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 .
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.
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.
Claim 1 and analogous claims 7 and 13 are indefinite because the limitation reciting “predicting a light correction coefficient … by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model” is unclear. In particular, the claim appears to recite that the learning model predicts “a light correction coefficient” while also requiring input of “the light correction coefficient” to that same learning model. Thus, it is unclear whether the input light correction coefficient is the same coefficient being predicted, an initial or prior coefficient used to generate a different output coefficient, or merely a training label or other data item. Because the claim language does not clearly define the relationship between the recited input coefficient and the predicted coefficient, the scope of the claim is uncertain.
Claims 1, 7, and 13 are further indefinite because the phrase “an intensity image obtained by observing an action caused by light corrected using a light modulator” is unclear. The claim does not reasonably apprise one of ordinary skill in the art what “action” is being observed, how that action is “caused” by the corrected light, or how observing that action yields the recited intensity image. Functional or result-oriented language is not per se improper, but claim language must still define the scope with reasonable certainty.
Claim 5 and analogous claim 11 is further indefinite because it recites that the foregoing parameter is input to the learning model “in addition to the light correction coefficient and the comparison data,” while claim 1 and analogous claim 7 already leaves unclear what “the light correction coefficient” is in this context. Accordingly, claim 5 and analogous claim 11 inherit the ambiguity of claim 1 and compounds the uncertainty as to the actual model inputs.
Claim 14 is indefinite because the limitation “concatenating the comparison data and the light correction coefficient, which is a basis of the intensity distribution” is unclear does not provide reasonably clear boundaries for the role or identity of the recited light correction coefficient. Because the claim does not clearly define what coefficient is being concatenated or what makes that coefficient “a basis of” the intensity distribution, the scope of the claim is uncertain.
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 they are directed to an abstract idea without significantly more.
Regarding claim 1:
Step 1: is the claim directed to one of the four statutory categories?
Yes, the claim is directed to a method.
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitations: “obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient;” is directed to a mental process of observation under MPEP 2106.04(a)(2)(III).
Further, the limitation: “calculating a comparison result between the intensity distribution and a target distribution to generate comparison data;” is directed to a mathematical concept under MPEP 2106.04(a)(2)(I).
Further, the limitation: “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,” is directed to a mental process of judgment under MPEP 2106.04(a)(2)(III).
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitations: “acquiring an intensity distribution along a predetermined direction for an intensity image,” and “by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model” are directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitations: “acquiring an intensity distribution along a predetermined direction for an intensity image,” and “by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model” are directed to the well-understood, routine, and conventional activity of “Receiving or transmitting data over a network” under MPEP 2106.05(d).
Regarding claim 2:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The claim is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “The light correction coefficient prediction method according to wherein the light correction coefficient is a coefficient of a Zernike polynomial to give a wavefront shape of the light” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “The light correction coefficient prediction method according to wherein the light correction coefficient is a coefficient of a Zernike polynomial to give a wavefront shape of the light” is directed to field of use under MPEP 2106.05(h).
Regarding claim 3:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The claim is dependent on claim 1. Further, the limitation: “wherein the intensity distribution is acquired as a brightness distribution by projecting brightness values of pixels in the intensity image onto predetermined coordinates” is directed to a mathematical concept under MPEP 2106.04(a)(2)(I).
Regarding claim 4:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The claim is dependent on claim 1. Further, the limitation: “wherein the intensity distribution is acquired as a distribution of a sum of the brightness values of the pixels projected onto the predetermined coordinates” is directed to a mathematical concept under MPEP 2106.04(a)(2)(I).
Regarding claim 5:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The claim is dependent on claim 1.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “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” is directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “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” is directed to the well-understood, routine, and conventional activity of “Receiving or transmitting data over a network” under MPEP 2106.05(d).
Regarding claim 6:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “wherein an adjustable parameter affecting aberrations related to the light is further predicted by using the learning model” is directed to a mental process of observation under MPEP 2106.04(a)(2)(III).
Claim 7 is rejected with the same rationale as claim 1.
