DETAILED ACTION
This office action is responsive to the response filled 3/2/2026. The application contains claims 1-25, all examined and rejected.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “interface unit configured to receive” in claim 18.
Claim limitation in claim 18 has been interpreted under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, because it uses a non-structural term “interface unit” coupled with functional language without reciting sufficient structure to achieve the function. Furthermore, the non-structural term is not preceded by a structural modifier.
Claim 18 recites the limitation “interface unit” coupled with functional language without reciting sufficient structure to achieve the function.
Since these claim limitations invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, claim 18 is interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph limitation: Fig. 2, Paragraph [0100] states, “interface unit INT may be realized as hardware- or software-interface, e.g., a PCI- bus, USB or fire-wire”, Paragraph [0103] states, “computing unit CU may be comprised in the interface unit INT, e.g., in the form of a processor of a tablet, laptop, or workstation computer. Alternatively, computing unit CU may comprise a real or virtual group of computers like a so called 'cluster' or 'cloud'. Such server system may be a central server, e.g., a cloud server, or a local server, e.g., located on a hospital site. Further, computing unit CU may comprise a memory such as a RAM for temporally loading the input dataset 1. According to some examples, such memory may as well be comprised in the interface unit INT”, Paragraph [0109] Based on the guidelines announced from Federal Register Vol. 76, No. 27, this has been interpreted as encompassing a hardware or hardware in combination with software implementation of the module, but not a pure software implementation.
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action. Claimed modules also trigger interpretation of the claim language under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph since they are considered a place holder for a corresponding structure in the specification.
If applicant does not wish to have the claim limitation treated under 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, applicant may amend the claim so that it will clearly not invoke 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph, or present a sufficient showing that the claim recites sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or 35 U.S.C. 112 (pre-AIA ), sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance with 35 U.S.C. § 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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 1-25 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claim 1 is rejected under 35 USC 101 because the claimed inventions are directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
While independent claims 1, 16, 18, 19, 20 are each directed to a statutory category, it recites a series of which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-25 are rejected under 35 U.S.C. § 101 because the instant application is directed to non-patentable subject matter. Specifically, the claims are directed toward at least one judicial exception without reciting additional elements that amount to significantly more than the judicial exception. The rationale for this determination is in accordance with the guidelines of USPTO, applies to all statutory categories, and is explained in detail below.
When considering subject matter eligibility under 35 U.S.C. 101, (1) it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, (2a) it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so (2b), it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself. Examples of abstract ideas include certain methods of organizing human activities; a mental processes; and mathematical concepts, (2019 PEG)
STEP 1.
Per Step 1, the claims are determined to include process, manufacture, and machine as in independent Claims 1, 16, 18, 19, 20, and in the therefrom dependent claims. Therefore, the claims are directed to a statutory eligibility category.
At step 2A, prong 1, The invention is directed to identifying features within received data that could be an indication of the probability of occurrence of a machine failure based on analyzed historic data which is akin to Mental Process (see Alice), As such, the claims include an abstract idea. When considering the limitations individually and as a whole the limitations directed to the abstract idea are:
“performing multivariate outlier detection on the input dataset, the performing including computing anomaly scores for at least a portion of the plurality of datapoints using a multivariate outlier detection algorithm” (mental process, mathematical concept);
identifying, at least one set of multivariate outlier datapoints among the plurality of datapoints based on the anomaly scores, the at least one set of multivariate outlier datapoints being usable in combination to identify the patient (mental process, mathematical concept)
The claim recites additional elements as
“computer-implemented data protection method” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
receiving an input dataset (insignificant extra-solution activity, MPEP 2106.05(g));
“input dataset including a plurality of datapoints, at least some of the plurality of datapoints including information usable in combination to identify a patient“ (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use).
This judicial exception is not integrated into a practical application. The elements are recited at a high level of generality, i.e. a generic computing system performing generic functions including generic processing of data. Accordingly the additional elements do not integrate the abstract into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Therefore the claims are directed to an abstract idea. (2019 Revised Patent Subject Matter Eligibility Guidance ("2019 PEG"). Thus, under Step 2A of the Mayo framework, the Examiner holds that the claims are directed to concepts identified as abstract.
STEP 2B.
Because the claims include one or more abstract ideas, the examiner now proceeds to Step 2B of the analysis, in which the examiner considers if the claims include individually or as an ordered combination limitations that are "significantly more" than the abstract idea itself. This includes analysis as to whether there is an improvement to either the "computer itself," "another technology," the "technical field," or significantly more than what is "well-understood, routine, or conventional" (WURC) in the related arts.
The instant application includes in Claim 1 additional steps to those deemed to be abstract idea(s).
When taken the steps individually, these steps are:
“computer-implemented data protection method” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2));
receiving an input dataset (WURC activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i))
“input dataset including a plurality of datapoints, at least some of the plurality of datapoints including information usable in combination to identify a patient“ (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f));
In the instant case, Claim 1 is directed to above mentioned abstract idea. Technical functions such as receiving, and extracting are common and basic functions in computer technology. The individual limitations are recited at a high level and do not provide any specific technology or techniques to perform the functions claimed.
In addition, when the claims are taken as a whole, as an ordered combination, the combination of steps does not add "significantly more" by virtue of considering the steps as a whole, as an ordered combination. The instant application, therefore, still appears only to implement the abstract idea to the particular technological environments using what is well-understood, routine, and conventional in the related arts. The steps are still a combination made to the abstract idea. The additional steps only add to those abstract ideas using well understood and conventional functions, and the claims do not show improved ways of, for example, an unconventional non-routine functions for analyzing model operations or updating the model that could then be pointed to as being "significantly more" than the abstract ideas themselves.
Moreover, Examiner was not able to identify any "unconventional" steps, which, when considered in the ordered combination with the other steps, could have transformed the nature of the abstract idea previously identified. The instant application, therefore, still appears to only implement the abstract ideas to the particular technological environments using what is well-understood, routine, and conventional (WURC) in the related arts.
Further, note that the limitations, in the instant claims, are done by the generically
recited computing devices. The limitations are merely instructions to implement the abstract idea on a computing device that is recited in an abstract level and require no more than a generic computing devices to perform generic functions.
