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 .
DETAILED ACTION
Claims status
Claims 1-15 are pending as the applicant filed Preliminary Amendment on 10/06/2023.
Claim Rejections - 35 USC § 112
2. 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 1-15 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.
Regarding claims 1-15, the term “aberration” is vague and a relative term that renders the claim indefinite. The term “aberration” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably appraised of the scope of the invention. An artisan doing measuring and testing would not know at what point “aberration” within the scope of the claim had been accomplished because nothing within the disclosure establishes when a sufficient “aberration” occurs.
Note: In view of the PTO compact prosecution, the Examiner notes that due to the indefiniteness issues described above all consideration of the merits of the claims in view of prior art is as best understood.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-15 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a light correction coefficient prediction method, comprising: acquiring an intensity distribution that is a distribution of intensities in a plurality of regions of interest within a predetermined range on an intensity image, the intensity image being obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient appears to be an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of calculating a comparison result between the intensity distribution and a target distribution to generate comparison data are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? The additional element of predicting a light correction coefficient, which is for performing aberration correction related to the light so that the intensity distribution approaches the target distribution, by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 1 not eligible.
Claim 7, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a light correction coefficient prediction device, comprising a processor configured to: acquire, for an intensity image obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient, an intensity distribution that is a distribution of intensities in a plurality of regions of interest within a predetermined range on the intensity image appears to be an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of calculate a comparison result between the intensity distribution and a target distribution to generate comparison data are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? The additional element of predict a light correction coefficient, which is for performing aberration correction related to the light so that the intensity distribution approaches the target distribution, by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 7 not eligible.
Claim 13, Step 1 the claim is a process (or machine) (Yes),
Step 2A Prong One, does the claim recite an abstract idea? current claim related to a machine learning method, comprising: acquiring an intensity distribution that is a distribution of intensities in a plurality of regions of interest within a predetermined range on an intensity image, the intensity image being obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient appears to be an abstract idea of mental process (MPEP 2106.04(a)) or data gathering equivalent to mathematical concept or mathematical manipulation function (MPEP 2106.04 (a) (2) (concept need not be expressed in mathematical symbols, because "[w]ords used in a claim operating on data to solve a problem can serve the same purpose as a formula), (OR Mathematical Concepts and Mental Processes) Step 2A Prong One: Yes.
Step 2A Prong Two, is the claim directed to an abstract idea? In other words, does claim recite additional elements that integrate the Judicial Exception into a practical application? the additional elements of calculating a comparison result between the intensity distribution and a target distribution to generate comparison data are recited at a high level of generality and merely amount to a particular field of use (see MPEP 2106.05(h)) and/or insignificant post-solution activity (MPEP 2106.05(g)), this does not integrate the Judicial Exception into a practical application,
Step 2A Prong Two: NO.
Step 2B, Does the claim recite additional element that amount to significantly more than the Judicial exception? The additional element of training a learning model to output a light correction coefficient, which is for performing aberration correction related to the light so that the intensity distribution approaches the target distribution, by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to the learning model appears to be field of use (See MPEP 2106.05(h) and MPEP 2106.05(f)) and/or merely amounts to insignificant extra-solution output of the results (see MPEP 2106.05(g)) and therefore fails to integrate the abstract idea into a practical application or amount to significantly more. Step 2B: No. claim 13 not eligible.
Claim 2 related to
appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible.
Claim 2 related to wherein the light correction coefficient is a coefficient of a Zernike polynomial to give a wavefront shape of the light appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 2 not eligible.
Claim 3 related to wherein the intensity distribution is acquired as a brightness distribution by calculating a sum of brightness values of pixels for each of the plurality of regions of interest appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 3 not eligible.
Claim 4 related to wherein the intensity distribution is acquired as a distribution in the plurality of regions of interest set by sequential shifting along a direction set in advance on the intensity image appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 4 not eligible.
Claim 5 related to wherein a parameter affecting aberrations related to the light is input to the learning model in addition to the light correction coefficient and the comparison data appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 5 not eligible.
Claim 6 related to wherein an adjustable parameter affecting aberrations related to the light is further predicted by using the learning model appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 6 not eligible.
Claim 8 related to wherein the light correction coefficient is a coefficient of a Zernike polynomial to give a wavefront shape of the light appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 8 not eligible.
Claim 9 related to wherein the intensity distribution is acquired as a brightness distribution by calculating a sum of brightness values of pixels for each of the plurality of regions of interest appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 9 not eligible.
