DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 5/27/2026 has been entered.
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 .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Response to Amendment
In the amendment dated 5/27/2026, the following has occurred: Claims 1, 8, and 15 have been amended.
Claims 1 – 20 are pending.
The Examiner notes that the Specification is written at a high level, using functional terms. Many items are described generically without providing examples. In other places, events occurs, such as “specifying” without describing what is required as the result or the process of actually performing the task. The Examiner notes the language from the following CAFC decision: Electronic Communication Technologies, LLC vs ShppersChoice.com, LLC - Decided: May 14, 2020. From page 8 of the decision
Next, we address step two of the framework and conclude that the claims do not include an inventive concept sufficient to transform the claimed abstract idea into a patent eligible application. Because claim 11 is specified at a high level of generality, is specified in functional terms, and merely invokes well-understood, routine, conventional components and activity to apply the abstract idea identified previously, see, e.g., ’261 patent claim 11; J.A. 576, 581–83, claim 11 fails at step two, see, e.g., Alice, 573 U.S. at 225–26; Mayo, 566 U.S. at 73; see also, e.g., Elec. Power Grp., 830 F.3d at 1355 (concluding patent claims were in eligible under § 101 in part because “[n]othing in the claims, understood in light of the specification, requires anything other than off-the-shelf, conventional computer, network, and display technology for gathering, sending, and presenting the desired information”).
Drawings
The drawings are objected to under 37 CFR 1.83(a) because they fail to show Figure 7 as described in the specification, paragraph 62. Any structural detail that is essential for a proper understanding of the disclosed invention should be shown in the drawing. MPEP § 608.02(d). Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. The figure or figure number of an amended drawing should not be labeled as “amended.” If a drawing figure is to be canceled, the appropriate figure must be removed from the replacement sheet, and where necessary, the remaining figures must be renumbered and appropriate changes made to the brief description of the several views of the drawings for consistency. Additional replacement sheets may be necessary to show the renumbering of the remaining figures. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
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 – 20 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.
The claims, understood as a whole in light of the Specification, recites subject matter within a statutory category as a process (claims 1 – 7), machine (claims 15 – 20), and manufacture (claims 8 – 14) which recite the abstract idea steps of
defining the anomalous subsets as at least one of a Medicare advantage population with anomalous quality measure compliance, a population with short term disability (STD) claims that have a relatively high percentage of switches into long term disability (LTD) benefits, a population of people with major joint replacements with relatively high complication rates after adjusting for risks based on comorbidities and a population of people who have higher rates of opioid usage detecting the anomalous subsets;
detecting the anomalous subsets of the Medicare advantage population with the anomalous quality measure compliance from claims and social determinants of health data;
detecting the anomalous subset of the population with the STD claims that have the relatively high percentage of switches into the LTD benefits from healthcare usage, clinical conditions, enrollment information and job characteristics;
detecting the anomalous subset of the population of people with the major joint replacements with the relatively high complication rates after adjusting for the risks based on comorbidities from actions or choices providers take for interventions comprising site of service, length of stay, day of week when a procedure is done and a type of discharge;
detecting the anomalous subset of the population of people who have the higher rates of opioid usage through use of healthcare history and social determinants;
ranking the anomalous subsets based on a score of each anomalous subset that is reflective of an anomaly thereof;
inputting data relating to outcomes, expected outcomes and sets of features in binary or numerical form;
searching over all subsets to identify an optimal subset using a scoring function analysis method, such as linear time subset scanning, to speed up searches,
wherein the linear time subset scanning uses a priority function to sort feature values of a covariate and scanning is done over top k feature values, for k = l...N such that the highest-scoring subset is guaranteed to be one of the top k highest priority feature values, for k= 1 ... N and all other subsets are suboptimal and need not be considered and the search space can be reduced from O(2N) to O(N);
using regularization factors to control a size of feature values and/or features in a subset and to control the identification of overlapping subsets;
specifying whether each of the anomalous subsets overlaps with another one of the anomalous subsets, whether each of the anomalous subsets is unique and whether each of the anomalous subsets is conditional; and
specifying as to whether the detecting of each of the anomalous subsets has a higher or lower outcome than expected
wherein the method further comprises informing outreach groups regarding the specifying as to whether the detecting of each of the anomalous subsets has the higher or lower outcome than expected to identify the factors, the subpopulation, the hot spot occurrences and the most anomalous nested subsets.
