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 .
Remarks
This Office Action is responsive to Applicants' Amendment filed on July 17, 2026, in which claims 1, 6, 7, 10, 11, 17, and 20-23 are currently amended. Claims 2-5, 7-12, and 2-5 and 13-15 are canceled. Claims 1, 6-12, and 16-24 are currently pending.
Drawings
Applicant's amendments made to the drawings are acknowledged. The replacement drawings are low quality scans containing illegible elements. Examiner’s objection to the drawings is maintained.
Specification
Applicant's amendments made to the specification are acknowledged. Examiner’s objection to the specification are hereby withdrawn, as necessitated by Applicant’s amendments made to the specification.
Response to Arguments
The objection to claim 20 is hereby withdrawn, as necessitated by applicant's amendments and remarks made to the rejections.
Applicant’s arguments with respect to rejection of claim 1 under 35 U.S.C. 112(b) based on amendment have been considered, however, are not persuasive. Examiner notes that claim 1 introduces “a first AI classifier” twice such that it’s unclear if the second recitation of “a first AI classifier” is the same as the first recited “first AI classifier”
The rejections to claims 10, 11, 17, and 21 under 35 U.S.C. § 112(b) are hereby withdrawn, as necessitated by applicant's amendments and remarks made to the rejections.
Applicant’s arguments with respect to rejection of claims 1, 6-12, and 16-24 under 35 U.S.C. 103 based on amendment have been considered and are persuasive. The argument is moot in view of a new ground of rejection set forth below.
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.
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, 7-12, and 16-21 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 claim 1, "a first AI classifier" in "the positive data set by a first AI classifier" lacks antecedent basis. Claim 1 already introduces "A first Artificial Intelligence (AI) classifier)" such that it's unclear if "a first classifier" in "the positive data set by a first AI classifier" is the same AI classifier or a different one. In the interest of further examination the limitation is interpreted as "the positive data set by the first AI classifier."
The remaining claims 7-12 and 16-21 are rejected with respect to their dependence on the rejected claim 1.
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.
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.
Claims 1, 8, 9, 12, 16, 17, 18, 19, 20, 21, 22, 23, and 24 are rejected under U.S.C. §103 as being unpatentable over the combination of Bakalo (“A dual branch deep neural network for classification and detection in mammograms”, 2019) and Williams (US20150254555A1).
Regarding claim 1, Bakalo teaches A method for building and training a first Artificial Intelligence (AI) classifier for detecting an indicium of at least one of: a disease, a condition, and a radiologic finding in a radiologic image of a human or non-human animal subject, the method comprising:([p. 3 §III] "we propose a deep network architecture that classifies mammogram regions into three different classes: normal tissue, benign and malignant findings using labels at the image level" [p. 4] "we apply a transfer learning approach by using the pre-trained VGG128 network by [26], trained on the ImageNet dataset" A mammogram is a radiologic digital image and Bakalo's learned malignant classification is trained to identify malignant radiologic findings)
assembling a positive data set comprising a plurality of positive digital files, each of the positive digital files comprising a presence of the indicum, ([p. 7] "The dataset was composed of 2,967 mammograms with normal images as well as various benign and suspiciously malignant findings. In terms of the global image BI-RADS (Breast Imaging Reporting and Data System), we had 350, 2,364, 146 and 107 corresponding to BI-RADS 1, 2, 4 and 5 captured from 65, 693, 81 and 62 individuals respectively [...] BI-RADS 4 & 5 defined as malignant (M) [...] a large positive set [...] BI RADS 4, 5 and 6 as positive" BI-RADS 4/5 constitute digital images assessed as malignant and thus contain the claimed target indicium for Bakalo's classification task.)
and obtaining positive evaluation results by processing the positive data set by a first AI classifier thereby training the first AI classifier for positive data; ([Abstract] "The network provides a global classification of the image into multiple classes, such as malignant, benign or normal" [p. 5] "mammography images {x(1),...,x(n)}. Each image x(t) consists of regions {r1(t),...,rm(t)} and is associated with a binary tuple label (yM(t),yB(t)) that indicates whether the image contains at least one malignant and/or one benign finding respectively. A normal case will have (0,0) label whereas a mammogram with both M and B finding will be labeled (1,1). The network provides soft decisions for each image x(t) regarding the values of yM(t) and yB(t)" See also FIG. 2. For malignant-positive images, the network evaluates the malignant probability 𝑝(𝑦𝑀 = 1|𝑥) and that probability contributes to the likelihood optimized during training. Thus positive mammograms are processed by the same malignant detection/classification network and affect its learned parameters.)
