DETAILED ACTION
This office action is responsive to the response filed 6/2/2026. The application contains claims 1-20, 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.
Claim limitations in amended claims 1, 8, and 20 have 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 “module” 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 1 recites the limitation "data set scan module”, “a transfer learning module”, “a classifier” coupled with functional language without reciting sufficient structure to achieve the function.
Claim 8 recites the limitation “a transfer learning module”, “a classifier”, coupled with functional language without reciting sufficient structure to achieve the function.
Claim 15 recites the limitation a transfer learning module”, “a classifier” 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, claims 1 and 8, and 20 are 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: Paragraph [0018] states, “a module can be implemented as a hardware circuit including custom very large scale integration (VLSI) circuits or gate arrays, off-the-shelf semiconductors such as logic chips, transistors, or other discrete components. A module can also be implemented in programmable hardware devices such as field programmable gate arrays, programmable array logic, programmable logic devices or the like. Modules can also be implemented in software for execution by various types of processors”. 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 § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-20 rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. The term “transfer learning related tasks” in claims 1, 8, and 15 is a relative term which renders the claim indefinite. The term “related” is not defined by the claim, the specification does not provide a standard for ascertaining the requisite degree, and one of ordinary skill in the art would not be reasonably apprised of the scope of the invention. The claims are indefinite as they fail to define the metes and bounds of the claimed invention (i.e. what tasks could be considered related and what tasks are not).
Dependent claims inherit the Independent claims deficiency.
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 7, 14, and 20 rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 7 disclose similar limitation. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements.
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 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, 8 and 15 are each directed to a statutory category, it recites a series of steps which appears to be directed to an abstract idea (mental process, mathematical concept).
Claims 1-20 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 Claim 1, 8, and 15, 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 used for outlier data detection 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:
“scan data set sources to extract therefrom labeled inlier data and labeled outlier data”, “learning patterns and relationships that govern the labeled inlier data and the labeled outlier data”, “using the learned patterns and relationships to uncover one or more functions for converting a data set from its current format to a one-size-fits-all data format; and generating transformed features by extracting features from data in the one-size- fits-all data format”, “use the one or more functions to convert new and unseen data of a data set having its own data format from a current data format of the new and unseen data to the one-size-fits-all data format”, “apply the outlier classification model to the new and unseen data in the one-size-fits-all data format to predict whether the new and unseen data is an outlier” (Mental process, observation, evaluation and judgment).
The claim recites additional elements as
“An outlier detection system” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
“a data set scan module configured“ (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)).
“wherein the labeled inlier data are diverse and the labeled outlier data are diverse” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h),
“a transfer learning module; and a classifier configured to work with the transfer learning module to perform transfer- learning-related tasks that comprise using model parameters of a first task to develop a second task wherein the first task” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)),
“wherein the second task comprises the classifier configured using the transformed features, inlier labels of the labeled inlier data, and outlier labels of the labeled outlier data to generate a supervised learning problem on which an outlier classification model of the classifier is trained” , “wherein the transfer learning module, after being trained, is configured to use the one or more functions” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)),
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:
“An outlier detection system” (“Using a computer as a tool to perform a mental process”, MPEP 2106.04(a)(2)(III)(C));
“a data set scan module configured“ (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)).
“wherein the labeled inlier data are diverse and the labeled outlier data are diverse” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h),
“a transfer learning module; and a classifier configured to work with the transfer learning module to perform transfer- learning-related tasks that comprise using model parameters of a first task to develop a second task wherein the first task” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)),
“wherein the second task comprises the classifier configured using the transformed features, inlier labels of the labeled inlier data, and outlier labels of the labeled outlier data to generate a supervised learning problem on which an outlier classification model of the classifier is trained” , “wherein the transfer learning module, after being trained, is configured to use the one or more functions” (merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)).
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 15 recites a system comprising “A computer program product for outlier detection, the computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor system” configured to perform the same steps of the computer system as set forth in claim 1, the added element of “A computer program product for outlier detection, the computer program product comprising a computer readable program stored on a computer readable storage medium, wherein the computer readable program, when executed on a processor system, causes the processor system” 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.
Claim 15 is therefore rejected according to the same findings and rationale as provided above.