Claim 10 related to wherein the intensity distribution is acquired as a distribution in the plurality of regions of interest set by sequential shifting along a direction set in advance on the intensity image appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 10 not eligible.
Claim 11 related to wherein a parameter affecting aberrations related to the light is input to the learning model in addition to the light correction coefficient and the comparison data appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 11 not eligible.
Claim 12 related to wherein an adjustable parameter affecting aberrations related to the light is further predicted by using the learning model appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 12 not eligible.
Claim 14 related to acquiring an intensity distribution that is a distribution of intensities in a plurality of regions of interest within a predetermined range on an intensity image, the intensity image being obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient, calculating a comparison result between the intensity distribution and a target distribution to generate comparison data; and concatenating the comparison data and the light correction coefficient, which is a basis of the intensity distribution appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 14 not eligible.
Claim 15 related to a trained learning model built by training using the machine learning appears recite further data characterization and mathematical concepts that are part of the abstract idea, claim 15 not eligible.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1 and 3-15/are rejected under 35 U.S.C. 102 (a) (1) as being anticipated by Ferreira et al. (US Patent Application Publication 2020/0284883 A1, Date Published: 2020-09-10)
Regarding claim 1:
Ferreira described a light correction coefficient prediction method, comprising:
acquiring an intensity distribution that is a distribution of intensities in a plurality of regions of interest within a predetermined range on an intensity image (0035, alternating of color or wavelength emission or intensity or beam angle LIDAR detection, 0305, image including a waveform), the intensity image being obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient (0443, correct object recognition using LIDAR sensor),
calculating a comparison result between the intensity distribution and a target distribution to generate comparison data (0506, compared with analog signal processing may be seen in its inherent robustness against external noise coupling and the inherent robustness of digital circuits against process variations); and predicting a light correction coefficient, which is for performing aberration correction related to the light so that the intensity distribution approaches the target distribution, by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model (2313, prediction methods like a Bayesian inference method, 3539, prediction-based algorithm may be employed. In such algorithm, data streams may be analyzed to predict next symbols, 3589, a reference LIDAR signal may be stored as a transformed learning vector, to be used in combination with a machine learning approach).
Regarding claim 7:
Ferreira described a light correction coefficient prediction device, comprising a processor configured to (0480, host processor): acquire, for an intensity image obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient, an intensity distribution that is a distribution of intensities in a plurality of regions of interest within a predetermined range on the intensity image (0035, alternating of color or wavelength emission or intensity or beam angle LIDAR detection, 0305, image including a waveform, 0046, light module for operating); calculate a comparison result between the intensity distribution and a target distribution to generate comparison data (0506, compared with analog signal processing may be seen in its inherent robustness against external noise coupling and the inherent robustness of digital circuits against process variations); and
predict a light correction coefficient, which is for performing aberration correction related to the light so that the intensity distribution approaches the target distribution, by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model (2313, prediction methods like a Bayesian inference method, 3539, prediction-based algorithm may be employed. In such algorithm, data streams may be analyzed to predict next symbols, 3589, a reference LIDAR signal may be stored as a transformed learning vector, to be used in combination with a machine learning approach).
Regarding claim 13:
Ferreira described a machine learning method (3589, a reference LIDAR signal may be stored as a transformed learning vector, to be used in combination with a machine learning approach), comprising: acquiring an intensity distribution that is a distribution of intensities in a plurality of regions of interest within a predetermined range on an intensity image, the intensity image being obtained by observing an action caused by light corrected using a light
modulator based on a light correction coefficient (0035, alternating of color or wavelength emission or intensity or beam angle LIDAR detection, 0305, image including a waveform, 0046, light module for operating)
calculating a comparison result between the intensity distribution and a target distribution to generate comparison data (0506, compared with analog signal processing may be seen in its inherent robustness against external noise coupling and the inherent robustness of digital circuits against process variations); and training a learning model to output a light correction coefficient, which is for performing aberration correction related to the light so that the intensity distribution approaches the target distribution, by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to the learning model (2313, prediction methods like a Bayesian inference method, 3539, prediction-based algorithm may be employed. In such algorithm, data streams may be analyzed to predict next symbols, 3589, a reference LIDAR signal may be stored as a transformed learning vector, to be used in combination with a machine learning approach).