identifying, in a plurality of candidate digital files for each of which an absence of the indicium has been assessed, ([p. 7] "BI-RADS 2 as benign (B) and BI-RADS 1 as normal (N) […] To this end we used p(yM = 1|x) scoring for M vs. B ∪ N (M vs. BN)" Bakalo explicitly treats benign and normal images as the opposing, non-malignant class in the M versus BN classification task)
an associated or accompanying finding that is different from the indicium and that is present in at least one of the positive digital files, ([p. 2] "The image Malignant+Benign is a case with additional benign finding" [p. 6] "(note that malignant images can still include benign findings)" The claimed indicium can be malignancy, while the "associated or accompanying finding" is a benign finding. Those are different radiologic findings, yet Bakalo explicitly has mammograms containing both.)
the associated or accompanying finding being identified by a second AI classifier that is specific for the associated or accompanying finding and is different from the first AI classifier;([p. 4] "Detection branch. In parallel, we compute the relevance of each region for the global image-level decision. We perform a distinct detection process for each type of abnormality- one for malignant regions and one for benign regions" [p. 4] "uB and uM are the parameter-sets of the benign and malignant detectors, respectively")
assembling a negative data set from the candidate digital files in which the associated or accompanying finding was identified and ([p. 7] "they assign all images with BI-RADS 1 and 2 as negative and BI RADS 4, 5 and 6 as positive")
obtaining negative evaluation results by processing the negative data set by the first AI classifier thereby training the first AI classifier for negative data;([p. 5] "if the image is normal or contains a benign finding, the model will concentrate on regions that were most probably and erroneously classified as malignant (hard negatives). This process, which is similarly applied for the benign class is equivalent to hard negative mining in natural images" When a benign or normal image is processed by the malignant path, suspicious regions that the network most nearly confuses with malignancy become hard negatives. Because this occurs within the training architecture governed by Bakalo's likelihood objective, those target-negative examples train the malignant model against false malignant signals.)
whereby the negative data set functions as a digital mask that trains the first AI classifier to focus only on the indicium;([p. 5] "if the image is normal or contains a benign finding, the model will concentrate on regions that were most probably and erroneously classified as malignant (hard negatives) […] Mask Computation: hc(i) is a binary value indicating whether region i is one of the k regions with the highest probability of being classified as c")
analyzing a test data set by the first AI classifier to obtain test evaluation results the test data set comprising a plurality of positive test digital files each comprising a presence of the indicium and a plurality of negative test digital files([p. 7] "Our evaluation on IMG dataset was based on 5 fold patient-wise cross-validation, where at each train and test iteration, all the images from the patient under test were strictly excluded from the training set. To this end we randomly split the data set into 5 folds according to patient IDs, keeping a similar distribution over breast composition and lesion types in the folds." [p. 7] "split into 100 positive (global BI-RADS 4,5,6) and 310 negative (global BI-RADS 1,2,3) mammograms [...] We conducted a random patients split on the INbreast images with 50% for train and 50% for test")
for each of which an absence of the indicium has been assessed,([p. 7] "BI-RADS 2 as benign (B) and BI-RADS 1 as normal (N) […] To this end we used p(yM = 1|x) scoring for M vs. B ∪ N (M vs. BN)" Bakalo explicitly treats benign and normal images as the opposing, non-malignant class in the M versus BN classification task)
and sorting the test evaluation results by at least one probability threshold to obtain sorted results; and([p. 7] "For performance measures, in addition to AUROC, we also report two more practical measures as used in [13]. The partial-AUC ratio (pAUCR), associated with the ratio of the area under the ROC curve in a high sensitivity range ([0.8,1]), representing the AUROC at a more relevant domain for clinicians. Also, we report the specificity extracted from the ROC curve at sensitivity 0.85 and 0.90 representing an average operation point (OP) for expert radiologists" A ROC operating point for a continuous probability score necessarily corresponds technically to selecting a decision threshold and classifying scores sorted relative to that threshold).