Claim 18 recites an apparatus comprising “an inference unit ”, and “at least one processor”, the added element of “an inference unit ”, and the at least one processor”, do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claims 19 and 20 recites an apparatus comprising “a non-transitory readable medium”, and “at least one processor”, the added element of “a non-transitory readable medium”, and “at least one processor”, do not transform the judicial exception into a practical application because they are tantamount to a mere instruction to apply the judicial exception to a generic computer. The additional elements are also not sufficient to amount to significantly more than the judicial exception because the action of implementing the method on a general purpose computer with at least one processor and at least one memory is tantamount to a mere instruction to apply the judicial exception to a computer.
Claims 16, 18, 19, 20 are therefore rejected according to the same findings and rationale as provided above.
CONCLUSION
It is therefore determined that the instant application not only represents an abstract idea identified as such based on criteria defined by the Courts and on USPTO examination guidelines, but also lacks the capability to bring about "Improvements to another technology or technical field" (Alice), bring about "Improvements to the functioning of the computer itself" (Alice), "Apply the judicial exception with, or by use of, a particular machine" (Bilski), "Effect a transformation or reduction of a particular article to a different state or thing" (Diehr), "Add a specific limitation other than what is well-understood, routine and conventional in the field" (Mayo), "Add unconventional steps that confine the claim to a particular useful application" (Mayo), or contain "Other meaningful limitations beyond generally linking the use of the judicial exception to a particular technological environment" (Alice), transformed a traditionally subjective process performed by humans into a mathematically automated process executed on computers (McRO), or limitations directed to improvements in computer related technology, including claims directed to software (Enfish).
The dependent claims, when considered individually and as a whole, likewise do not provide "significantly more" than the abstract idea for similar reasons as the independent claim.
claims 2 disclose “automatically de-identifying the at least one set of multivariate outliers of datapoints to generate a processed dataset (mental process) and providing the processed dataset” (WURC activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose wherein the automatically de-identifying includes one or more of: removing a datapoint; rounding a value of a datapoint; substituting a datapoint; categorizing a datapoint; or transforming a datapoint (mental process); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose “displaying the input dataset on a user interface with the at least one set of multivariate outlier datapoints highlighted” (WURC activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “receiving user input via the user interface, the user input being directed to the at least one set of multivariate outlier datapoints; processing the input dataset according to the user input to generate a processed dataset; and providing the processed dataset” (WURC activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 6 disclose “wherein the identifying includes identifying a set of datapoints as the at least one set of multivariate outlier datapoints in response to an anomaly score of the set of datapoints exceeding a threshold” (mental process, mathematical concept); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 7 disclose the identifying includes identifying a plurality of sets of multivariate outlier datapoints (mental process, mathematical concept); and the method further includes displaying a ranking of the plurality of sets of multivariate outliers of datapoints based on respective anomaly scores on a user interface (WURC activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 8 disclose multivariate outlier detection algorithm is a machine learning-based algorithm (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 9 disclose multivariate outlier detection algorithm is selected from a group including one or more of: an isolation forest; an elliptic envelope; a fast-minimum covariance determinant estimator; or local outlier factors (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 10 disclose receiving user input directed to at least one datapoint in the at least one set of multivariate outlier datapoints, the user input being directed to de-identifying the at least one datapoint (WURC activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)); and training the multivariate outlier detection algorithm based on the user input (This limitation is directed to training a system which is a high-generic computer software process of training data. This limitation does not amount to significantly more than the judicial exception, see MPEP 2106.05 (f)) , It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 11 disclose performing includes computing partial anomaly scores for at least the portion of the plurality of datapoints using a plurality of different multivariate outlier detection algorithms; and the computing of the anomaly scores includes aggregating the partial anomaly scores to generate the anomaly scores (mental process, mathematical concept). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 12 disclose executing an explainable AI module (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); and displaying a result produced by the explainable AI module (WURC activity, sending, receiving, displaying and processing data are common and basic functions in computer technology, MPEP 2106.05(d)(II)(i)). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 13 disclose performing univariate outlier detection on the input dataset (mental process) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 14 disclose performing direct identifier detection on the input dataset (mental process) ). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 15 disclose input dataset includes at least one electronic medical health record of a patient (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 16 is rejected under similar rationale to claim 1 and additionally disclose generating an output dataset, datapoints including information that is usable in combination to identify a patient, being de-identified based on the anomaly scores in the output dataset. (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 17 disclose the input dataset is received from a first device at a second device, the second device being remote from the first device; the multivariate outlier detection is performed by the second device; and the output dataset is transmitted from the second device to the first device. . (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 18 similar in scope to claims 1 and 2; therefore it is rejected under similar rationale. In addition, claim 18 disclose “A data processing apparatus for providing an output dataset, the data processing apparatus comprising: an interface unit configured to” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); claim 19 similar in scope to claim 1; therefore it is rejected under similar rationale. In addition, claim 19 disclose “A non-transitory computer program product comprising program elements that, when executed by at least one processor of a system” (“Using a computer as a tool to perform a mental process”, MPEP 2106.05(f)(2)); It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 20 similar in scope to claim 20; therefore it is rejected under similar rationale. claims 21 disclose computing of the partial anomaly scores for at least the portion of the plurality of datapoints is based on a user-selectable preference (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 22 disclose the direct identifier detection is performed on the input dataset using a natural language processing algorithm (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 23 disclose the input dataset includes a plurality of medical health records of a plurality of patients (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 24 disclose explainable AI module includes shapley additive explanations (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea. claims 25 disclose “computer-implemented data protection method according to claim 2, wherein the providing provides the processed dataset to an external device” (data description , which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and mere instructions to “apply” the abstract ideas, which cannot provide an inventive concept. See MPEP 2106.05(f)), It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea.
The dependent claims which impose additional limitations also fail to claim patent eligible subject matter because the limitations cannot be considered statutory. The dependent claim(s) have been examined individually and in combination with the preceding claims, however they do not cure the deficiencies of claim 1 ; where all claims are directed to the same abstract idea, "addressing each claim of the asserted patents [is] unnecessary." Content Extraction &. Transmission LLC v, Wells Fargo Bank, Natl Ass'n, 776 F.3d 1343, 1348 (Fed. Cir. 2014). If applicant believes the dependent claims are directed towards patent eligible subject matter, they are invited to point out the specific limitations in the claim that are directed towards patent eligible subject matter. Claims for the other statutory classes are similarly analyzed.