Regarding claim 3, Ferreira further described wherein the intensity distribution is acquired as a brightness distribution by calculating a sum of brightness values of pixels for each of the plurality of regions of interest (2302, brightness, also in contrast to the adjacent brightness conditions, light color and/or light modulation).
Regarding claim 4, Ferreira further described wherein the intensity distribution is acquired as a distribution in the plurality of regions of interest set by sequential shifting along a direction set in advance on the intensity image (0035, intensity or beam angle, 2349, read outs can be differentiated in a time sequential manner).
Regarding claim 5, Ferreira further described wherein a parameter affecting aberrations related to the light is input to the learning model in addition to the light correction coefficient and the comparison data (0585, compare the electrical voltage read from the energy storage circuit).
Regarding claim 6, Ferreira further described wherein an adjustable parameter affecting aberrations related to the light is further predicted by using the learning model (0043, allow adjusting for instance, laser pulse shape, temporal length, rise- and fall times, polarization, laser power, laser type, 0084, to train machine learning algorithms).
Regarding claim 8, Ferreira further described the light correction coefficient prediction device (0443, combined distance measurement thus increasing the likeliness of a correct object recognition.)
Regarding claim 9, Ferreira further described wherein the intensity distribution is acquired as a brightness distribution by calculating a sum of brightness values of pixels for each of the plurality of regions of interest (2302, brightness, also in contrast to the adjacent brightness conditions, light color and/or light modulation).
Regarding claim 10, Ferreira further described wherein the intensity distribution is acquired as a distribution in the plurality of regions of interest set by sequential shifting along a direction set in advance on the intensity image (0035, intensity or beam angle, 2349, read outs can be differentiated in a time sequential manner).
Regarding claim 11, Ferreira further described wherein a parameter affecting aberrations related to the light is input to the learning model in addition to the light correction coefficient and the comparison data (0585, compare the electrical voltage read from the energy storage circuit).
Regarding claim 12, Ferreira further described wherein an adjustable parameter affecting aberrations related to the light is further predicted by using the learning model (0043, allow adjusting for instance, laser pulse shape, temporal length, rise- and fall times, polarization, laser power, laser type, 0084, to train machine learning algorithms).
Regarding claim 14, Ferreira further described acquiring an intensity distribution that is a distribution of intensities in a plurality of regions of interest within a predetermined range on an intensity image (0035, alternating of color or wavelength emission or intensity or beam angle LIDAR detection, 0305, image including a waveform, 0046, light module for operating), the intensity image being obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient (0035, alternating of color or wavelength emission or intensity or beam angle LIDAR detection, 0305, image including a waveform, 0046, light module for operating), calculating a comparison result between the intensity distribution and a target distribution to generate comparison data (0585, compare the electrical voltage read from the energy storage circuit); and concatenating the comparison data and the light correction coefficient, which is a basis of the intensity distribution (5368, a LIDAR beam wavelength, an intensity, 6131, link to the previous block).
Regarding claim 15, Ferreira further described a trained learning model built by training using the machine learning (2313, prediction methods like a Bayesian inference method, 3539, prediction-based algorithm may be employed. In such algorithm, data streams may be analyzed to predict next symbols, 3589, a reference LIDAR signal may be stored as a transformed learning vector, to be used in combination with a machine learning approach).
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ferreira et al. (US Patent Application Publication 2020/0284883 A1, Date Published: 2020-09-10) in view of GONG, CN 106873152 A, Date Published:
2017-06-20, CPC G06N 20/00.
Regarding claim 2, Ferreira further described wherein the light correction coefficient is a coefficient to give a wavefront shape of the light (3150, nonzero coefficients, 3408, corrections from the predefined route, 3619, shape of at least a portion of the at least one event time series based on the one or more reference LIDAR signals).
Ferreira further does not describe Zernike polynomial.
GONG described Zernike polynomial (page 2, Zernike polynomial reconstruction) so high-speed correction can obtain (page 2).
It would have been obvious to one of ordinary skill in the art at the time the invention was made to modify Ferreira to have the Zernike polynomial taught by GONG so high-speed correction can obtain.
Contact information
6. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Tung Lau whose telephone number is (571)272-2274, email is Tungs.lau@uspto.gov. The examiner can normally be reached on Tuesday-Friday 7:00 AM-5:00 PM EST.
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/TUNG S LAU/Primary Examiner, Art Unit 2857
Technology Center 2800
February 23, 2026