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 .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 10/10/2024 was filed and is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Specification
The title of the invention is not descriptive. A new title is required that is clearly indicative of the invention to which the claims are directed.
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.
(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.
Claim(s) 1-9 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Capel (“An Effective Bail-out Test for RANSAC Consensus Scoring”, 2005).
Regarding claims 1, 8, 9, Capel teaches An information processing apparatus (Capel, pg 2, first full paragraph, reproduced below:
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. “Algorithm” is being interpreted to involve “an information processing apparatus”) comprising:
at least one memory storing instructions (Capel, see pg 2 image above, “algorithm” is being interpreted to involve “at least one memory storing instructions”); and
at least one processor (Capel, see pg 2 image above, “algorithm” is being interpreted to involve “at least one processor”) configured to execute the instructions to:
estimate, using sample points extracted from a plurality of data points (Capel, see pg 2 image above, “randomly selected subset of size d must be inliers”, which is being interpreted as extracted sample points from a plurality of data points), tentative geometric parameters (Capel, see pg 2 image above, “for a given hypothesis” is being interpreted to involve tentative geometric parameters; as RANSAC is used in computer vision problems that involve geometry (Abstract)) that fit the sample points (Capel, see pg 2 image above, “We propose a novel test…for a given hypothesis”. Which is being interpreted to involve testing the estimated tentative hypothesis for the sample points);
calculate errors (Capel, see Section 2, ¶3 image below: “d^2” or the “Sampson approximation to the squared F-manifold re-projection distance”, is being interpreted to involve calculating errors) between the plurality of data points and the tentative geometric parameters (Capel, pg 2, Section 2, ¶3, reproduced below:
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. “Sampson approximation to the squared F-manifold re-projection distance”. Is being interpreted to involve the tentative geometric parameters [hypothesis] and plurality of data points, the possible inliers);
calculate a boundary value (Capel, see equation 3 paragraphs image below, “a lower bound” is being interpreted as involving a boundary value that is calculated) for classifying (Capel, see equation 3 paragraphs image below, equation 3 is being interpreted as involved in classifying because the threshold shows at least two different classes: satisfying the threshold or going out of the threshold) a logarithmic space (Capel, see equation 3 paragraphs image below, the equation involving log functions is being interpreted as logarithmic space) of the calculated errors into two or more classes (Capel, see equation 3 paragraphs below, equation 3 is being interpreted as involved in classifying because the threshold shows at least two different classes: satisfying the threshold or going out of the threshold), and determine a threshold value by transforming the calculated boundary value (Capel, pg 4, paragraph before and after equation 3, reproduced below:
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. Equation 3 is being interpreted to involve a threshold value k that is required for at least one unpolluted basis. The log involving the lower bound is being interpreted as involving a transforming the calculated boundary value); and
count a number of inliers (Capel, pg 5, first two paragraphs, reproduced below:
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. “total inlier count” is being interpreted as “count a number of inliers”) for which the calculated errors are less than or equal to the calculated threshold value (Capel, see pg 5 first 2 paragraphs image above, determining if something is an inlier is being interpreted as “calculating errors less than or equal to the calculated threshold value”, otherwise all points would be inliers instead of a subset), and when the number of inliers counted increases (Capel, see pg 5 first 2 paragraphs image above, “If this probability is below a given threshold, we can safely abort further scoring of S”. Which shows that if it goes above the threshold, then the number of inliers counted increases, and scoring would continue), update current best geometric parameters (Capel, see pg 5 first 2 paragraphs image above, the scoring of hypothesis S is being interpreted to involve best geometric parameters that are updated) and a current best number of inliers (Capel, pg 5, two lines after equation 6, reproduced below:
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. Which shows current number of inliers, k is set to the best number of inliers) with the tentative geometric parameters (Capel, see pg 5 first 2 paragraphs image above, the new hypothesis S is being interpreted as involving “tentative geometric parameters”) and the number of inliers (Cape, see pg 5 two lines after equation 6 image above, k is being interpreted to involve “number of inliers”), respectively.
Regarding claim 2, Capel teaches The information processing apparatus according to claim 1, wherein the one or more processors further:
repeats a series of processing (Capel, see table 1 image below, the while loop shows “repeats a series of processing”) to be executed by the process of estimating the tentative geometric parameters (Capel, pg 3, Table 1, reproduced below:
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. “Hypothesis” is being interpreted as “tentative geometric parameters”.),
the process of calculating the errors (Capel, see Table 1 image above, row (3) of the while loop, “set of inliers” shows “calculating the errors”, to determine inliers and outliers),
the process of determining the threshold value (Capel, see Table 1 image above, T_inlier is an example threshold value that was determined), and
the process of updating the tentative geometric parameters (Capel, see Table 1 image above, the for loop after row 2 of the while loop: “For each hypothesis p”, which shows the hypothesis updates after each iteration of the loop) and the number of inliers (Capel, see Table 1 image above, “set of inliers” shows that inliers are counted)
until the number of repetitions reaches a preset upper limit value (Capel, see Table 1 image above, “n_max” variable shows an example number of repetitions reaches a preset upper limit value, as this is how the current while loop operates).