For at least these reasons, the claimed inventions of each of dependent, are directed or indirect to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more and are rejected under 35 USC 101.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-3, 6, 8-9, 12-20, 22-23, and 25 are rejected under 35 U.S.C. 103 as being unpatentable over “Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts” [hereinafter D1] in view of “Big healthcare data: preserving security and privacy” [hereinafter D2].
With regard to Claim 1,
D1 teach a computer-implemented data protection method, comprising:
receiving an input dataset, the input dataset including a plurality of datapoints, at least some of the plurality of datapoints including information usable in combination to identify a patient (Fig. 1, “data provider”, “secure data upload”, “Data quality assessment”, P.4, Col. 1-2, 2.2.2. Secure sharing of the cohort data, “pseudonymized patient data are uploaded into secure private data databases within the Greek Research and Technology Network (GRNET) cloud infrastructure”, “Both the raw cohort data and the curated cohort data, as well as, the harmonized cohort data are stored in these private cloud databases“, Col. 2, 2.2.3., “pseudonymized data are stored in a tabular format, (ii) each row in the tabular format corresponds to a patient record, and (iii) each column in the tabular format corresponds to a feature (e.g., a laboratory examination)”);
performing multivariate outlier detection on the input dataset, the performing including computing anomaly scores for at least a portion of the plurality of datapoints using a multivariate outlier detection algorithm (P.4, Col. 2, 2.2.3. Data quality assessment, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level“, “multivariate outlier detection methods involve the application of the isolation forests [34–36] and the local outlier factor (LOF)”, “Given a feature vector x from a larger set of n input feature vectors, say X = {x1; x2; _ _ _ ; xn}, the anomaly score is defined as in … Samples with scores very close to 1 are marked as anomalies, whereas samples with scores smaller than 0.5 are inliers”); and
identifying, at least one set of multivariate outlier datapoints among the plurality of datapoints based on the anomaly scores (P.4, Col. 2, 2.2.3. Data quality assessment, “Samples with scores very close to 1 are marked as anomalies, whereas samples with scores smaller than 0.5 are inliers. The Local Outlier Factor (LOF) [34,36] was also used as a density based approach which measures the local density of a given data point with respect to its neighboring points, where the number of nearest neighbors determines the accuracy of the model”), the at least one set of multivariate outlier datapoints being usable (P.4, Col. 2, 2.2.3. “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level”).
D1 teach the ability to detect and identify multivariate outlier datapoints among the plurality of datapoints based on the anomaly scores, and the ability to act upon the identified multivariate outlier datapoints within a data curation and harmonization workflow to improve clinical dataset quality. However, D1 does not explicitly teach datapoints being usable in combination to identify the patient.
D2 teach datapoints being usable in combination to identify the patient. (P.8, “Data masking Masking replaces sensitive data elements with an unidentifiable value. It is not truly an encryption technique so the original value cannot be returned from the masked value. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. Thus, data masking is one of the most popular approach to live data anonymization. k-anonymity”).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of big data analytics in healthcare and protecting the healthcare data privacy. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to protects individual privacy and ensures legal compliance with regulations and prevent breaches of sensitive information and other types of security incidents so as to make effective use of the big healthcare data (D2, P.2, ¶2).
With regard to Claim 2,
D1 disclose the computer-implemented data protection method according to claim 1, further comprising:
automatically de-identifying the at least one set of datapoints to generate a processed dataset (Fig. 1, “GDPR compliance evaluation”, “Secure data upload”, “Data sharing management module”, P.4, Col. 1-2, 2.2.2. Secure sharing of the cohort data “Upon the GDPR compliance of the DPIA and DPA documents, the pseudonymized patient data are uploaded into secure private databases within the Greek Research and Technology Network (GRNET) cloud infrastructure”); set of multivariate outliers (P.4, Col. 2, 2.2.3. Data quality assessment, “Samples with scores very close to 1 are marked as anomalies, whereas samples with scores smaller than 0.5 are inliers. The Local Outlier Factor (LOF) [34,36] was also used as a density based approach which measures the local density of a given data point with respect to its neighboring points, where the number of nearest neighbors determines the accuracy of the model”), and providing the processed dataset ((Fig. 1, “Data sharing management module”, “Data access handler”, Fig. 4).
D1 does not explicitly teach
D2 teach automatically de-identifying the at least one set of multivariate outliers of datapoints to generate a processed dataset; and providing the processed dataset (P.8, “Data masking Masking replaces sensitive data elements with an unidentifiable value. It is not truly an encryption technique so the original value cannot be returned from the masked value. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. Thus, data masking is one of the most popular approach to live data anonymization. k-anonymity”). In other words, D1 teach the ability to identify multivariate outliers of datapoints and to automatically de-identify patient data at a general dataset level. However, D1 does not explicitly teach automatically de-identifying the identified multivariate outliers of datapoints. D2 disclose explicit techniques for automatically de-identify data such as masking, suppression, substitution, and generalization that could be applied to multivariate outliers of datapoints identified by D1.
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of big data analytics in healthcare and protecting the healthcare data privacy. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to prevent breaches of sensitive information and other types of security incidents so as to make effective use of the big healthcare data (D2, P.2, ¶2).
With regard to Claim 3,
D1-D2 teach the computer-implemented data protection method according to claim 2, wherein the automatically de-identifying includes one or more of:
removing a datapoint; rounding a value of a datapoint; substituting a datapoint; categorizing a datapoint; or transforming a datapoint (D2, P.8, “Data masking Masking replaces sensitive data elements with an unidentifiable value. It is not truly an encryption technique so the original value cannot be returned from the masked value. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. Thus, data masking is one of the most popular approach to live data anonymization. k-anonymity”).
The same motivation to combine for claim 2 equally applies for current claim.