Independent claims 8 and 15 are the same analogy and rejected using similar analysis as claim 1.
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 “The outlier detection system of claim 1, wherein: the data set sources comprise 10,000 or more data set sources; and the transfer learning module uses a network of pipelines to learn the patterns and the relationships” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 3 disclose “The outlier detection system of claim 2, wherein: the labeled inlier data and the labeled outlier data of each of the data set sources are segmented based on characteristics of the labeled inlier data and the labeled outlier data; and a pipeline of the network of pipelines is created for each of the data sources and for each of the characteristics of the labeled inlier data and labeled outlier data” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 4 disclose “The outlier detection system of claim 3, wherein the characteristics are based at least in part on distinctions between formats of the labeled inlier data and the labeled outlier data” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 5 disclose “The outlier detection system of claim 4, wherein the first task further comprises the transfer learning module using the patterns and relationships that govern the labeled inlier data and the labeled outlier data to” (The module usage is merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h), description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h)), “generate anomaly scores associated with outputs generated by each of the pipelines of the network of pipelines” (Mental process). It does not integrate the abstract idea into a practical application and did not add significantly more to the abstract idea, claims 6 disclose “The outlier detection system of claim 5, wherein generating the transformed features is further based at least in part on the anomaly scores” (description of data, which is directed to generally linking the use of a judicial exception to a particular technological environment or field of use and merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) and the actual step of generating the transformed features based on anomaly score is mental process). 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 outlier detection system of claim 1, wherein the transfer learning module is configured (a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h) to use the one or more functions to convert the new and unseen data of a data set having its own data format from a current data format of the new and unseen data to the one-size-fits-all data format” (Mental process).
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 claims 2-7, 9-14 and 16-20,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 § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1, 7-8, 14-15, and 20 are rejected under 35 U.S.C. 102(a)(1) and 35 U.S.C. 102(a)(2) as being anticipated by GIETZEN et al . [US 2022/0245801 A1, hereinafter D1].
With regard to Claim 1,
D1 teach outlier detection system comprising: a data set scan module configured to scan data set sources to extract therefrom labeled inlier data and labeled outlier data (¶56, “genotyping instruments 111, also referred to as genotyping scanners and genotyping platforms. The network(s) 155 couples the genotyping instruments 111, the process cycle images database 115, the failure categories labels database 117, the labeled process cycle images database 138, the trained good vs. bad classifier 151, the basis of Eigen images database 168, the trained root cause classifier 171, and the feature generator 185, in communication with one another”, ¶57, “Illumina's BeadChip imaging systems such as ISCAN™ system. The instrument can detect fluorescence intensities of hundreds to millions of beads arranged in sections on mapped locations on image generating chips”, ¶¶60-61, “The labeled training examples can comprise of successful (good) and unsuccessful (bad) process cycle images. The unsuccessful process cycle images are labeled as belonging to one of the six failure categories listed“), wherein the labeled inlier data are diverse and the labeled outlier data are diverse (¶43, “The failed analyses currently are understood to fit in five categories plus a residual failure category. The five failure categories are hybridization or hyb failures, spacer shift failures, offset failures, surface abrasion failures and reagent flow failures. The residual category is unhealthy patterns due to mixed effects, unidentified causes, and weak signals”, ¶57, “The genotyping instruments can be used in a wide variety of physical environments and operated by technicians of varying skills levels. The sample preparation can take two to three days and can include manual and automated handling of samples”, ¶61, “unsuccessful process cycle images are labeled as belonging to one of the six failure categories“, “size of the training data set is increased up to 75,000 training examples using feature engineering techniques. The size of the training database can increase as more labeled image data is collected from laboratories using the genotyping instruments”);
a transfer learning module (¶62, “96 weights for components of labeled images are used to train the classifiers. The basis of Eigen images can be stored in the database 168”, ¶125, “we used 96 weights of components of labeled production images to train random forest classifiers”); and
a classifier configured to work with the transfer learning module to perform transfer-learning-related tasks that comprise using model parameters of a first task to develop a second task (¶128, “the weights of the 96 components (or the Eigen images) are adjusted so that the prediction error is reduced”, ¶131, “weights of the Eigen images are learned during the training of the classifier as described above”) wherein the first task comprises:
