, convolving Fourier features across spatial and spectrum components, predicting a Gaussian-window standard deviation from the frequency representation, and generating an adaptive illumination phasor field); and
a scene reconstruction unit configured to reconstruct a hidden scene based on the predicted phasor field (Sun in §3.1, p.4, Eq. (3) discloses reconstructing a point of a hidden object from a phase field using wave-propagation function modeled by the Rayleigh-Sommerfeld diffraction integral; §3.2, pp.4-5 discloses converting the phasor-processed feature into reconstructed intensity and depth images of the hidden scene).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li by incorporating FTT-illumination-function-IFFT phasor preprocessing, adaptive temporal-frequency convolution and phasor-field generation, and RSD propagation that is taught by Sun, since both reference are analogous art in the field of neural NLOS reconstruction from spatial-temporal transient measurements; thus, one of ordinary skilled in the art would be motivated to combine the references since Li’s under-scanning 3D residual transient-recovery framework with Sun’s adaptive phasor-field frequency processing and RSD reconstruction yields the predictable result of reconstructing a hidden volume from fewer, noise-affected scanning measurements using a learned frequency-selective phasor field, thereby reducing acquisition time and suppressing noise and background artifacts while improving reconstructed intensity, depth, position, and shape.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim 2
Regarding Claim 2, Li in view of Sun teaches The artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device of claim 1, wherein the input processing unit performs denoising through sensor noise simulation on the partial video to generate a denoised partial video (Sun in §3.1, p. 4, Eq. (4) discloses simulating ambient-light and background SPAD sensor noise by applying a Poisson distribution to transient measurements; §3.4, pp. 5-6, Figure 4 discloses an APF denoising module that selects the useful frequency spectrum, attenuates noise-bearing components, and outputs clean, denoised transient features).
Claim 3
Regarding Claim 3, Li in view of Sun teaches The artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device of claim 1, wherein
the neural network processing unit performs an input phasor convolution for generating the frequency-converted video by performing FFT transform, application of an illumination function, and IFFT transform on the partial video (Sun in §3.1, p. 4, Eq. (2) discloses an illumination phasor field represented as a Gaussian function in the Fourier domain; §3.4, p. 6, Eq. (9) discloses input phasor convolution according to Eq. (9), thereby applying Fourier conversion, the illumination phasor field, and inverse Fourier conversion to a transient feature to generate a temporal-domain output feature), and
the illumination function extracts a frequency band of interest by passing a specific frequency band in a frequency band of the partial video (Sun in §3.1, p. 4, Eq. (2) discloses that the Gaussian illumination phasor field has a pass-band width determined by its standard deviation; §3.4, pp. 5-6, Eqs. (6)-(8) discloses selecting an effective frequency spectrum band by predicting the Gaussian standard deviation and attenuating frequency components associated with noise).
Claim 4
Regarding Claim 4, Li in view of Sun teaches The artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device of claim 3, wherein the neural network processing unit inputs the frequency-converted video to the time domain network implemented as a residual block for temporal information processing, to generate a temporal information preserving image (Sun in §3.4, p. 6, Eq. (9) discloses that the inverse-transform output FA is a temporal-domain feature at each scanning point. Li in §4.1, pp. 4-5, Figure 2(b) discloses inputting spatial-temporal transient data to a TRN having four multiple-kernel 3D residual blocks and three further 3D residual blocks, each residual block including two 3D convolutions and a residual connection, to generate a recovered sufficient-scanning transient measurement that preserves time-of-flight histogram information).
Claim 5
Regarding Claim 5, Li in view of Sun teaches The artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device of claim 4, wherein the neural network processing unit inputs the temporal information preserving image to the frequency domain network implemented as a convolutional layer for processing frequency components of the temporal information preserving image, to generate a frequency information-processed video (Sun in §3.4, pp. 5-6, Figure 4 discloses transforming a transient feature into the frequency domain along the temporal dimension and successively convolving the resulting Fourier features across spatial and spectrum components to generate an enhanced frequency feature representation).
Claim 6
Regarding Claim 6, Li in view of Sun teaches The artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device of claim 5, wherein the neural network processing unit extracts a frequency band of interest from the frequency information-processed video through target training, to generate the predicted phasor field for predicting the hidden object (Sun in §3.4, pp. 5-6, Figure 4, Eqs. (6)-(9) discloses predicting a Gaussian standard deviation from the convolved frequency representation, using that standard deviation to select the relevant pass band, and generating the adaptive illumination phasor field; §3.5, p. 6, Eq. (10)-(11) discloses end-to-end target training against ground-truth hidden-object intensity and depth, whereby the learned APF parameters are optimized to generate the phasor field used to predict the hidden object; Li in §4.3, p. 5, Eq. (4)-(5) discloses end-to-end supervised target training against recovered transient and hidden-object outputs).
Claim 8
Regarding Claim 8, Li in view of Sun teaches The artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device of claim 1, wherein the scene reconstruction unit determines a position and shape of the object through a Rayleigh-Sommerfeld diffraction (RSD) operation for the frequency band constituting the predicted phasor field, to restore the hidden scene (Sun in §3.1, p. 4, Eq. (2)-(3) discloses a frequency-banded illumination phasor field and reconstructing a point of the hidden object from the phasor field using a wave-propagation function modeled by the RSD integral; §§3.1 and 3.4, pp. 4-6, Figure 4 discloses predicting the Gaussian pass band and rendering hidden-object intensity and depth, which determine the object’s spatial position and shape).