However, Bakalo does not explicitly teach examining the sorted results to identify incorrectly sorted results and retraining by reanalyzing the first AI classifier for the incorrectly sorted results thereby building and training the first AI classifier.
Williams, in the same field of endeavor, teaches examining the sorted results to identify incorrectly sorted results and retraining by reanalyzing the first AI classifier for the incorrectly sorted results thereby building and training the first AI classifier. ([¶0167] " in the example document of FIG. 13, the file displayed has a path of “/user1/public_html/products/submit.php” and received a predicted maliciousness score of 0.85. In this example, a Domain Expert has reviewed this file and marked it as being malicious, agreeing with the system's prediction. If an expert disagrees with and reverses the predicted class of a data element, they may mark that element 1 as the correct class and submit it to be part of the Training Corpus 508. This may be referred to as a “labeling conflict.” The submission of a document into the Training Corpus 508 after a labeling conflict may be performed automatically or with confirmation from a human user. However, submission of data into the Training Corpus 508 does not immediately increase the performance of the system. After a re-training of the model with the updated Training Corpus 508 the Domain Expert's refinement may be incorporated, but methods are available to reduce the amount of time required to re-train including using a combination of models that differing amounts of time and data sizes to train." Williams thresholded, scored, and ranked outputs are explicitly examined by domain experts who examine the results. When a domain expert identifies a mistake they explicitly "reverse the predicted class" and resubmit it to the training corpus for retraining the model).
Bakalo as well as Williams are directed towards using machine learning for positive and negative classifiers of medical data. Therefore, Bakalo as well as Williams are analogous art in the same field of endeavor. It would have been obvious before the effective filing date of the claimed invention to combine the teachings of Bakalo with the teachings of Williams by performing retraining based on incorrectly sorted results. Williams provides as additional motivation for combination that this allows ([¶0103] “a human to evaluate the systems performance and diagnose problems or identify potential improvements”).
Regarding claim 8, the combination of Bakalo and Williams teaches The method according to claim 1 further comprising prior to sorting, transforming the test evaluation results to a numeric score having a normalized distribution across a defined range. (Bakalo [p. 4] "Each region is classified, in this study, as normal (N), benign (B) or malignant (M) using a softmax layer: […] The detection result is a distribution over all the regions in the image implemented by a softmax operation" Those are normalized probability distributions: the class probabilities sum to one across N, B, M, and the detection probabilities sum to one across regions for the respective finding class).
Regarding claim 9, the combination of Bakalo and Williams teaches The method according to claim 1, the probability threshold is selected from: a negative probability threshold, a positive probability threshold, an aggregate positive probability threshold, and an aggregate negative probability threshold. (Bakalo [p. 2] "The image class probability is then obtained by aggregation of detection and classification abnormality probabilities for all regions in the image." See also Eqn. 3).
Regarding claim 12, the combination of Bakalo and Williams teaches The method according to claim 1, the test data set further comprises a plurality of test digital files.(Bakalo [p. 7] "In our test scenario, we split the mammograms into the following three global labels: BI-RADS 4 & 5 defined as malignant (M), BI-RADS 2 as benign (B) and BI-RADS 1 as normal (N)" A mammogram is a radiologic digital image).
Regarding claim 16, the combination of Bakalo and Williams teaches The method according to claim 1, the digital file is a format selected from at least one of: an image, a waveform, a genomic file, a metadata, a report, and a written template obtained from a subject.(Bakalo [p. 3 §III] "we propose a deep network architecture that classifies mammogram regions into three different classes: normal tissue, benign and malignant findings using labels at the image level" [p. 4] "we apply a transfer learning approach by using the pre-trained VGG128 network by [26], trained on the ImageNet dataset" A mammogram is a radiologic digital image).