learning patterns and relationships that govern the labeled inlier data and the labeled outlier data (¶44, “From tens of thousands of labeled images, a linear basis of 40 to 100 or more image components was identified. One approach to forming an Eigen basis was principal component analysis (PCA) followed by rank ordering of components according to a measure of variability explained … Each image to be analyzed by Eigen image analysis is represented as a weighted linear combination of basis images. Each weight for the ordered set of basis components is used as a feature for training a classifier”, ¶74, “The PCA technique finds vectors that best account for the distribution of section images within the entire image space”, ¶124, “The labeled images include both good images from successful production cycles and failed images from unsuccessful production cycles”, Eigen basis from the labeled corpus is the learned pattern, weighted linear combination is learned relationships between images and those patterns, ¶43, “The five failure categories are hybridization or hyb failures, spacer shift failures, offset failures, surface abrasion failures and reagent flow failures. The residual category is unhealthy patterns due to mixed effects, unidentified causes, and weak signals”);
using the learned patterns and relationships to uncover one or more functions for converting a data set from its current format to a one-size-fits-all data format (¶62, “The production cycle images are represented as a weighted linear combination of basis images for input to classifiers”, ¶¶64-65, “The convolutional neural networks (CNNs) applied by the technology disclosed require square shaped input images. Therefore, the system includes logic to position rectangular shaped (J×K pixels) section images into square shaped (M×N pixels) analysis frames”, ¶112, “The resulting flattened section images are a one-dimensional array, i.e., 14,400×1 pixels each”); and
generating transformed features by extracting features from data in the one-size- fits-all data format (¶82, “Each weight of the ordered set of basis components is used as a feature for training the classifier. For instance, in one implementation, 96 weights for components of labeled images were used to train the classifier”);
wherein the second task comprises the classifier configured using the transformed features, inlier labels of the labeled inlier data, and outlier labels of the labeled outlier data to generate a supervised learning problem on which an outlier classification model of the classifier is trained (¶128, “the good vs. bad classifier uses the image description features from the training data and applies one-vs-the-rest (OvR) classification of the good class (or healthy labeled images) versus the multiple bad classes”);
wherein the transfer learning module, after being trained, is configured to use the one or more functions to convert new and unseen data of a data set having its own data format from a current data format of the new and unseen data to the one-size-fits-all data format (¶71, “Higher resolution images obtained from genotyping instruments or scanners can require more computational resources to process. The images obtained from genotyping scanners are resized by the image scaler 237 so that images of sections of image generating chips are analyzed at a reduced resolution such as 180×80 pixels”, “images of the sections obtained from the scanner are at a resolution of 3600×1600 pixels. In another implementation, images of sections obtained from the scanner are at a resolution of 3850×1600 pixels”, ¶62, ¶112, “The resulting flattened section images are a one-dimensional array, i.e., 14,400×1 pixels each”, ¶131, “The trained classifier accesses a basis of Eigen images with which to analyze a production image. The trained classifier creates image description features for the production image based on linear combination of Eigen images. The weights of the Eigen images are learned during the training of the classifier as described above”)); and
wherein the classifier is configured to apply the outlier classification model to the new and unseen data in the one-size-fits-all data format to predict whether the new and unseen data is an outlier (¶131-132, “if the image is classified as a bad image indicating a failed process cycle (step 5), the system invokes root cause classifier 171”, ¶133).
With regard to Claim 7,
D1 teach the outlier detection system of claim 1, wherein the transfer learning module is configured to use the one or more functions to convert the new and unseen data of a data set having its own data format from a current data format of the new and unseen data to the one-size-fits-all data format (¶71, “Higher resolution images obtained from genotyping instruments or scanners can require more computational resources to process. The images obtained from genotyping scanners are resized by the image scaler 237 so that images of sections of image generating chips are analyzed at a reduced resolution such as 180×80 pixels”, “images of the sections obtained from the scanner are at a resolution of 3600×1600 pixels. In another implementation, images of sections obtained from the scanner are at a resolution of 3850×1600 pixels”, ¶62, ¶112, “The resulting flattened section images are a one-dimensional array, i.e., 14,400×1 pixels each”, ¶131, “The trained classifier accesses a basis of Eigen images with which to analyze a production image. The trained classifier creates image description features for the production image based on linear combination of Eigen images. The weights of the Eigen images are learned during the training of the classifier as described above”).