These steps of claims 1 – 20 , as drafted, under the broadest reasonable interpretation, includes mathematical concepts. The Examiner understands the claimed invention in light of the Specification. Particularly, paragraphs 51 and 52 state: (emphasis added)
[0050] With reference to FIG. 3, a computer-implemented method 300 is provided for differentiating patterns of care (DPoC) to detect anomalous subsets in any given population with a defined set of outcomes and features. A shown in FIG. 3, the computer-implemented method 300 includes detecting the anomalous subsets (block 303), ranking the anomalous subsets based on a score of each anomalous subset that is reflective of an anomaly thereof (block 304), specifying whether each of the anomalous subsets overlaps with another one of the anomalous subsets, whether each of the anomalous subsets is unique and whether each of the anomalous subsets is conditional (block 305) and specifying as to whether the detecting of each of the anomalous subsets has a higher or lower outcome than expected (block 306). The computer-implemented method can further include summarizing and visualizing results (i.e., within reports) of the detecting of block 303, the ranking of block 304, the specifying of whether each of the anomalous subsets overlaps with another one of the anomalous subsets, whether each of the anomalous subsets is unique and whether each of the anomalous subsets is conditional of block 305 and the specifying as to whether the detecting of each of the anomalous subsets has a higher or lower outcome than expected of block 306 (block 307).
[0051] The computer-implemented method 300 is characterized in that input data preparation (block 301), setup configuration (block 302), algorithm executions of blocks 303-306 and report generations of block 307 are automated, with input data including binary or numerical outcome data and feature data with an unlimited number of features. In accordance with one or more embodiments of the present invention, the setup configuration of block 302 can include at least one or more of a feature selection operation in which the features of the unlimited number of features are selected (block 3021) and a parameter choice operation in which parameters include regularization parameters, counts for bootstrap repetitions, randomization initializations, numbers of the anomalous subsets to be identified and anomaly detection directions (block 3022). Also, in accordance with one or more embodiments of the present invention, the specifying of whether each of the anomalous subsets overlaps with another one of the anomalous subsets, whether each of the anomalous subsets is unique and whether each of the anomalous subsets is conditional of block 305 can include setting the specifying to specify one of whether each of the anomalous subsets overlaps with another one of the anomalous subsets, whether each of the anomalous subsets is unique or whether each of the anomalous subsets is conditional (block 3051).
In other words, the central portion of the claimed invention is performed using “algorithm executions.” The invention is not a technological improvement but rather the invention improves upon the “conventional pattern detection methods” by applying the abstract idea to technology as stated to achieve all the benefits of applying that abstract idea to technology. paragraph 40 states
[0040] For example, using conventional pattern detection methods, an analyst can correlate outcomes with some features to identify anomalies, one at a time or through an exhaustive grid search but, with tens to hundreds of features and values, analysis quickly becomes complicated and time consuming. While the analyst can filter the search, results can be biased by intuition or prior knowledge. Moreover, while the analyst may build a targeted clustering or prediction model to identify important features, such model may not reveal hidden anomalous patterns or help identify model biases.
Paragraph 41 begins by solving this human based problem, “Turning now to an overview of the aspects of the invention, one or more embodiments of the invention address shortcomings of the above-described approach by providing for a general data-driven process to identify and rank anomalous subsets in populations, to generalize identifications of anomalous subsets for large cases of outcomes and to expand analyses to multiple iterations in order to uncover different data insights.” Although the solution is technological, the solution does not represent a technical improvement to overcome a technical problem.
The result of the invention is data and therefore there is no practical application. Quoting from paragraph 51 above, the invention ends with the “report generations of block 307.” Paragraph 52 repeats this idea that the invention ending with a report in figure 4, paragraph 52 first with, “The results of the running of the algorithm(s) are then summarized and visualized (block 408). Then, figure 4 and paragraph 52 conclude with “Subsequently, results are re-summarized and re-visualized (block 411).”
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claims 2 – 7, 9 – 14, and 16 – 20, reciting particular aspects of how anomalous detection may be performed in the mind but for recitation of generic computer components).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of computer implemented amounts to invoking computers as a tool to perform the abstract idea, see MPEP 2106.05(f))
add insignificant extra-solution activity to the abstract idea (such as recitation of specifying… amounts to insignificant application, see MPEP 2106.05(g); such as informing… amounts to extra-solution activity, see 2106.05(g))
Regarding the question of practical application, the instant independent claims end with:
to identify:
factors driving higher surgery rates or complication rates in the Medicare advantage population with the anomalous quality measure compliance,
a subpopulation of the population with the STD claims that have the relatively high percentage of switches into the LTD benefits from healthcare usage,
hot spot occurrences for inpatient surgeries with an extended length of more than four days of stay of the population of people with the major joint replacements with the relatively high complication rates after adjusting for the risks based on comorbidities, and
most anomalous nested subsets that are increasingly anomalous with higher percentages of opioid users but that decrease in size with increasing odds ratios and decreasing sizes.