Regarding claim 17, the combination of Bakalo and Williams teaches The method according to claim 1 further comprising processing for analyzing results obtained from at least one of: medical images, non-medical images, medical report data, including words, phrases, sentences, medical laboratory data, medical waveforms such as electrocardiograph, electroencephalograph and electromyograph, radiologic images, genetic data.(Bakalo [p. 7] "In our test scenario, we split the mammograms into the following three global labels: BI-RADS 4 & 5 defined as malignant (M), BI-RADS 2 as benign (B) and BI-RADS 1 as normal (N)" A mammogram is a radiologic digital image).
Regarding claim 18, the combination of Bakalo and Williams teaches The method according to claim 17, the images are photographs.(Bakalo [p. 7] "In our test scenario, we split the mammograms into the following three global labels: BI-RADS 4 & 5 defined as malignant (M), BI-RADS 2 as benign (B) and BI-RADS 1 as normal (N)" A mammogram is a radiologic digital image).
Regarding claim 19, the combination of Bakalo and Williams teaches The method according to claim 1 further comprising acquiring at least one of: the positive data set, the negative data set, and the test data set.(Bakalo [p. 7] "In our test scenario, we split the mammograms into the following three global labels: BI-RADS 4 & 5 defined as malignant (M), BI-RADS 2 as benign (B) and BI-RADS 1 as normal (N)" A mammogram is a radiologic digital image).
Regarding claim 20, the combination of Bakalo and Williams teaches The method according to claim 19 wherein the acquiring further comprises extracting at least one of: the positive data set, the negative data set, and the test data set from a database library.(Bakalo [p. 7] "Our second test bed used for our weakly supervised model, was composed of the INbreast (INB) publicly available FFDM dataset [9]. This small dataset has 410 mammograms from 116 cases and was split into 100 positive (global BI-RADS 4,5,6) and 310 negative (global BI-RADS 1,2,3) mammograms").
Regarding claim 21, the combination of Bakalo and Williams teaches The method according to claim 1, examining is performed on at least one of: a user interface, (Williams [¶0182] "Pages that are scored as malicious may be logged and made available for audit or Domain Expert examination using User Interface 532" [¶0183] "if anomalous pages are detected, those pages are also made available in User Interface 532")
and a system interface.(Williams [¶0201] "Decision 536 may be used to automatically adjudicate the claim, sending the claim decision to a claims payment and recording system using mechanisms including, but not limited to, database records or application programming interface calls" API interpreted as a system interface).
Regarding claim 22, claim 22 is directed towards a system for performing the method of claim 1. Therefore, the rejection applied to claim 1 also applies to claim 22.
the system comprising: at least one AI processor programmed to implement the first AI classifier and a second AI classifier and to: (Williams [¶0067] "Network computer 300 includes one or more processor devices, such as, processor 302" [¶0075] "Applications 314 may also include, web server 316, machine learning engine 318, interactive tuning application 321, or the like." [¶0090] "Data Manager 506 which may be responsible for sorting and storing the data in the corpus the that data belongs to").
Regarding claim 23, the combination of Bakalo and Williams teaches The system according to claim 22 further comprising a user interface or a system interface.(Williams [¶0182] "Pages that are scored as malicious may be logged and made available for audit or Domain Expert examination using User Interface 532" [¶0183] "if anomalous pages are detected, those pages are also made available in User Interface 532" [¶0201] "Decision 536 may be used to automatically adjudicate the claim, sending the claim decision to a claims payment and recording system using mechanisms including, but not limited to, database records or application programming interface calls" API interpreted as a type of system interface).
Regarding claim 24, the combination of Bakalo and Williams teaches The system according to claim 22 further comprising at least one database library.(Bakalo [p. 7] "Our second test bed used for our weakly supervised model, was composed of the INbreast (INB) publicly available FFDM dataset [9]. This small dataset has 410 mammograms from 116 cases and was split into 100 positive (global BI-RADS 4,5,6) and 310 negative (global BI-RADS 1,2,3) mammograms").
Claims 6, 7, 10, and 11 are rejected under U.S.C. §103 as being unpatentable over the combination of Bakalo and Williams and in further view of Herbei (“Classification with reject option”, 2005).