With regard to Claim 8,
Claim 8 is similar in scope to claim 1; therefore it is rejected under similar rationale.
With regard to Claim 14,
Claim 14 is similar in scope to claim 7; therefore it is rejected under similar rationale.
With regard to Claim 15,
Claim 15 is similar in scope to claim 1; therefore it is rejected under similar rationale.
With regard to Claim 20,
Claim 20 is similar in scope to claim 7; therefore it is rejected under similar rationale.
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 2, 9, and 16 are rejected under 35 U.S.C. 103 as being unpatentable over GIETZEN et al . [US 2022/0245801 A1, hereinafter D1] in view of Fan et al. [US 2021/0303585 A1, hereinafter Fan].
With regard to Claim 2,
D1 teach the outlier detection system of claim 1.
D1 does not disclose the data set sources comprise 10,000 or more data set sources ; and the transfer learning module uses a network of pipelines to learn the patterns and the relationships.
Fans teach data set sources comprise 10,000 or more data set sources (¶36, “ data sources 125 may include network devices, e.g., routers, switches, multiplexers, firewalls, traffic shaping devices or systems, base stations, remote radio heads, baseband units, gateways, and so forth”, ¶49, “sources (e.g., all routers within a given network region, all devices in a subnet, all base stations in a selected state, wind speed information for a selected geographic area for a selected time period, all captured images or video in a selected area for a selected period of time, etc.)”); and the transfer learning module uses a network of pipelines to learn the patterns and the relationships (Fig. 4, ¶2, “A data pipeline is a set of data processing elements connected in series, where the output of one element is the input of the next. The elements of a data pipeline may operate in parallel or in a time-sliced fashion”, ¶29, “the data blending module may create two data pipelines (or two separate paths in a single pipeline) to allow source data to be delivered to a target in multiple paths”, ¶65, “data pipeline 121 for an additional duration so as to obtain the additional data associated with the time period of the new request”, ¶126, “the plan may be for configuring two data pipelines (which may alternatively or additionally be considered to be a single, shared data pipeline)”, ¶36, “data from various data sources 125 may be filtered and transformed to achieve one or more data sets and/or subsets of data that can be common across a set of data pipelines and data pipeline instances “, “the targets 129 may comprise various devices and/or processing systems, which may include various machine learning (ML) modules hosting one or more machine learning models (MLMs). For instance, a first one of the targets 129 may comprise a MLM to process image data and may be trained to recognize images of different animals, a second one of the targets 129 may comprise a MLM to process financial data and may be trained to recognize and alert for unusual account activity, and so forth”, ¶12).
Examiner further notes that Fans disclose the ability of scanning plural data set sources to extract data. data set sources include network devices, routers, switches, base stations, and gateways (¶36), and scopes such as all routers within a given network region, all devices in a subnet, all base stations in a selected state (¶49). Fans does not disclose a specific number of data set sources. However, the claimed threshold of ten thousand or more sources is a matter of a degree; the system perform the same function of scanning data set sources to extract data regardless of how many sources are scanned. Nothing in the specification establish that ten thousand is critical, produce unexpected results or cause the system to operate differently than at a smaller number. On the contrary, the specification disclose an implementation using 520 data sets with 6M records (¶¶50-51). Where the general conditions of a claim are disclosed in the prior art, discovering an optimum or workable range involves only routine skill in the art. In re Aller, 220 F.2d 454, 456, 105 USPQ 233, 235 (CCPA 1955) (MPEP 2144.05 II). Selecting a number of data set sources would therefore have been obvious to one of ordinary skill in the art.
D1 and Fans are analogous art to the claimed invention because they are from a similar field of endeavor of processing data from multipole sources for input to machine learning models. 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 Fans with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1 as described above by establishing a pipeline for delivering data as disclosed by Fans to accommodate new sources by creating schemas to handle new source data retrievals and/or to integrate new data pipeline component types, and assemble and tear down data pipelines in real-time, while , automatically generating an end-to-end plan to obtain and transmit the right data from the right source(s) to the right target(s). As data pipelines may be constructed dynamically, and on an as-needed basis such that even complex or demanding client requests may be fulfilled without human interaction, and without component-specific human expertise regarding the various data pipeline components (Fans, ¶12). This is simply combining prior art elements according to known methods to yield predictable results, 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 9,
Claim 9 is similar in scope to claim 2; therefore it is rejected under similar rationale.