Claim 8 is rejected with the same rationale as claim 2.
Claim 9 is rejected with the same rationale as claim 3.
Claim 10 is rejected with the same rationale as claim 4.
Claim 11 is rejected with the same rationale as claim 5.
Claim 12 is rejected with the same rationale as claim 5.
Regarding claim 13:
Step 1: is the claim directed to one of the four statutory categories?
Yes, the claim is directed to a method.
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitations: “acquiring an intensity distribution along a predetermined direction for an intensity image obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient;” is directed to a mental process of observation under MPEP 2106.04(a)(2)(III); further, “calculating a comparison result between the intensity distribution and a target distribution to generate comparison data;” is directed to a mathematical concept under MPEP 2106.04(a)(2)(I).
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitations: “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,” is directed to field of use under MPEP 2106.05(h), further, the limitation: “by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to the learning model” is directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitations: “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,” is directed to field of use under MPEP 2106.05(h), further, the limitation: “by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to the learning model” is directed to the well-understood, routine, and conventional activity of “Receiving or transmitting data over a network” under MPEP 2106.05(d).
Regarding claim 14:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes. The limitation: “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” are directed to mathematical concepts under MPEP 2106.04(a)(2)(I).
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “acquiring an intensity distribution along a predetermined direction for an intensity image obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient;” is directed to mere data gathering under MPEP 2106.05(g).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “acquiring an intensity distribution along a predetermined direction for an intensity image obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient;” is directed to mere data gathering under MPEP 2106.05(g).
Regarding claim 15:
Step 2A, prong 1: Is the claim directed to a law of nature, a natural phenomenon, or an abstract
idea?
Yes, the claim is dependent on claim 13.
Step 2A, prong 2: Do the additional elements integrate into a practical application?
No. The limitation: “A trained learning model built by training using the machine learning method” is directed to field of use under MPEP 2106.05(h).
Step 2B: Does the claim recite additional elements that amount to significantly more than the
judicial exception?
No. The limitation: “A trained learning model built by training using the machine learning method” is directed to field of use under MPEP 2106.05(h).
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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-15 are rejected under 35 U.S.C. 103 as being unpatentable over US Pre-Grant Patent 2020/0116589 (Eguchi).
Regarding claim 1 and analogous claims 7 and 13:
Eguchi teaches:
1. acquiring an intensity distribution along a predetermined direction for an intensity image obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient;
(Eguchi, ¶0019)
“The image sensor 103 acquires a light intensity distribution of the optical image at each moved position, and stores the acquired light intensity distribution in the computer 105 or an unillustrated data storage [i.e. acquiring an intensity distribution along a predetermined direction for an intensity image obtained].”
“The computer 105 estimates the aberration of the target optical system 102 by post-processing a plurality of light intensity distributions [i.e. by observing an action caused by light corrected using a light modulator].”
(Eguchi, ¶0021)
“One post-processing method executed by the computer 105 is, for example, an optimization. The optimization estimates the aberration by sequentially changing the aberration so as to minimize the evaluation. In order to reduce the calculation load, it is possible to develop the aberration with an appropriate function and perform the optimization using the coefficient as an optimization variable. An aberration developing function is, for example, a Zernike polynomial [i.e. based on a light correction coefficient;].”
2. calculating a comparison result between the intensity distribution and a target distribution to generate comparison data;
(Eguchi, ¶0036)
“A differentiation of z on the right side can be approximated by a difference value between two intensity measurement values, and the intensity distribution on the left side can be approximated by an average value of the two intensity measurement values [i.e. calculating a comparison result between the intensity distribution and a target distribution to generate comparison data;].”
3. 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,
(Eguchi, ¶0032)
“In the step S102, the computer 105 acquires the approximated aberration [i.e. and predicting a light correction coefficient,]. The approximated aberration is an aberration acquired from the light intensity distribution without executing a calculation with a large load such as a repetitive calculation and roughly reproduces an outline of the aberration of the target optical system. The method for acquiring the approximated aberration can use, for example, a method of solving the TIE and a method of using a result of machine learning [i.e. which is for performing aberration correction related to the light so that the intensity distribution approaches the target distribution,].”