Regarding claim 6, the combination of Bakalo and Williams teaches The method according to claim 1 further comprising after retraining, performing iterations of the steps of sorting, examining, (Williams [¶0183] "During Domain Expert Analysis 530, suspected malicious webpages are reviewed by Domain Experts, and if appropriate, supplied a class label for the type of malicious code detected. From time 1 to time, this data is included back into the Training Corpus 508 and revised versions of the Models 518 may be trained using the new data, based on Retraining Decision 119. If a malicious page is scored with a high degree of confidence, and a malicious code class label is also scored with a high-degree of confidence, Decision Process 536 may apply a set of adjustable confidence thresholds, and decide whether to automatically remediate the malicious code. Remediation actions may vary based on the malicious code class label." [¶0197] "Thresholds and rules may be tuned to maximize the efficiency gained by automation decision").
However, the combination of Bakalo and Williams doesn't explicitly teach and retraining the first AI classifier by a series of decreasing probability thresholds thereby obtaining a positive AI classifier.
Herbei, in the same field of endeavor, teaches and retraining the first AI classifier by a series of decreasing probability thresholds thereby obtaining a positive AI classifier([p. 4] "we can restrict ourselves to the cases 0 ≤ d ≤ 1/2 and we denote the relevant risk function […] the Bayes rule (5) simplifies to […] 1 if n(x) > 1-d" [p. 21] "We split the data D into a training set D1 and D2, each of size 50 and consider, separately, two choices for d: d = .25 and d = .50. For each pair (k,h), we use the training data D1 to estimate η and the testing data D2 to estimate the risk" See also FIG. 1 where the series d, 1-d is interpreted as a series of decreasing probability (Bayes) thresholds to obtain a positive AI classifier n(x). See also FIG. 7 where Herbei varies d).
The combination of Bakalo and Williams as well as Herbei are directed towards machine learning classification. Therefore, the combination of Bakalo and Williams as well as Herbei are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to substitute the combination of Bakalo and Williams generic threshold strategy with the mathematically precise thresholding strategy in Herbei. Herbei explicitly extends the threshold to medical classification and provides as additional motivation for combination ([Abstract] “We extend the mathematical framework even further by differentiating between costs as sociated with the two possible errors: predicting f(X) = 0 whilst Y = 1 and predicting f(X) = 1 whilst Y = 0. Such situations are common in, for instance, medical studies where misclassifying a sick patient as healthy is worse than the opposite.”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 7, the combination of Bakalo and Williams teaches The method according to claim 1 further comprising after retraining, performing iterations of the steps of sorting, examining, (Williams [¶0183] "During Domain Expert Analysis 530, suspected malicious webpages are reviewed by Domain Experts, and if appropriate, supplied a class label for the type of malicious code detected. From time 1 to time, this data is included back into the Training Corpus 508 and revised versions of the Models 518 may be trained using the new data, based on Retraining Decision 119. If a malicious page is scored with a high degree of confidence, and a malicious code class label is also scored with a high-degree of confidence, Decision Process 536 may apply a set of adjustable confidence thresholds, and decide whether to automatically remediate the malicious code. Remediation actions may vary based on the malicious code class label." [¶0197] "Thresholds and rules may be tuned to maximize the efficiency gained by automation decision").
However, the combination of Bakalo and Williams doesn't explicitly teach and retraining the first AI classifier by a series of increasing probability thresholds thereby obtaining a negative AI classifier.
Herbei, in the same field of endeavor, teaches and retraining the first AI classifier by a series of increasing probability thresholds thereby obtaining a negative AI classifier([p. 4] "we can restrict ourselves to the cases 0 ≤ d ≤ 1/2 and we denote the relevant risk function […] the Bayes rule (5) simplifies to […] 1 if n(x) > 1-d" [p. 21] "We split the data D into a training set D1 and D2, each of size 50 and consider, separately, two choices for d: d = .25 and d = .50. For each pair (k,h), we use the training data D1 to estimate η and the testing data D2 to estimate the risk" See also FIG. 1 where the series d, 1-d is interpreted as a series of increasing probability (Bayes) thresholds to obtain a negative AI classifier n(x)).