With regard to Claim 16,
Claim 16 is similar in scope to claim 2; therefore it is rejected under similar rationale.
Claims 3-6, 10-13, and 17-19 are rejected under 35 U.S.C. 103 as being unpatentable over GIETZEN et al . [US 2022/0245801 A1, hereinafter D1] in view of Fan et al. [US 2021/0303585 A1, hereinafter Fan] in view of Dodson et al. [US 2018/0316707 A1, hereinafter Dodson].
With regard to Claim 3,
D1-Fans teach the outlier detection system of claim 2, wherein: a pipeline of the network of pipelines is created for each of the data sources (Fan, ¶60, “the DPMA module 117 may decompose the specification of the information model 195 into mini-specifications for driving data retrieval and data joins, e.g., one mini-specification per data source”, ¶47, “A mini-specification may also be tailored to a set of pipeline instances where data from a more general view is filtered or enriched for their instance-specific scopes”, and for each of the characteristics of the labeled inlier data and labeled outlier data (Fan, Fig. 4, SCENARIO 3, ¶36, “data from various data sources 125 may be filtered and transformed to achieve one or more data sets and/or subsets of data that can be common across a set of data pipelines and data pipeline instances”, ¶32, “data blending module may dissect dataset(s) into appropriate chunks to facilitate reuse; and the data blending module may blend datasets and store the datasets in various intermediate nodes”, ¶47, “A mini-specification may also be tailored to a set of pipeline instances where data from a more general view is filtered or enriched for their instance-specific scopes”, ¶52, “ request 127 may comprise metadata relating to one or more names (e.g., of one or more of the data sources 125, targets 129, types of data sources, and/or types of targets, etc.), one or more regions (e.g., a town, a county, a state, a numbering plan area (NPA), a cell and/or a cluster of cells, a subnet, a defined network region (e.g., a marketing area), etc.), one or more task types (e.g., “market intelligence,” “network load balancing,” “media event support” (e.g., data analysis for large network-impacting events, such as for large concerts, sporting events, etc.), and so forth), a technology (e.g., cellular, Voice over Internet Protocol (VoIP), fiber optic broadband, digital subscriber line (DSL), satellite, etc.), and/or various additional parameters”). The same motivation to combine for claim 2 equally applies for current claim
D1-Fans does not explicitly teach the labeled inlier data and the labeled outlier data of each of the data set sources are segmented based on characteristics of the labeled inlier data and the labeled outlier data
Dodson teach labeled inlier data and the labeled outlier data of each of the data set sources are segmented based on characteristics of the labeled inlier data and the labeled outlier data (¶84, “grouping two or more of the data instances into one or more groups based on correspondence between the multi-dimensional feature vectors and a clustering type”, ¶88, “The commonalities can be based on similarities in feature values. For example, data instances of multiple tenants that have approximately similar CPU, memory, bandwidth, and other related resource values may be grouped together”, ¶91, “The data labeling, i.e. as outlier (or inlier being not an outlier) thus generated, is then automatically mapped onto a set of rules which can be use to identify outliers (or inliers)”).
D1-Fans and Dodson are analogous art to the claimed invention because they are from a similar field of endeavor of detecting outliers in 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-Fans resulting in resolutions as disclosed by Dodson with a reasonable expectation of success.