4. by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model.
(Eguchi, ¶0051)
“One method of acquiring the approximated aberration from a defocused image is a method of acquiring the approximated aberration using machine learning [i.e. by inputting the comparison data and the light correction coefficient, which is a basis of the intensity distribution, to a learning model].”
Regarding claim 2 and analogous claim 8:
Eguchi teaches:
1. according to wherein the light correction coefficient is a coefficient of a Zernike polynomial to give a wavefront shape of the light.
(Eguchi, ¶0020)
“Furthermore, the Seidel aberration can be measured by developing the wavefront aberration with a Zernike polynomial [i.e. to give a wavefront shape of the light].”
(Eguchi, ¶0023)
“In the step S1, the computer 105 determines an initial value of the optimization variable. When the aberration is developed with the Zernike polynomial, the initial value of the expansion coefficient may be determined [i.e. according to wherein the light correction coefficient is a coefficient of a Zernike polynomial].”
Regarding claim 5 and analogous claim 11:
Eguchi teaches:
1. 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.
(Eguchi, ¶0032)
“The method for acquiring the approximated aberration can use, for example, a method of solving the TIE and a method of using a result of machine learning.”
(Eguchi, ¶0034)
“In the step S104, the computer 105 determines an initial value using the aberration from which the aberration component caused by the error has been removed [i.e. 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].”
Regarding claim 6 and analogous claim 12:
Eguchi teaches:
1. wherein an adjustable parameter affecting aberrations related to the light is further predicted by using the learning model.
(Eguchi, ¶0046)
“FIG. 6 illustrates light intensity distributions used to obtain the approximated aberration according to this embodiment. Since the approximated aberration is acquired using the TIE, this embodiment provides a large defocus amount such that the image substantially reproduces the pupil shape [i.e. wherein an adjustable parameter affecting aberrations related to the light is further predicted by using the learning model].”
Regarding claim 14:
Eguchi teaches:
1. acquiring an intensity distribution along a predetermined direction for an intensity image obtained by observing an action caused by light corrected using a light modulator based on a light correction coefficient;
(Eguchi, ¶0019)
“The image sensor 103 acquires a light intensity distribution of the optical image at each moved position, and stores the acquired light intensity distribution in the computer 105 or an unillustrated data storage [i.e. acquiring an intensity distribution along a predetermined direction for an intensity image obtained].”
“The computer 105 estimates the aberration of the target optical system 102 by post-processing a plurality of light intensity distributions [i.e. by observing an action caused by light corrected using a light modulator].”
(Eguchi, ¶0021)
“One post-processing method executed by the computer 105 is, for example, an optimization. The optimization estimates the aberration by sequentially changing the aberration so as to minimize the evaluation. In order to reduce the calculation load, it is possible to develop the aberration with an appropriate function and perform the optimization using the coefficient as an optimization variable. An aberration developing function is, for example, a Zernike polynomial [i.e. based on a light correction coefficient;].”
2. calculating a comparison result between the intensity distribution and a target distribution to generate comparison data;
(Eguchi, ¶0036)
“A differentiation of z on the right side can be approximated by a difference value between two intensity measurement values, and the intensity distribution on the left side can be approximated by an average value of the two intensity measurement values [i.e. calculating a comparison result between the intensity distribution and a target distribution to generate comparison data;].”
3. concatenating the comparison data and the light correction coefficient, which is a basis of the intensity distribution.
(Eguchi, ¶0036)
“Here, ∇.sub.⊥ is a differential operator in the x and y directions, x and y express an orthogonal coordinate on a plane perpendicular to the optical axis, z is a coordinate in the optical axis direction, z.sub.0 is a measurement position, and I(x, y, z) and ϕ(x, y, z) are the light intensity distribution and phase distribution on the plane perpendicular to the optical axis at the position z, respectively, and λ is a wavelength [i.e. concatenating the comparison data and the light correction coefficient, which is a basis of the intensity distribution].”