The combination of Bakalo and Williams as well as Herbei are directed towards machine learning classification. Therefore, the combination of Bakalo and Williams as well as Herbei are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to substitute the combination of Bakalo and Williams generic threshold strategy with the mathematically precise thresholding strategy in Herbei. Herbei explicitly extends the threshold to medical classification and provides as additional motivation for combination ([Abstract] “We extend the mathematical framework even further by differentiating between costs as sociated with the two possible errors: predicting f(X) = 0 whilst Y = 1 and predicting f(X) = 1 whilst Y = 0. Such situations are common in, for instance, medical studies where misclassifying a sick patient as healthy is worse than the opposite.”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 10, the combination of Bakalo and Williams teaches The method according to claim 9.
However, the combination of Bakalo and Williams doesn't explicitly teach, the aggregate positive probability threshold is selected from the group consisting of: 99% probability, 95% probability, 90% probability, 85% probability, 80% probability, 75% probability, 70% probability, 65% probability, 60% probability, 55% probability, and 50% probability.
Herbei, in the same field of endeavor, teaches the aggregate positive probability threshold is selected from the group consisting of: 99% probability, 95% probability, 90% probability, 85% probability, 80% probability, 75% probability, 70% probability, 65% probability, 60% probability, 55% probability, and 50% probability. ([p. 4] "we can restrict ourselves to the cases 0 ≤ d ≤ 1/2 and we denote the relevant risk function […] the Bayes rule (5) simplifies to […] 1 if n(x) > 1-d" Herbei explicitly restricts d to less than or equal to 50% so 1-d therefore must be greater than 50%).
The combination of Bakalo and Williams as well as Herbei are directed towards machine learning classification. Therefore, the combination of Bakalo and Williams as well as Herbei are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to substitute the combination of Bakalo and Williams generic threshold strategy with the mathematically precise thresholding strategy in Herbei. Herbei explicitly extends the threshold to medical classification and provides as additional motivation for combination ([Abstract] “We extend the mathematical framework even further by differentiating between costs as sociated with the two possible errors: predicting f(X) = 0 whilst Y = 1 and predicting f(X) = 1 whilst Y = 0. Such situations are common in, for instance, medical studies where misclassifying a sick patient as healthy is worse than the opposite.”). This motivation for combination also applies to the remaining claims which depend on this combination.
Regarding claim 11, the combination of Bakalo and Williams teaches The method according to claim 9.
However, the combination of Bakalo and Williams doesn't explicitly teach, the aggregate negative probability threshold is selected from the group consisting of: 49% probability, 45% probability, 40% probability, 35% probability, 30% probability, 25% probability, 20% probability, 15% probability, 10% probability, 5% probability, and 0% probability.
Herbei, in the same field of endeavor, teaches the aggregate negative probability threshold is selected from the group consisting of: 49% probability, 45% probability, 40% probability, 35% probability, 30% probability, 25% probability, 20% probability, 15% probability, 10% probability, 5% probability, and 0% probability. ([p. 4] "we can restrict ourselves to the cases 0 ≤ d ≤ 1/2 and we denote the relevant risk function […] the Bayes rule (5) simplifies to […] 1 if n(x) > 1-d" Herbei explicitly restricts d to less than or equal to 50% so 1-d therefore must be greater than 50%).
The combination of Bakalo and Williams as well as Herbei are directed towards machine learning classification. Therefore, the combination of Bakalo and Williams as well as Herbei are reasonably pertinent analogous art. It would have been obvious before the effective filing date of the claimed invention to substitute the combination of Bakalo and Williams generic threshold strategy with the mathematically precise thresholding strategy in Herbei. Herbei explicitly extends the threshold to medical classification and provides as additional motivation for combination ([Abstract] “We extend the mathematical framework even further by differentiating between costs as sociated with the two possible errors: predicting f(X) = 0 whilst Y = 1 and predicting f(X) = 1 whilst Y = 0. Such situations are common in, for instance, medical studies where misclassifying a sick patient as healthy is worse than the opposite.”). This motivation for combination also applies to the remaining claims which depend on this combination.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SIDNEY VINCENT BOSTWICK whose telephone number is (571)272-4720. The examiner can normally be reached M-F 7:30am-5:00pm EST.
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/SIDNEY VINCENT BOSTWICK/Examiner, Art Unit 2124
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