With regard to Claim 6,
D1-D2 teach the computer-implemented data protection method according to claim 1, wherein the identifying includes identifying a set of datapoints as the at least one set of multivariate outlier data points in response to an anomaly score of the set of data points exceeding a threshold (D1, Fig. 1, “Federated AI Analytics Model”, Federated AI Analytics training/testing feeding output models, P.4, Col. 2, 2.2.3. Data quality assessment, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level“, “multivariate outlier detection methods involve the application of the isolation forests [34–36] and the local outlier factor (LOF)”, “Given a feature vector x from a larger set of n input feature vectors, say X = {x1; x2; _ _ _ ; xn}, the anomaly score is defined as in … Samples with scores very close to 1 are marked as anomalies, whereas samples with scores smaller than 0.5 are inliers”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 8,
D1-D2 teach the computer-implemented data protection method according to claim 1, wherein the multivariate outlier detection algorithm is a machine learning-based algorithm (D1, P.4, Col. 2, 2.2.3. Data quality assessment, “multivariate outlier detection methods involve the application of the isolation forests [34–36] and the local outlier factor (LOF)”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 9,
D1-D2 teach the computer-implemented data protection method according to claim 1, wherein the multivariate outlier detection algorithm is selected from a group including one or more of: an isolation forest; an elliptic envelope; a fast-minimum covariance determinant estimator; or local outlier factors (D2, P.4, Col. 2, 2.2.3. Data quality assessment, “multivariate outlier detection methods involve the application of the isolation forests [34–36] and the local outlier factor (LOF)”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 12,
D1-D2 teach the computer-implemented data protection method according to claim 1, further comprising:
executing an explainable AI module (D1, P.3, Col. 2, “federated AI analytics module supports the training of supervised machine learning algorithms across the federated cloud databases towards the construction of explainable and trustworthy AI models which are validated on a series of federated and harmonized testing cohort databases”, P.13, Col. 1-2, “In addition, the AI model provides explainable scores which can be used by the clinician to assess the contribution of critical risk factors”); and displaying a result produced by the explainable AI module (D1, Fig. 1, output, P.3, Col. 2, “The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module. The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services, including 3D”, P.13, Col. 1-2, “In addition, the AI model provides explainable scores which can be used by the clinician to assess the contribution of critical risk factors …”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 13,
D1-D2 teach the computer-implemented data protection method according to claim 1, further comprising: performing univariate outlier detection on the input dataset (D1, P.4, Col.2, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level. The univariate methods involve the application of the z-score and the Interquartile Range (IQR) [33] measures”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 14,
D1 disclose the computer-implemented data protection method according to claim 1.
D1 does not explicitly teach performing direct identifier detection on the input dataset.
D2 teach performing direct identifier detection on the input dataset (P.8, “Data masking Masking replaces sensitive data elements with an unidentifiable value. It is not truly an encryption technique so the original value cannot be returned from the masked value. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. Thus, data masking is one of the most popular approach to live data anonymization. k-anonymity”).
D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of big data analytics in healthcare and protecting the healthcare data privacy. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to protects individual privacy and ensures legal compliance with regulations and prevent breaches of sensitive information and other types of security incidents so as to make effective use of the big healthcare data (D2, P.2, ¶2).
With regard to Claim 15,
D1-D2 teach the computer-implemented data protection method according to claim 1, wherein the input dataset includes at least one electronic medical health record of a patient (D1, P. 4, Col. 2, 2.2.3., “pseudonymized data are stored in a tabular format, (ii) each row in the tabular format corresponds to a patient record, and (iii) each column in the tabular format corresponds to a feature (e.g., a laboratory examination)”, P.8, Table 1, “3.1. Cohort data origin A summary of the overall demographic information from the 21 European databases on pSS is presented in Table 1”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 16,
D1-D2 teach a computer-implemented data protection method comprising:
performing the computer-implemented data protection method of claim 1 (D1, P.4, Col. 2, 2.2.3. Data quality assessment, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level“, “multivariate outlier detection methods involve the application of the isolation forests [34–36] and the local outlier factor (LOF)”); and
generating an output dataset (D1, Fig. 1, (Output layer) “High quality and harmonized cohort data”, P.3, Col. 2, “The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module. The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services, including 3D”), datapoints including information that is usable in combination to identify a patient, being de-identified based on the anomaly scores in the output dataset (D1, P.4, Col. 2, 2.2.3. Data quality assessment, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level. The univariate methods involve the application of the z-score and the Interquartile Range (IQR) [33] measures. The multivariate outlier detection methods involve the application of the isolation forests [34–36] and the local outlier factor (LOF)”, “Given a feature vector x from a larger set of n input feature vectors, say X = {x1; x2; _ _ _ ; xn}, the anomaly score is defined as in … Samples with scores very close to 1 are marked as anomalies, whereas samples with scores smaller than 0.5 are inliers”, D2, “P.8, “Data masking Masking replaces sensitive data elements with an unidentifiable value. It is not truly an encryption technique so the original value cannot be returned from the masked value. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. Thus, data masking is one of the most popular approach to live data anonymization. k-anonymity””). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 17,
D1-D2 teach the computer-implemented data protection method according to claim 16, wherein the input dataset is received from a first device at a second device, the second device being remote from the first device (D1, Fig. 1, Data provider – Data processor, “GDPR compliance evaluation”, “Secure data upload”, “Data sharing management module”, Fig. 2,, P.4, Col. 1-2, 2.2.2. Secure sharing of the cohort data “Upon the GDPR compliance of the DPIA and DPA documents, the pseudonymized patient data are uploaded into secure private databases within the Greek Research and Technology Network (GRNET) cloud infrastructure”);
the multivariate outlier detection is performed by the second device (D1, P.3, Col. 1, “this is the first GDPR compliant and federated cloud computing platform which provides easy to use services, to address the clinical unmet needs in pSS”, Fig. 1, “Cohort data analytics layer”, Data processing and analysis including outlier detection are performed in remote processor (in the cloud), Fig. 4, P.4, Col. 2, 2.2.3. Data quality assessment, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level“, P.4, Col. 1-2, 2.2.2. Secure sharing of the cohort data, “pseudonymized patient data are uploaded into secure private data databases within the Greek Research and Technology Network (GRNET) cloud infrastructure”, “Both the raw cohort data and the curated cohort data, as well as, the harmonized cohort data are stored in these private cloud databases“); and
the output dataset is transmitted from the second device to the first device (D1, Fig. 1, “output layer”, , Fig. 4, P.3, Col. 2, “The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module. The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services, including 3D”). The same motivation to combine for claim 1 equally applies for current claim.
With regard to Claim 18,
Claim 18 is similar in scope to claims 1 and 2; therefore it is rejected under similar rationale.