One of ordinary skill in the art would be motivated to modify D1-Fans as described above allow clustering and outlier extraction in high-order feature analyses to be support anomaly detection (Dodson ¶73). This is simply combining prior art elements according to known methods to yield predictable results, 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 4,
D1-Fans-Dodson teach the outlier detection system of claim 3, wherein the characteristics are based at least in part on distinctions between formats of the labeled inlier data and the labeled outlier data (¶51, “ request interpreter and fulfillment module 111 may be configured to process requests that may be in accordance with various Data Definition Languages (e.g., Structured Query Language (SQL), eXtensible Markup Language (XML) Schema Definition (XSD) Language, Java Script Object Notation (JSON) Schema, etc.) … request interpreter and fulfillment module 111 comprises an abstract symbol manipulator that extracts symbols from data definition languages and handles rules relating the symbols. As such, the data pipeline controller 110 may handle any data for which descriptor symbols have been provided”, ¶26, “ when the data pipeline controller becomes aware of a new data source or other data pipeline components (or a new data source type and/or a new data pipeline component type), the data pipeline controller may attempt to derive a default data schema (and for a new data source, to also profile the data). The data schema may be in terms of the symbols that the data pipeline controller is made aware of”, “data pipeline controller may validate fresh batches of data from a data source against a previously defined data schema, and any differences in the statistical profile of the new batch versus previous batches may be noted”, ¶63, “Data pipeline component C may be configured to obtain summary data from data pipeline component B1 (e.g., again using Kafka, DMaap, nanomsg, or the like), to smooth the data and remove any outliers, and to place the processed data into a JSON format”, Dodson, ¶81, “multi-dimensional attribute sets can include pairs of categorical parameters and values”, ¶38, ¶133, different processing selected based on data format this is distinction between format that drive how data is grouped). The same motivation to combine for claim 3 equally applies for current claim.
With regard to Claim 5,
D1-Fans-Dodson teach the outlier detection system of claim 4, wherein the first task further comprises the transfer learning module using the patterns and relationships that govern the labeled inlier data and the labeled outlier data to generate anomaly scores associated with outputs generated by each of the pipelines of the network of pipelines (D1, ¶131, “The trained classifier creates image description features for the production image based on linear combination of Eigen images. The weights of the Eigen images are learned during the training of the classifier as described above”, ¶132, “each decision tree in the random forest will output seven probability values, i.e., one value per class”, ¶94, “the classification confidence score from the classifier is compared with a threshold to classify the image as a healthy (or good or successful) image or an unhealthy (or bad or failed) image”, Fan, ¶36, “ records and/or alerts regarding network anomaly detections”, “a MLM to process financial data and may be trained to recognize and alert for unusual account activity, and so forth”). The same motivation to combine for claim 3 equally applies for current claim.
With regard to Claim 6,
D1-Fans-Dodson teach the outlier detection system of claim 5, wherein generating the transformed features is further based at least in part on the anomaly scores (D1, ¶82, “Each weight of the ordered set of basis components is used as a feature for training the classifier. For instance, in one implementation, 96 weights for components of labeled images were used to train the classifier”, ¶132, “each decision tree in the random forest will output seven probability values, i.e., one value per class”, ¶94, “the classification confidence score from the classifier is compared with a threshold to classify the image as a healthy (or good or successful) image or an unhealthy (or bad or failed) image”, ¶128, “During training, the output of the random forest is compared with ground truth labels and a prediction error is calculated. During backward propagation, the weights of the 96 components (or the Eigen images) are adjusted so that the prediction error is reduced”, ¶131, “The weights of the Eigen images are learned during the training of the classifier as described above”, ¶128, “The number of components or Eigen images depends on the number of components selected from output of principal component analysis (PCA) using the explained variance measure”, Dodson, “for each (projection, normalized measure) pair associate a weight, unit initialized, which can be adjusted via gradient descent based on user feedback to maximize some measure of accuracy of the ensemble”, ¶141, “he system then receives feedback about the goodness (e.g., accuracy) of outlier detection and learns wij by gradient descent”, ¶142, “the system extracts the dimensions that are most important for labeling a point as an outlier. The system can accomplish this in a greedy fashion by zeroing the affect of each feature in turn on all of the outlier functions and seeing which has the largest resulting decrease in the overall measure of outlier-ness“). The same motivation to combine for claim 3 equally applies for current claim.
With regard to Claim 10,
Claim 10 is similar in scope to claim 3; therefore it is rejected under similar rationale.
With regard to Claim 11,
Claim 11 is similar in scope to claim 4; therefore it is rejected under similar rationale.
With regard to Claim 12,
Claim 12 is similar in scope to claim 5; therefore it is rejected under similar rationale.
With regard to Claim 13,
Claim 13 is similar in scope to claim 6; therefore it is rejected under similar rationale.
With regard to Claim 17,
Claim 17 is similar in scope to claim 3; therefore it is rejected under similar rationale.