Regarding claim 3, Capel teaches The information processing apparatus according to claim 2, wherein the one or more processors further:
determines the threshold value (Capel, see nearest pg 3 image below, “Probability of sampling k basis sets all of which are polluted”, which is being interpreted as involving a threshold value to determine if data pollution occurs) from statistical amounts (Capel, pg 3, last paragraph to pg 4 line 1, reproduced below:
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. “Probability” shows “statistical amounts” and “probability distribution”) by fitting a predetermined probability distribution (Capel, pg 5, ¶2, line 2, “hypergeometric distribution”, which is being interpreted as a “predetermined probability distribution”) to the classified classes (Capel, pg 5, ¶2, line 2, “hypergeometric distribution”, which is being interpreted as a “predetermined probability distribution” as the inliers detected follow this distribution, which shows another class of outlier can be classified as a class).
Regarding claim 4, Capel teaches The information processing apparatus according to claim 3, wherein the one or more processors further:
if the calculated threshold value is in a range between a predetermined minimum threshold value and a predetermined maximum threshold value (Capel, pg 5, 2 lines after equation 6, reproduced below:
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. Which is being interpreted as having a k_n between min and max threshold value that causes the rejection hypothesis to be rejected instead of accepted),
the threshold value determining unit outputs the threshold value (Capel, see pg 5 nearest image above, the threshold value output is being interpreted to be part of the algorithm process of checking the output value), and
if the calculated threshold value is out of the range (Capel, pg 5, 2 lines after equation 6: “if k_n < k_min”, the rejection hypothesis is accepted and we can bail out”, which shows outside of the minimum threshold),
the threshold value determining unit outputs whichever of the minimum threshold value (Capel, pg 5, 2 lines after equation 6: “if k_n < k_min”, the rejection hypothesis is accepted and we can bail out”, which is being interpreted to involve outputting the threshold value, otherwise the algorithm wouldn’t know to trigger the if statement) or the maximum threshold value is closer to the calculated threshold value (Capel, see pg 5 nearest image above, which shows it is possible for a k_n to be above the maximum threshold value of k_best, and the algorithm would check the output result).
Regarding claim 5, Capel teaches The information processing apparatus according to claim 4, wherein the one or more processors further:
classifies the classes (Capel, see nearest pg 5 image below, the “rejection hypothesis” is being interpreted as involving classifies the classes) when the number of inliers whose errors are smaller than a previous maximum threshold value (Capel, see nearest pg 5 image below, “k is < k_best”, which are the total inlier count is less than best total inlier count, and k is being interpreted as smaller than a previous maximum, k_best, threshold value) or a predetermined threshold value is larger than a minimum number of samples (Capel, pg 5, 2 lines after equation 6, reproduced below:
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. Which shows that when k_n is greater than k_min, the rejection hypothesis is rejected instead. K_n is being interpreted as a predetermined threshold value is larger than a minimum number of samples, or k_min).
Regarding claim 6, Capel teaches The information processing apparatus according to claim 2, wherein the one or more processors further:
calculates, using a plurality of error functions (Capel, pg 5, equation 6, the HG functions are being interpreted as involving plurality of error functions), the errors (Capel, pg 5, the lines before equation 6, “cumulative density function as HG…the probability that k less than or equal k_0, is being interpreted as example error) between the plurality of data points (Capel, see nearest pg 5 image below, k_n is being interpreted as involving the plurality of data points) and the tentative geometric parameters for each error function (Capel, see nearest pg 5 image below, “hypothesis” is being interpreted to involve “tentative geometric parameters”),
determines the threshold value for each set of the calculated errors (Capel, equation 6, and the lines before it, show “a confidence P_conf”, which is being interpreted to involve an example error calculation to determine the threshold value), and
counts the number of inliers for each of the threshold values (Capel, pg 5, 2 lines after equation 6, reproduced below:
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.k_n is being interpreted as counter the number of inliers, as seen in the line before equation 6), and performs updating (Capel, see pg 5 nearest image above, the hypothesis acceptance or rejection is being interpreted as involving updating) when all (Capel, see nearest pg 5 image above, a value above or at k_best shows all inliers counted increase), at least one (Capel, see nearest pg 5 image above, the k_n value may become larger than k_min, which is being interpreted as at least one), or half or more of the numbers of inliers counted increase (Capel, see nearest pg 5 image above, which shows it is possible for k_n to be half or more number of inliers counted increase).
Regarding claim 7, Capel teaches The information processing apparatus according to claim 2, wherein the one or more processors further:
when the updating unit has updated the best geometric parameters (Capel, pg 6, Table 1, which shows the RANSAC algorithm with modifications that are being interpreted as part of the updating unit that updates the best geometric parameters, or the hypothesis that gets updated with iteration), the upper limit value of the number of repetitions (Capel, see nearest image below, n_max is the while loop end condition, or the upper limit value of the number of repetitions) is changed based on the ratio of inliers in the plurality of data points (Capel, pg 3, Table 1, row 5 of the while loop, reproduced below:
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; pg 3, 2nd to last line of the page, reproduced below:
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. The
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value is the fraction or ratio or inliers).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Raguram et al ("A Comparative Analysis of RANSAC Techniques Leading to Adaptive Real-Time Random Sample Consensus”, 2008) discloses RANSAC with improvements that involve log space classification (inlier).
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/J.B.D./Examiner, Art Unit 2667
/MATTHEW C BELLA/Supervisory Patent Examiner, Art Unit 2667
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