With regard to Claim 19,
Claim 19 is similar in scope to claims 1; therefore it is rejected under similar rationale. D1 further disclose a non-transitory computer program product comprising program elements that, when executed by at least one processor of a system, cause the system to perform the computer-implemented data protection method of claim 1 See at least Fig. 1, “Fig. 1. An illustration of the core modules of the HarmonicSS cloud computing platform”, P.2, Col. 1, ¶1, “Τhe platform was developed under the HarmonicSS EU funded project (HARMONIzation and integrative analysis of regional, national and international Cohorts on primary Sjögren’s Syndrome (pSS) towards improved stratification, treatment and health policy making) [7,9–12,14,15] and removes the need for the installation of local servers or any type of software in each site through the adoption of a federated data management platform which supports a large family of federated AI algorithms yielding interpretable and explainable AI models … this is the first GDPR compliant and federated cloud computing platform which provides easy to use services, to address the clinical unmet needs in pSS”, Col. 2, ¶2, “The federated AI analytics module supports the training of supervised machine learning algorithms across the federated cloud databases towards the construction of explainable and trustworthy AI models which are validated on a series of federated and harmonized testing cohort databases The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module. The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services, including 3D”).
With regard to Claim 20,
Claim 20 is similar in scope to claims 19; therefore it is rejected under similar rationale.
With regard to Claim 22,
D1-D2 teach the computer-implemented data protection method according to claim 14, wherein the direct identifier detection is performed on the input dataset using a natural language processing algorithm (D2, P.2, “Another example is the UNC Health Care (UNCHC), which is a non-profit integrated healthcare system in North Carolina that has implemented a new system allowing clinicians to rapidly access and analyze unstructured patient data using natural-language processing. In fact, UNCHC has accessed and analyzed huge quantities of unstructured content contained in patient medical records to extract insights and predictors of readmission risk for timely intervention, providing safer care for high-risk patients and reducing re-admissions [5].”). The same motivation to combine for claim 14 equally applies for current claim.
With regard to Claim 23,
D1-D2 teach the computer-implemented data protection method according to claim 15, wherein the input dataset includes a plurality of medical health records of a plurality of patients (D1, P. 4, Col. 2, 2.2.3., “pseudonymized data are stored in a tabular format, (ii) each row in the tabular format corresponds to a patient record, and (iii) each column in the tabular format corresponds to a feature (e.g., a laboratory examination)”, P.8, Table 1, “3.1. Cohort data origin A summary of the overall demographic information from the 21 European databases on pSS is presented in Table 1”). The same motivation to combine for claim 15 equally applies for current claim.
With regard to Claim 25,
D1-D2 teach the computer-implemented data protection method according to claim 2, wherein the providing provides the processed dataset to an external device (D1, Col. 2, ¶3, “The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module. The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services …”, D2, P.8, “Data masking Masking replaces sensitive data elements with an unidentifiable value. It is not truly an encryption technique so the original value cannot be returned from the masked value. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. Thus, data masking is one of the most popular approach to live data anonymization. k-anonymity”, P. 4, ¶4, “Privacy … focuses on the use and governance of individual’s personal data like making policies and establishing authorization requirements to ensure that patients’ personal information is being collected, shared and utilized in right ways”). The same motivation to combine for claim 2 equally applies for current claim.
Claim 7 is rejected under 35 U.S.C. 103 as being unpatentable over “Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts” [hereinafter D1] in view of “Big healthcare data: preserving security and privacy” [hereinafter D2] in view of “A Comparative Evaluation of Unsupervised Anomaly Detection Algorithms for Multivariate Data” hereinafter D3 Published 2016.
With regard to Claim 7,
D1-D2 teach the computer-implemented data protection method according to claim 1, wherein the identifying includes identifying a plurality of sets of multivariate outlier datapoints (D1, P.4, Col. 2, 2.2.3. Data quality assessment, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level“, “multivariate outlier detection methods involve the application of the isolation forests [34–36] and the local outlier factor (LOF)”, “Given a feature vector x from a larger set of n input feature vectors, say X = {x1; x2; _ _ _ ; xn}, the anomaly score is defined as in … Samples with scores very close to 1 are marked as anomalies, whereas samples with scores smaller than 0.5 are inliers”). The same motivation to combine for claim 14 equally applies for current claim.
D1-D2 does not explicitly teach the method further includes displaying a ranking of the plurality of sets of multivariate outliers of datapoints based on respective anomaly scores on a user interface.
D3 teach identifying includes identifying a plurality of sets of multivariate outliers of datapoints, and the method further includes displaying a ranking of the plurality of sets of multivariate outliers of datapoints based on respective anomaly scores on a user interface (P.4, “As an output of an anomaly detection algorithm, two possibilities exist. First, a label can be used as a result indicating whether an instance is an anomaly or not. Second, a score or confidence value can be a more informative result indicating the degree of abnormality. For supervised anomaly detection, often a label is used due to available classification algorithms. On the other hand, for semi-supervised and unsupervised anomaly detection algorithms, scores are more common. This is mainly due to the practical reasons, where applications often rank anomalies and only report the top anomalies to the user. In this work, we also use scores as output and rank the results such that the ranking can be used for performance evaluation. Of course, a ranking can be converted into a label using an appropriate threshold”).
D1-D2 and D3 are analogous art to the claimed invention because they are from a similar field of endeavor of anomaly detection algorithms for multivariate Data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by D3 with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2 as described above to allow reporting the top anomalies to the user and usage of ranking for performance evaluation (D3, P.4, “This is mainly due to the practical reasons, where applications often rank anomalies and only report the top anomalies to the user. In this work, we also use scores as output and rank the results such that the ranking can be used for performance evaluation”).
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over “Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts” [hereinafter D1] in view of “Big healthcare data: preserving security and privacy” [hereinafter D2] in view of Li et al. [US 12182307 B1, hereinafter Li].
With regard to Claim 10,
D1-D2 teach the computer-implemented data protection method according to claim 9, further comprising:
receiving user input directed to at least one datapoint in the at least one set of multivariate outlier data- points (D1, Fig. 1, Fig. 4 Col. 2, ¶3, “The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module. The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services …”).
D1-D2 does not explicitly teach user input being directed to de-identifying the at least one datapoint; and training the multivariate outlier detection algorithm based on the user input.