Claim 9
Regarding Claim 9, Li teaches An artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction method performed in an artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device, the artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction method comprising:
an input processing unit step of sampling a partial video from an original image (Li in §3, p.3, Eq. (3) discloses an under-scanning transient measurement and a corresponding sufficient-scanning transient measurement; ¶ discloses extracting selected scanning points from initial full transient data to generate 32x32, 16x16, 8x8 under-scanning spatial-temporal measurements. Under BRI, the initial full spatial-temporal measurement is the “original image” and the extracted under-scanning measurement is the “partial video”);
(Li in §4.1, pp. 4-5, Figure 2(b) discloses a time-domain transient recovery network having multiple 3D residual blocks that process spatial-temporal transient data).
Li does not explicitly teach all of a neural network processing unit step of generating a frequency-converted video for the partial video and inputting the frequency-converted video to time and frequency domain networks to generate a predicted phasor field); and a scene reconstruction step of reconstructing a hidden scene based on the predicted phasor field.
However, Sun teaches a neural network processing unit step of generating a frequency-converted video for the partial video (Sun in §3.1, p.4, Eq. (2) discloses a Fourier-domain Gaussian illumination phase field; §3.4, p.6, Eq. (9) discloses convolving a transient feature with that illumination phase field according to Eq. (9), thereby using frequency conversion and inverse conversion to generate a spatial-temporal phasor-convolved feature)
and inputting the frequency-converted video to time and frequency domain networks to generate a predicted phasor field (Sun in §3.4, p.5-6, Figure 4 discloses temporally transforming transient features into the frequency domain, convolving Fourier features across spatial and spectrum components, predicting a Gaussian-window standard deviation from the frequency representation, and generating an adaptive illumination phasor field); and
a scene reconstruction unit step of reconstructing a hidden scene based on the predicted phasor field (Sun in §3.1, p.4, Eq. (3) discloses reconstructing a point of a hidden object from a phase field using wave-propagation function modeled by the Rayleigh-Sommerfeld diffraction integral; §3.2, pp.4-5 discloses converting the phasor-processed feature into reconstructed intensity and depth images of the hidden scene).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li by incorporating FTT-illumination-function-IFFT phasor preprocessing, adaptive temporal-frequency convolution and phasor-field generation, and RSD propagation that is taught by Sun, since both reference are analogous art in the field of neural NLOS reconstruction from spatial-temporal transient measurements; thus, one of ordinary skilled in the art would be motivated to combine the references since Li’s under-scanning 3D residual transient-recovery framework with Sun’s adaptive phasor-field frequency processing and RSD reconstruction yields the predictable result of reconstructing a hidden volume from fewer, noise-affected scanning measurements using a learned frequency-selective phasor field, thereby reducing acquisition time and suppressing noise and background artifacts while improving reconstructed intensity, depth, position, and shape.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as obvious over Li et al (Li, Y., Zhang, Y., Ye, J., Xu, F., & Xiong, Z. (2023). Deep non-line-of-sight imaging from under-scanning measurements. Advances in Neural Information Processing Systems, 36, 59095-59106., hereafter referred to as Li) in view of Sun et al (Sun, S., Li, Y., Zhang, Y., & Xiong, Z. (2024). Generalizable non-line-of-sight imaging with learnable physical priors. arXiv preprint arXiv:2409.14011., hereafter referred to as Sun), further in view of Meyer et al (Meyer, G. P. (2019). An alternative probabilistic interpretation of the huber loss. arXiv preprint arXiv:1911.02088., hereafter referred to as Meyer).
Claim 7
Regarding Claim 7, Li in view of Sun teaches The artificial intelligence-based non-line-of-sight (NLOS) imaging reconstruction device of claim 6, wherein the neural network processing unit implements the target training using a loss function for controlling outliers of the hidden object (Li in §4.3, p. 5, Eq. (5) discloses target training using L1 loss between predicted and ground-truth transient measurements and hidden-object intensity images; §5.3.1, pp. 6-7 discloses that its recovered transient histograms avoid the larger outliers produced by interpolation).
Li in view of Sun does not explicitly teach all of wherein the neural network processing unit implements the target training using a loss function for controlling outliers of the hidden object.
However, Meyer teaches wherein the neural network processing unit implements the target training using a loss function for controlling outliers of the hidden object (Meyer in Abstract, pp.1-2, §3.1 discloses L1 loss is less sensitive to outlier than L2 loss and that Huber loss is differentiable and robust to outliers).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to modify the invention of Li in view of Sun by incorporating an outlier-robust L1 or Huber target-training loss that is taught by Meyer, since both reference are analogous art in the field of supervised neural regression using noisy prediction-target data; thus, one of ordinary skilled in the art would be motivated to combine the references since Li in view of Sun’s transient, phasor, intensity, and depth prediction losses with Meyer’s outlier-robust loss formula yields the predictable result of reducing the influence of abnormally large training residuals, thereby stabilizing phasor-field training and improving robustness of hidden-object reconstruction.
Thus, the claimed subject matter would have been obvious to a person having ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JUSTIN P CASCAIS whose telephone number is (703) 756-5576. The examiner can normally be reached Monday-Friday 8:00-4:00.
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/J.P.C./Examiner, Art Unit 2674
/ONEAL R MISTRY/Supervisory Patent Examiner, Art Unit 2674
Date: 7/28/2026
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