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 .
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 submission dated 2/11/2026, no claims were 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.
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;
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 performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the computer language, detecting in the context of this claim encompasses a mental process of the user. Similarly, the limitation of ranking, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. For example, but for the computer language, specifying in the context of this claim encompasses a mental process of the user. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
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 9/25/2025 have been fully considered but they are not persuasive.
Examiner Interview
The Applicant states, “The disclosure identifies concrete shortcomings of "conventional pattern detection methods," including exhaustive grid searches that become "complicated and time consuming" with "tens to hundreds of features and values," and bias from intuition or prior knowledge (DETAILED DESCRIPTION, "Turning now to an overview of technologies ... ," [0040])” The Examiner notes that the Applicant is leaving out an important portion of paragraph 40.
[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 40, continues to describe what “the analyst” does. In fact, the problems described in paragraph 40 are not technological but rather human. The complicated and time consuming are related to the analyst.
The Applicant furthers states, “It then teaches "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" ([0041]). Here, like in paragraph 40, context is important.
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. The data-driven process uses an unsupervised learning approach which automatically detects, ranks, summarizes and visualizes anomalous subgroups whereby actionable features and values can be emphasized through conditional scanning and iterations.
The specification does not state that the invention creates something new. Rather, the specification states that the invention “uses an unsupervised learning approach.” This is not the description of new technology but rather the application of existing technology to the abstract idea to achieve the benefits of applying that technology to the abstract idea. The Specification emphasizes the use of existing technology in paragraph 44 (emphasis added)
[0044] Embodiments of the invention utilize Al, which includes a variety of so-called machine learning technologies. The phrase "machine learning" broadly describes a function of electronic systems that learn from data. A machine learning system, engine, or module can include a trainable machine learning algorithm that can be trained, such as in an external cloud environment, to learn functional relationships between inputs and outputs, and the resulting model (sometimes referred to as a "trained neural network," "trained model," and/or "trained machine learning model") can be used for managing information during a web conference, for example. In one or more embodiments of the invention, machine learning functionality can be implemented using an artificial neural network (ANN) having the capability to be trained to perform a function. In machine learning and cognitive science, ANN s are a family of statistical learning models inspired by the biological neural networks of animals, and in particular the brain. ANN s can be used to estimate or approximate systems and functions that depend on a large number of inputs. Convolutional neural networks (CNN) are a class of deep, feed-forward ANN s that are particularly useful at tasks such as, but not limited to analyzing visual imagery and natural language processing (NLP). Recurrent neural networks (RNN) are another class of deep, feed-forward ANNs and are particularly useful at tasks such as, but not limited to, unsegmented connected handwriting recognition and speech recognition. Other types of neural networks are also known and can be used in accordance with one or more embodiments of the invention described herein.
Again, the invention uses convention technology in known ways.
The Applicant furthers states, “The core algorithmic advances include linear-time subset scanning with priority-based ordering to reduce the search space from O(2N) to O(N) ([0059]), strategies for identifying and ranking overlapping and mutually exclusive subsets (FIGS. 5-6; [0060]-[0061]), and regularization to control complexity and guide multiple-subset detection ([0062]).” However, these paragraphs are introduced by paragraphs 57 and 58
[0057] In accordance with one or more embodiments of the present invention, executions of the algorithm(s) and processes described above search over all subsets to identify an optimal subset using various techniques. This is achieved using a scoring function analysis method, such as linear time subset scanning, to speed up searches along with enhancements to find targeted anomalous subsets efficiently and to provide for interpretations of results. These include, but are not limited to, outcome analysis and predictive bias assessment. Outcome analysis uses automated stratification (AS) to find subsets with the most differences between actual and population averages. Predictive bias assessment uses bias scanning to find subsets with the most differences between actual outcome and predicted probabilities and can be used to detect model biases.
[0058] AS generally focuses on identifying anomalous subsets of records that have significantly higher/lower-than-expected outcomes with a goal of maximizing a scoring function over all subsets to finds the subsets that have the highest scores, where the scoring function must satisfy a linear time subset scanning property that enables the search to be conducted in linear time.
These are not the descriptions of new technology but rather the result, again, of applying existing technology.
The Applicant states, “Here, each independent claim incorporates the improvement elements disclosed: (a) a specific anomaly-detection workflow, structured inputs with risk-adjusted conditioning (detecting from claims/social determinants …” But, yet it is the Examiner’s opinion that it does not provide specific information but rather describes the invention at a high level using functional terms. The Examiner, being one of ordinary skill, finds that the does not show “an improvement in the functioning of a computer, or an improvement to other technology or a technical field ...”