Li teach receiving user input directed to at least one datapoint in the at least one set of multivariate outliers of data- points, the user input being directed to de-identifying the at least one datapoint (Col. 4-5, lines 58-3, “Corrections from an authoritative human source (e.g., a human), allow a machine learning-based system to request feedback from the authoritative human source. For PHI detection, the feedback may be in the form of asking the authoritative human source how to classify difficult annotations, or by providing the authoritative human source with a new subset of documents to annotate/correct”); and training the multivariate outlier detection algorithm based on the user input (Col. 4-5, lines 58-3, “With each feedback and correction from the authoritative human source, the inventive system learns to correct mistakes, and update a PHI de-identification model or set of rules for future PHI de-identification, causing a next iteration of detection to be more accurate by incorporating lessons learned from the previous iteration. Ideally, over time the authoritative human source may need to make fewer corrections, and merely reviews the correctness of the automated results.”).
D1 and Li are analogous art to the claimed invention because they are from a similar field of endeavor of protecting health information. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by Li with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above to allow the system to learns to correct mistakes, and update a PHI de-identification model or set of rules for future PHI de-identification, causing a next iteration of detection to be more accurate by incorporating lessons learned from the previous iteration (Li, (Col. 4-5, lines 58-3).
Claims 4-5, 11 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over “Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts” [hereinafter D1] in view of “Big healthcare data: preserving security and privacy” [hereinafter D2] in view of Callot et al. [US 12265446 B1, hereinafter Callot].
With regard to Claim 4,
D1-D2 teach the computer-implemented data protection method according to claim 1, further comprising:
displaying the input dataset on a user interface (D1, Fig. 4, (UI), P.3, Col. 1, 2. Materials and methods 2.1. Overview “The HarmonicSS platform includes a wealth of harmonized cohort databases on top of which the core modules operate.”, Col. 2, ¶3, “The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module. The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services …”) with the at least one set of multivariate outlier (Fig. 1, “Visual analytics and user interfaces module”, P.4, Col. 2, 2.2.3.1. “Both the raw cohort data and the curated cohort data, as well as, the harmonized cohort data are stored in these private cloud databases“, Col. 2, 2.2.3., “Then, the data curation workflow ensures that the structure of each shared dataset fulfills the following requirements: (i) the shared pseudonymized data are stored in a tabular format, (ii) each row in the tabular format corresponds to a patient record, and (iii) each column in the tabular format corresponds to a feature (e.g., a laboratory examination)”, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level”, P.3, Col. 2, ¶3, “The outcomes of the modules from the cohort data management layer and the cohort data analytics layer are presented to the users of the platform through the visual analytics and user interfaces module. The latter provide highly interactive graphical user interfaces (GUIs) and visual analytics services …”). The same motivation to combine for claim 1 equally applies for current claim.
D1-D2 does not explicitly teach datapoints highlighted.
Callot teach displaying the input dataset on a user interface with the at least one set of multivariate outlier datapoints highlighted (Col. 16, lines 1-7, “Web-based interface 802 implemented by an analytics service similar to analytics service 102 of FIG. 1 includes a message area 877 and several metric time series graphs associated with a detected anomaly in the depicted embodiment”, Col. 16, lines 25-30, “In the depicted embodiment, metrics M3 (whose values for approximately an hour before the detection of the divergence-based anomaly are shown in graph 807) and M4 (whose values over the same duration are shown in graph 809) have been identified as potential candidates for further analysis”, Col. 4-5, lines 63-2, “Such an interface may indicate observed values of one or more metrics that have been identified as potentially likely to be relevant to the anomaly, and may include visual cues (such as icons or text notations) indicating particular changes in the metrics during the time interval which led to the identification of the metrics as candidates for further analysis”).
D1-D2 and Callot are analogous art to the claimed invention because they are from a similar field of endeavor of anomaly detection. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by Callot with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2 as described above to help with the debugging and resolution of the root causes of the anomaly (Callot, Col. 4, lines, 62-67). This is use of known technique to improve similar devices (methods, or products) in the same way and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 5,
D1-D2-Callot teach the computer-implemented data protection method according to claim 4, further comprising:
receiving user input via the user interface, the user input being directed to the at least one set of multivariate outlier datapoints (D1, P.7, 2.6. Visual analytics and user interfaces module, “The visual analytics methods were implemented using the HealthVision web visualization platform [46] which consists of visualization and data analysis components that are linked to each other in reactive workflows. Each component accepts specific inputs, either from other components or from the user, and produces outputs that can be used by other components, or renders visual components (input controls, etc.) on the screen”, Callot, “Col. 14-15, lines 64-12, “A client 710 may provide a set of preferences regarding the anomaly detection plan”, Col. 5, lines 25-34, “multi-factor anomaly detection … in response to one or more programmatic requests directed to the analytics service by its clients. Clients may utilize the programmatic interfaces supported by the analytics service to specify various preferences pertaining to the analysis, including for example specific algorithms or models to be used, hyper-parameters of the machine learning models”);
processing the input dataset according to the user input to generate a processed dataset; and providing the processed dataset (D1, Fig. 1, Fig. 2, Fig. 4, “Visual analytics and user interfaces module”, “Data access handler”, “output”, “High quality and harmonized cohort data”, P.7, 2.6. Visual analytics and user interfaces module, “Each component accepts specific inputs, either from other components or from the user, and produces outputs that can be used by other components, or renders visual components (input controls, etc.) on the screen. The user interface (UI) serves as connecting link between the platform user and the backend services (Fig. 4)”, “backend server is responsible for orchestrating: (i) access to the REST (Representational state transfer) services of other users, including user authentication, execution of data analytics services, data sharing management, etc. (ii) access to the cloud file storage, where the backend server handles file transfer to the cloud and manages directory structures for services that require file uploads via the user interface, (iii) access to the MySQL databases of prospective cohort data”, Callot, Fig. 2-4, Fig. 8, “Col. 14-15, lines 64-12, “A client 710 may provide a set of preferences regarding the anomaly detection plan”, Col. 5, lines 25-34, “multi-factor anomaly detection … in response to one or more programmatic requests directed to the analytics service by its clients. Clients may utilize the programmatic interfaces supported by the analytics service to specify various preferences pertaining to the analysis, including for example specific algorithms or models to be used, hyper-parameters of the machine learning models”, computation of aggregated anomaly scores 251 for various points in time. A given aggregated anomaly score S (t) may, for example, be generated based on determining where along the range of predicted values some number of post-prediction values lie, mapping the placement of the values to respective anomaly score contributions, and combining the anomaly score contributions.”, Col. 12, lines 15-18, “anomaly score contributions with respect to each of the three predictions and the value of the metric at the measurement time may be computed and aggregated …”). The same motivation to combine for claim 4 equally applies for current claim.