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.
Claims 3-4, 9-10, and 15 are rejected under 35 U.S.C. 103 as being unpatentable over US Pre-Grant Patent 2020/0116589 (Eguchi) in view of US Pre-Grant Patent 2010/0265330 (Li et al; Li).
Regarding claim 3 and analogous claim 9:
Eguchi teaches the method of claim 1.
Eguchi does not explicitly teach:
1. wherein the intensity distribution is acquired as a brightness distribution by projecting brightness values of pixels in the intensity image onto predetermined coordinates.
Li teaches:
1. wherein the intensity distribution is acquired as a brightness distribution by projecting brightness values of pixels in the intensity image onto predetermined coordinates.
(Li, ¶0033)
“Two thresholds are used in such detection process: T.sub.0, which thresholds all pixels that form the blob, and T.sub.1, which thresholds the "core'" pixels within the blob [i.e. wherein the intensity distribution is acquired as a brightness distribution by projecting brightness values of pixels]. As the core part 402 is generally brighter than its surrounding part (which is referred to "halo" 404) within a blob, T.sub.1 is set to be slightly larger than T.sub.0. FIG. 3 shows an example of detected blobs (indicated by bounding boxes 302, 304, 306, 308) resulted from two headlights and their road reflections, and FIG. 4 shows the structure of a magnified blob [i.e. in the intensity image onto predetermined coordinates].”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Eguchi with Li. It would have been obvious to modify Eguchi such that the intensity distribution is acquired as brightness values of pixels projected onto predetermined coordinates, because Li teaches using both pixel-intensity information from all pixels within a detected blob and horizontal/vertical projection features of the blob as compact image features for machine-learning classification.
Regarding claim 4 and analogous claim 10:
Eguchi teaches the method of claim 1.
Eguchi does not explicitly teach:
1. wherein the intensity distribution is acquired as a distribution of a sum of the brightness values of the pixels projected onto the predetermined coordinates.
Li teaches:
(Li, ¶0041)
“The third category is "Shape Features", which describe a blob's shape in 20 different aspects. Specifically, they include: the area of the blob (in unit of pixels); the ratio of the blob area over the size of its bounding box; the aspect ratio and angle of the blob, which are calculated from the moments; the average and variance of the blob's radius; the average and variance of the blob's diameter; ratio of the major axis to the minor axis; four types of eccentricity features calculated from blob's major and minor axis information…”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Eguchi with Li. It would have been obvious to modify Eguchi such that the intensity distribution is acquired as brightness values of pixels projected onto predetermined coordinates, because Li teaches using both pixel-intensity information from all pixels within a detected blob and horizontal/vertical projection features of the blob as compact image features for machine-learning classification. Summing the brightness values of pixels projected onto predetermined coordinates would have been a predictable way to generate a brightness projection distribution from the image data for classification of light-object blobs.
Regarding claim 15:
Eguchi teaches the method of claim 13.
Eguchi does not explicitly teach:
1. A trained learning model built by training using the machine learning method according to claim 13.
Li teaches:
1. A trained learning model built by training using the machine learning method according to claim 13.
(Li, ¶0041)
“In this embodiment, a Support Vector Machine (SVM)-based learning approach is applied to recognize different types of image blobs, based on which a frame-level decision making process is carried out to determine the final beam state at real time.”
One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Eguchi with Li. One of ordinary skill in the art, at the time the invention was filed, would have been motivated to modify Eguchi with Li. It would have been obvious to modify Eguchi with the use of a SVM to better identify the blobs of pixel brightness data.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL JUSTIN BREENE whose telephone number is (571)272-6320. Examiner
interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-
based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO
Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael J Huntley can be reached on 303-297-4307. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786 9199 (IN USA OR CANADA) or 571-272-1000.
/P.J.B./ Examiner, Art Unit 2129
/MICHAEL J HUNTLEY/Supervisory Patent Examiner, Art Unit 2129
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