The limitations following “to identify” are the intended use of the “specify” limitation. Further, these potentially identified objects describe data that has only a potential usage. Only if a user, independently of the claimed invention, acts upon the data does the data have an application. Therefore, the claimed invention has no practical application.
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 – 7, 9 – 14, and 16 – 20, additional limitations which amount to invoking computers as a tool to perform the abstract idea). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as claims 1 – 20; detecting, ranking, and overlapping, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii))
Additional Elements
Computer including process, memory, and storage – paragraphs 25 and 26
Network – paragraphs 24, 31
Peripheral devices – paragraph 31
Instructions – paragraph 27
Neural network / machine learning – paragraphs 44 – 47
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 2 – 7, 9 – 14, and 16 – 20, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation.
The Examiner has no suggestions to overcome this rejection.
Response to Arguments
Applicant's arguments filed 5/27/2026 have been fully considered but they are not persuasive.
Claim Rejections under 35 U.S.C. § 101
The Applicant states, “The present claims now recite the concrete algorithmic …” However, adding math to the instant invention does not make the invention less abstract.
The Applicant states, “Under Step 2A, Prong One, the applicants respectfully submit that the Examiner's characterization of the amended claims as merely mental processes or mathematical concepts is no longer reasonable when the claims are considered as a whole.” The Examiner has removed the “mental process” portion of the instant rejection.
The Applicant states, “The amended claims reflect that disclosed improvement by requiring the specific LTSS priority-sorting and top-k scanning process that reduces the search space from O(2N) to O(N), together with regularization that controls subset complexity and overlapping-subset identification.” Here, as in the previous remarks, the Applicant argues paragraph 40 and 41. Here, as in the previous responses, the Examiner replies that the instant invention discuss overcoming human based problems by applying technology. The “improvement” discussed is not one of technology but over human process.
The Applicant states, “These limitations are drawn directly from paragraph [0059] and are the type of "particular way to achieve a desired outcome" that the Desjardins memorandum identifies as important to the technological-improvement inquiry under MPEP § 2 106.05(a).” The Examiner notes that Desjardins regards the question of technological improvement as disclosed. However, the Applicant can only show paragraph 40 and 41 which do not discuss technological improvements.
The Applicant states, “Instead, the claims now recite a particular computer-implemented search mechanism that changes how the computer evaluates the subset space. The claimed reduction of the search space from O(2N) to O(N), using the LTSS priority and top-k guarantee, is a computational improvement in the operation of the claimed computer-implemented method, computer program product, and computing system.” The Examiner is not persuaded by adding more math to a computer to make that math less abstract.
The Applicant states, “Nor do the amended claims merely end with insignificant post-solution reporting. The claims require the algorithmic detection and ranking architecture before the outreach-program limitation, and the resulting outputs are constrained by the claimed overlap, uniqueness, conditionality, and nested-subset requirements.” The instant invention reads in data, processes the read in data, and outputs the results of read in and processed data. The fact that this processed data was computed using a complex algorithm does not eliminate the abstractness. The fact that potentially useful data was outputted does not change the fact that the data is only potentially used.
The Applicant states, “Instead, the claimed processor, computer-readable storage media, and computer-readable program code are configured to execute a particular search-space-reducing algorithmic process and subset-control process. That ordered combination is meaningfully more than using a generic computer as a tool to perform mental steps.” The Applicant’s opinion is not supported by the Specification.
The Applicant states, “The Panel credited improvements disclosed in the specification and reflected in the claims, including reduced storage, reduced system complexity, and preserving prior performance during later machine-learning tasks. …The claimed improvement is not subsumed by the mathematical score; rather, the score is used within the recited computer-implemented mechanism for efficiently searching and curating anomalous subsets.” The Examiner notes that the Applicant does not quote the Specification when arguing whether the improvement exists. Instead, because the Specification is silent regarding technological improvements, the Applicant must take out of context snippets from sentences and from paragraphs. The Examiner believes that the Applicant does not support his opinions with facts.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Narayanam et al Pub. No.: US 2024/0320538 Systems and methods identify anomalous data in tabular data.
Nainwani et al Pub. No.: US 2023/0179488 detecting a set of point anomalies in real-time data associated with each network entity for each feature thereof, and, in accordance with reading anomaly scores associated with an event as an input feedback, the each feature of the each network entity as a dimension of the input feedback and a category of the event as a label thereof, predictively classifying a future event into a predicted category in accordance with subjecting the anomaly scores associated with the event to a binning process and interpreting a severity indicator of the event.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Neal R Sereboff whose telephone number is (571)270-1373. The examiner can normally be reached M - T, M - F 8AM - 6PM.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Robert Morgan can be reached on (571)272-6773. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/NEAL SEREBOFF/
Primary Examiner
Art Unit 3626
Prosecution Insights