With regard to Claim 11,
D1-D2 teach the computer-implemented data protection method according to claim 1, plurality of different multivariate outlier detection algorithms and to generate the anomaly scores (D1, Fig. 1, “Federated AI Analytics Model”, Federated AI Analytics training/testing feeding output models, P.4, Col. 2, 2.2.3. Data quality assessment, “The outlier detection stage of the data curation workflow involves the accurate detection and subsequent elimination of feature values that significantly deviate from the standard distribution of the clinical data either on a univariate or on a multivariate level“, “multivariate outlier detection methods involve the application of the isolation forests [34–36] and the local outlier factor (LOF)”, “Given a feature vector x from a larger set of n input feature vectors, say X = {x1; x2; _ _ _ ; xn}, the anomaly score is defined as in … Samples with scores very close to 1 are marked as anomalies, whereas samples with scores smaller than 0.5 are inliers”). The same motivation to combine for claim 1 equally applies for current claim.
D1-D2 does not explicitly teach performing includes computing partial anomaly scores for at least the portion of the plurality of datapoints using a plurality of different multivariate outlier detection algorithms; and the computing of the anomaly scores includes aggregating the partial anomaly scores to generate the anomaly scores.
Callot teach wherein the performing includes computing partial anomaly scores for at least the portion of the plurality of datapoints using a plurality of different multivariate outlier detection algorithms (Fig. 2, Col. 8-9, lines 57-22, “Several different types of probabilistic forecasting models may be employed in different embodiments … In some embodiments, a set of per-time-series probabilistic forecasting models such as 230A, 230B or 230C may be used. Each per-time-series model, such as FM1, FM2, FM3, FM4, FM5 or FM6 … instead of or in addition to per-time-series forecasting models, one or more joint forecasting models 265 may be employed. Such a joint forecasting model 265 may consume more than one time series as input, and generate combined or joint probability distributions for all the input time series for various time horizons or lead times. (32) Using the different models available, a set of forecasts 240 comprising respective probability distributions as a function of time may be obtained … resulting in the computation of aggregated anomaly scores 251 for various points in time. A given aggregated anomaly score S (t) may, for example, be generated based on determining where along the range of predicted values some number of post-prediction values lie, mapping the placement of the values to respective anomaly score contributions, and combining the anomaly score contributions.”, Col. 12, lines 5-7, “Another option with respect to temporal combinations is to consider forecasts obtained for a given time from multiple runs (or multiple versions) of a forecasting model …”, Col. 12, lines 15-18, “anomaly score contributions with respect to each of the three predictions and the value of the metric at the measurement time may be computed and aggregated …”); and
the computing of the anomaly scores includes aggregating the partial anomaly scores to generate the anomaly scores (Col. 8-9, lines 57-22, “Several different types of probabilistic forecasting models may be employed in different embodiments … In some embodiments, a set of per-time-series probabilistic forecasting models such as 230A, 230B or 230C may be used. Each per-time-series model, such as FM1, FM2, FM3, FM4, FM5 or FM6 … instead of or in addition to per-time-series forecasting models, one or more joint forecasting models 265 may be employed. Such a joint forecasting model 265 may consume more than one time series as input, and generate combined or joint probability distributions for all the input time series for various time horizons or lead times. (32) Using the different models available, a set of forecasts 240 comprising respective probability distributions as a function of time may be obtained … resulting in the computation of aggregated anomaly scores 251 for various points in time. A given aggregated anomaly score S (t) may, for example, be generated based on determining where along the range of predicted values some number of post-prediction values lie, mapping the placement of the values to respective anomaly score contributions, and combining the anomaly score contributions.”, Col. 8, lines 3-12, “ After the aggregated anomaly score is computed at a particular point in time, the anomaly detector may determine whether the score exceeds a threshold in the depicted embodiment … If the score exceeds the threshold, one or more anomaly response operations may be initiated”, Col. 13, lines 14-19, “combination of the anomaly score contributions with respect to MTS1 and MTS2 satisfy a different threshold. Depending on the anomaly detection plan, in some embodiments only alarms/actions triggered by aggregated scores may be initiated”, Col. 13, lines 60-64, “In order to generate the overall score, the anomaly detector may aggregate a plurality of anomaly score contributions corresponding to different factors and parameters indicated in the anomaly detection plan”, Col. 14-15, lines 64-12, “A client 710 may provide a set of preferences regarding the anomaly detection plan”, Col. 5, lines 25-34, “multi-factor anomaly detection technique of the kind introduced above may be performed at a network-accessible analytics service of a provider network, e.g., in response to one or more programmatic requests directed to the analytics service by its clients. Clients may utilize the programmatic interfaces supported by the analytics service to specify various preferences pertaining to the analysis, including for example specific algorithms or models to be used, hyper-parameters of the machine learning models (such as the features to be used for time series forecasting, etc.), and so on”).
D1-D2 and Callot are analogous art to the claimed invention because they are from a similar field of endeavor of anomaly detection. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by Callot with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2 as described above to improve detection accuracy, increase robustness to noise, and enhance the interpretability and reliability of the overall anomaly detection system. This is use of known technique to improve similar devices (methods, or products) in the same way and applying a known technique to a known device (method, or product) ready for improvement to yield predictable results (MPEP 2143).
With regard to Claim 21,
D1-D2-Callot teach the computer-implemented data protection method according to claim 11, wherein the computing of the partial anomaly scores for at least the portion of the plurality of datapoints is based on a user-selectable preference (Callot, Col. 14-15, lines 64-12, “A client 710 may provide a set of preferences regarding the anomaly detection plan”, Col. 5, lines 25-34, “multi-factor anomaly detection technique of the kind introduced above may be performed at a network-accessible analytics service of a provider network, e.g., in response to one or more programmatic requests directed to the analytics service by its clients. Clients may utilize the programmatic interfaces supported by the analytics service to specify various preferences pertaining to the analysis, including for example specific algorithms or models to be used, hyper-parameters of the machine learning models (such as the features to be used for time series forecasting, etc.), and so on”). The same motivation to combine for claim 11 equally applies for current claim.