With regard to Claim 18,
Claim 18 is similar in scope to claim 4; therefore it is rejected under similar rationale.
With regard to Claim 19,
Claim 19 is similar in scope to claims 5 and 6; therefore it is rejected under similar rationale.
Response to Arguments
Applicant arguments related that the claims recite a particular machine-learning architecture and a particular sequence of computer-implemented operations that address a technical problem arising from outlier detection across diverse data formats. The claimed transfer learning module does not merely observe or evaluate information. Rather, it is trained on diverse labeled inlier/outlier data to learn patterns and relationships, uncover conversion functions for converting data to a one-size-fits-all data format, and cooperate with a classifier trained on transformed features generated from that one-size-fits-all data format. These operations are rooted in computer-implemented machine learning and data-format conversion, and they are not processes that can practically be performed in the human mind.
Examiner respectfully disagrees; The argued limitations has not been classified as part of the abstract idea. A human can scan or read data, learn relations and patterns, use learned knowledge to convert data to different format. The usage of machine learning model merely indicates a field of use or technological environment in which the judicial exception is performed and fails to add an inventive concept to the claims. See MPEP 2106.05(h).
Applicant arguments that the claims improve computerized outlier detection across diverse data formats.
Examiner respectfully disagrees, outlier detection is part of the abstract idea (mental process observation and judging if the data diverge from expected patterns). An improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology (MPEP 2106.05(a)(II)). Further, the data format conversion is a data process that fall within the human ability to convert data from one format to another for analysis. The claims does not disclose any specific way for the format conversion, only that the functions are uncovered and applied. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it" (MPEP 2106.05(f)(1)).
Applicant argue that dependent claims further recite, for example, 10,000 or more data set sources, a network of pipelines, segmentation of labeled inlier and outlier data based on characteristics of the data, creation of pipelines for data sources and characteristics, characteristics based on format distinctions, anomaly scores generated from pipeline outputs, and transformed features based at least in part on those anomaly scores. These limitations further define the particular technical mechanism by which the system handles diverse data formats and builds the representation used for supervised outlier classification.
Examiner respectfully disagrees; the argued limitations are disclosed in a high degree of generality without disclosing any details. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it" (MPEP 2106.05(f)(1)).
Applicant argue that amended claims also recite significantly more than any alleged abstract idea under Step 2B. The ordered combination is not merely the use of generic computer components to collect and analyze data. Instead, the claims require a trained transfer learning module that uncovers conversion functions, converts new and unseen data from its current data format to a one-size-fits-all data format, and cooperates with a classifier trained using transformed features and inlier/outlier labels. This ordered combination provides a particular technological solution for converting diverse-format outlier-detection data into a representation usable for supervised classification, and then applying the trained model to new and unseen data. Accordingly, the claims are analogous to claims directed to improvements in computer-related technology, rather than claims that merely apply an abstract idea on a generic computer.
Examiner respectfully disagrees, outlier detection is part of the abstract idea (mental process observation and judging if the data diverge from expected patterns). An improvement in the abstract idea itself (e.g. a recited fundamental economic concept) is not an improvement in technology (MPEP 2106.05(a)(II)). Further, the data format conversion is a data process that fall within the human ability to convert data from one format to another for analysis. The claims does not disclose any specific way for the format conversion, only that the functions are uncovered and applied. The recitation of claim limitations that attempt to cover any solution to an identified problem with no restriction on how the result is accomplished and no description of the mechanism for accomplishing the result, does not integrate a judicial exception into a practical application or provide significantly more because this type of recitation is equivalent to the words "apply it" (MPEP 2106.05(f)(1)).
Examiner notes that the same response to the arguments is applicable to claims 8 and 15 that recite corresponding method and computer program product implementations of the same eligible technical architecture and their dependent claims.
Applicant’s arguments with respect to claim(s) 1-20 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Conclusion
The prior art made of record and not relied upon is considered pertinent to the applicant’s disclosure.
US Patent Application Publication No. 20220398503 filed by IMAS et al. that disclose a ML model for anomaly detection that operates on feature vector and whose parameters are influenced by other related models via similarity regulation. Th system can detect failure, fraudulent transactions, network security breaches. Therefore it teaches an ML based anomaly detection framework with cross model knowledge transfer See at least ¶¶16-18
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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