The Applicant states, “The claims reflect those improvements (independent and dependent claims cited above). Consistent with the second bullet of the memo, this satisfies Step 2A, Prong One/Two: the disclosure identifies a technological improvement and the claims include the steps that provide that improvement, even without reciting it in "thereby" language. Accordingly, the claimed invention fits squarely within the Desjardins guidance for eligibility.” The Examiner disagrees with the Applicant’s opinion. The Examiner reads the claimed invention, as a whole, in light of the originally filed specification. The Examiner believes that the Applicant provides no proof that the invention is a technological improvement but does prove that the invention uses technology.
Claim Rejections under 35 U.S.C. § 101
The Applicant states, “When the claims are evaluated as a whole and in view of the specification, they are directed to a specific improvement to technology and, at minimum, integrate any alleged abstract idea into a practical application consistent with the USPTO's updated guidance following Ex Parte Desjardins, Appeal No. 2024-000567 (PTAB Sept. 26, 2025) (Appeals Review Panel) (precedential) (see MPEP § 2106.04(d) and § 2106.05(a), as revised in the Desjardins memorandum).” The Examiner disagrees in that the Specification does not described the details required to show an improvement. Therefore, the Examiner understands that all arguments directed towards a technical or technological improvement are moot.
In sum, the Examiner understands that the invention uses known technology in known ways to overcome human activity problems. It does so by applying technology to the abstract idea to achieve all the benefits of applying that technology to the abstract idea. The Examiner believes that the Specification agrees that this is an application of technology with the inclusion of paragraph 63 and 67
[0063] Various embodiments of the invention are described herein with reference to the related drawings. Alternative embodiments of the invention can be devised without departing from the scope of this invention. Various connections and positional relationships ( e.g., over, below, adjacent, etc.) are set forth between elements in the following description and in the drawings. These connections and/or positional relationships, unless specified otherwise, can be direct or indirect, and the present invention is not intended to be limiting in this respect. Accordingly, a coupling of entities can refer to either a direct or an indirect coupling, and a positional relationship between entities can be a direct or indirect positional relationship. Moreover, the various tasks and process steps described herein can be incorporated into a more comprehensive procedure or process having additional steps or functionality not described in detail herein.
[0067] The descriptions of the various embodiments of the present invention have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments described herein.
The Examiner understands the generic nature of the invention as disclosed.
The Applicant states, “Here, the claims themselves reflect the disclosed improvement by reciting a particular way to detect and curate anomalous subsets at scale in high-dimensional health data, for example:” The Examiner sees the invention differently. The invention relates to the process of reading in data. Applying an algorithm to the read in data and then outputting the results of reading in and processing the data.
The Applicant states, “The Office Action's characterization of "detecting," "ranking," and "specifying" as mental steps (DETAILED ACTION ,i 10) oversimplifies the concrete, parameterized, automated pipeline recited and disclosed.” Yet, as the Examiner continues to press, the Specification never states a specific required algorithm. The Specification describes families of algorithms that may be used. The Examiner believes it is the Applicant that is adding features into the invention that are not claimed and are not disclosed.
The Applicant states, “Here, the pipeline's constrained detection, de-overlapping, and nested selection directly configures how outreach systems target interventions in a way not present in "conventional pattern detection methods" ([0040]).” The Applicant is changing a single word, “using” to a phrase, “not present in.” The Specification does not states that these solutions were not available but rather states that they were not typical. An alternate existing solution is not a technological improvement.
The Applicant states, “The Examiner's "but for a computer" framing (DETAILED ACTION ,i 10) is inconsistent with this guidance given the explicitly claimed automation and parameter-driven, non-manual processing.” The Applicant’s opinion of the claimed invention is noted. However, the Examiner is guided by the case facts.
The Applicant states, “This is a particular solution, not a bare instruction to apply mathematics.” The Applicant’s “The present claims recite how the system constrains detection (risk adjustment, intervention features), structures outputs ( overlap/unique/conditional; nested monotonic progression with odds-ratio/size trade-offs), and operationalizes the results (informing outreach programs to identify concrete targets)” quotation of known mathematical routines is the an application of math. The Specification never states that this math is anything but known math.
The Applicant states, “The claims recite a non-conventional arrangement:” The Examiner finds no proof that the arrangement claimed and the related disclosure is non-conventional.
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 nonprovisional extension fee (37 CFR 1.17(a)) 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.
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