Claim 24 is rejected under 35 U.S.C. 103 as being unpatentable over “Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts” [hereinafter D1] in view of “Big healthcare data: preserving security and privacy” hereinafter D2 in view of Lim [US 20220148093 A1].
With regard to Claim 24,
D1-D2 teach the computer-implemented data protection method according to claim 12.
D1-D2 does not teach explainable AI module includes shapley additive explanations.
Lim teach explainable AI module includes shapley additive explanations (¶74, “Explainable AI modules adopts the Shapley value framework, which is built upon cooperative game theory and focus on local explanation that is model agnostic. The Shapley value of a feature for a query point explains the contribution of the feature to a prediction (response for regression or score of each class for classification) at the specified query point. The Shapley value corresponds to the deviation of the prediction for the query point from the average prediction, due to the feature. For each query point, the sum of the Shapley values for all features corresponds to the total deviation of the prediction from the average …”).
D1-D2 and Lim are analogous art to the claimed invention because they are from a similar field of machine learning based analysis of data. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1-D2 resulting in resolutions as disclosed by Callot with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-D2 as described above to explain the contribution of each individual feature to a specific prediction at a specified query point (Lim ¶74).
Response to Arguments
Applicant argue that the claims does not disclose mathematical concept (Remarks P. 11).
Examiner respectfully disagrees, computing anomaly score and comparing values is a mathematical concept.
Applicant argue that "performing multivariate outlier detection on the input dataset, the performing including computing anomaly scores for at least a portion of the plurality of datapoints using a multivariate outlier detection algorithm," as previously recited by claim 1, is clearly not practically performable in the human mind, even using a pen and paper.
Examiner respectfully disagrees, the argued limitations could practically performable in the human mind using a pen and paper.
Applicant argue that claim 1 as a whole is directed to improved devices and methods for detecting multivariate outliers with greater accuracy (Remarks 11-13).
Examiner respectfully disagrees, the current claims does not disclose extra elements that are not "well-understood, routine, or conventional" (WURC) in the related arts and the actual improvement cannot be the abstract idea. As an improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology. For example, in Trading Technologies Int’l v. IBG, 921 F.3d 1084, 1093-94, 2019 USPQ2d 138290 (Fed. Cir. 2019), the court determined that the claimed user interface simply provided a trader with more information to facilitate market trades, which improved the business process of market trading but did not improve computers or technology MPEP 2106.05(a)(II).
Same analysis is applied on claim 18 and the claim is not eligible for the same reasons provided for rejecting claim 1.
Applicant’s arguments, see Remarks P. 16, filed 3/2/2026, with respect to the rejection(s) of claim(s) 1 and 18 under 35 USC 102(a)(1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of “Big healthcare data: preserving security and privacy” [hereinafter D2]. D1 teach the ability to detect and identify multivariate outlier datapoints among the plurality of datapoints based on the anomaly scores, and the ability to act upon the identified multivariate outlier datapoints within a data curation and harmonization workflow to improve clinical dataset quality. However, D1 does not explicitly teach datapoints being usable in combination to identify the patient.
D2 teach datapoints being usable in combination to identify the patient. (P.8, “Data masking Masking replaces sensitive data elements with an unidentifiable value. It is not truly an encryption technique so the original value cannot be returned from the masked value. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. Thus, data masking is one of the most popular approach to live data anonymization. k-anonymity”). D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of big data analytics in healthcare and protecting the healthcare data privacy. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to protects individual privacy and ensures legal compliance with regulations and prevent breaches of sensitive information and other types of security incidents so as to make effective use of the big healthcare data (D2, P.2, ¶2).
Applicant argue that the resultant combination fails to render claims 2-5, 7,10-11, 14, 18, 21-22 and 24 obvious because Abouelmehdi, Goldstein, Li, Callot and/or Lim suffer from at least the same deficiencies as Pezoulas with regard to independent claim 1.
Examiner respectfully disagrees, D1 teach the ability to detect and identify multivariate outlier datapoints among the plurality of datapoints based on the anomaly scores, and the ability to act upon the identified multivariate outlier datapoints within a data curation and harmonization workflow to improve clinical dataset quality. However, D1 does not explicitly teach datapoints being usable in combination to identify the patient. D2 teach datapoints being usable in combination to identify the patient. (P.8, “Data masking Masking replaces sensitive data elements with an unidentifiable value. It is not truly an encryption technique so the original value cannot be returned from the masked value. It uses a strategy of de-identifying data sets or masking personal identifiers such as name, social security number and suppressing or generalizing quasi-identifiers like date-of-birth and zip-codes. Thus, data masking is one of the most popular approach to live data anonymization. k-anonymity”). D1 and D2 are analogous art to the claimed invention because they are from a similar field of endeavor of big data analytics in healthcare and protecting the healthcare data privacy. Thus, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to modify D1 resulting in resolutions as disclosed by D2 with a reasonable expectation of success. One of ordinary skill in the art would be motivated to modify D1 as described above to protects individual privacy and ensures legal compliance with regulations and prevent breaches of sensitive information and other types of security incidents so as to make effective use of the big healthcare data (D2, P.2, ¶2).
As to the remaining dependent claims, applicant argue that they are allowable due to their respective direct and indirect dependencies upon one of the aforementioned Independent claims. The examiner respectfully disagrees, Independent claims were not allowable as stated in the paragraph above in this “Response to Arguments” section in this office action.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 20200380117 filed by Marwah et al. that disclose the ability to calculate multiple anomaly scores that could be aggregated for anomaly detection
Examiner has pointed out particular references contained in the prior arts of record in the body of this action for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and Figures may apply as well. It is respectfully requested from the applicant, in preparing the response, to consider fully the entire references as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior arts or disclosed by the examiner. It is noted that any citation to specific pages, columns, figures, or lines in the prior art references any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331-33, 216 USPQ 1038-39 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 USPQ 275, 277 (CCPA 1968)).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). 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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MOHAMED ABOU EL SEOUD whose telephone number is (303)297-4285. The examiner can normally be reached Monday-Thursday 9:00am-6:00pm MT.
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/MOHAMED ABOU EL SEOUD/Primary Examiner, Art Unit 2148
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