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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 6/25/2026 has been entered.
Claim Objections
Claim 1, 5, 8, and 12 are objected to because of the following informalities:
Claim 1 and 8 do not show all the amended limitations from the previous version of the claims as shown below:
when the validating fails, transforming the input data by: determining if a global range transformation, or a global distribution transformation would cause successful validation and applying at least one of the global range transformation, and the global distribution transformation; and applying a machine learning (ML) transformation; , wherein the ML transformation selects from a plurality of ML models based upon the domain;
Claim 5 analogous claim 12 covers the same limitations that were amended in claim 1 such as “comparing numerical range values” and “comparing distribution of the input data ” that are allowed by the domain is similar to claim 5 and 12 recites “validation includes comparing the input data to an expected range and distribution curve for the domain.”
Claim 8 does not properly indent or user proper punctuation to determine when one limitation ends and another one begins.
Appropriate correction is required.
Response to Arguments
Applicant's arguments filed 6/25/2026 have been fully considered but they are not persuasive.
Regarding applicants arguments for 35 U.S.C. 101 on page 6 of remarks, applicant argues “In the prior office action claim 1 was amended to recite "providing a set of input data into a sequestered computing node, wherein the sequestered computing node prevents access by any party to the computing environment, and generates an encrypted artifact containing results of all processing within the sequestered computing node." Support for this amendment may be found in the specification at paragraph 57, which recites "encrypted, containerized AI model which also terminates into an Intel SGX-sequestered enclave. …,
Claims 1 and 8 are believed to recite limitations that are inexorably tied to a specific computational environment, and performing activities that are not merely done on a generic computer system. As such, claims 1 and 8 are believed to now recite eligible subject matter, thus rendering the rejections under 35 USC 101 moot.“ Applicant argues that the process claimed in the independent claims 1 and 8 happen in a sequestered enclave using encryption. However claim 1 does not reflect the limitations shown below as part of claim 1 to occur within any sequestered enclave using encryption.
Claim 1 limitations
identifying a domain for the set of input data by comparing headers and metadata to the kind of data in the set of input data using an Al clustering model;
comparing numerical range values of the input data against range values allowed by the domain, and failing the validation if a range threshold of the input data values is outside the range values allowed by the domain, and wherein the range threshold is configurable up to 10%;
comparing distribution of the input data against a distribution curve allowed by the domain by at least one of least mean square, Procrustes distance or Frechet distance methodologies, and failing the validation if the distribution distance is above a distribution threshold; and
when the validating fails, transforming the input data by:
determining if a global range transformation, or a global distribution transformation would cause successful validation and applying at least one of the global range transformation, and the global distribution transformation; and applying a machine learning (ML) transformation, wherein the ML transformation selects from a plurality of ML models based upon the domain;
iteratively validating and transforming the input data until validation passes; and processing the validated data using at least one algorithm to generate the results.
The specification needs to be purported in order to determine that the process as claimed occurs within the sequestered enclave. Amending the limitations to where the process is occurring within the enclave could help in overcoming the 35 U.S.C. 101 rejection. Similarly claim 8 has a similar issue of where the sequestrated enclave is claimed, yet does not link the process as occurring in the sequestrated enclave. Also formatting claim 8 similarly to how claim 1 is formatted could help in identifying what elements are claimed as part of each part of the process. Further the key management is not part of the claim limitations. The applicants arguments have been considered but are not persuasive. Also the applicant amended the claim limitations and the claim limitations have not been examined.
(Examiner Note: The applicant could overcome the 101 rejection by provided how the sequestered computing node works with the encrypted data by validating and transforming the data as claimed to generate data that can be used in a machine learning model given that the specification gives supports for the amendments.)
Regarding applicants arguments for 35 U.S.C. 103 on page 7-11 of remarks, applicant argues ”Claim 1 and 8 have been amended to recite "identifying a domain for the set of input data
by comparing headers and metadata to the kind of data in the set of input data." There is no
discussion in the cited art of analyzing header and metadata for the input data to classified kinds
of data as now claimed. For at least this reason, claims 1 and 8 are believed allowable over the
cited art.
As none of the cited art does validations against ranges allowed by a domain, distribution
curves as allowed by domain, or data field correlations to identify data outside of allowed
boundaries, or domain identification via clustering models, Applicants believe claims 1 and 8, as
amended are believed to be allowable.” The applicant argues how the amended limitations have overcome the prior art cited. Applicant states that the prior art of record does not teach “There is no discussion in the cited art of analyzing header and metadata for the input data to classified kinds of data as now claimed” but the claim amended limitation “identifying a domain for the set of input data by comparing headers and metadata to the kind of data in the set of input data” does not claim any classification thus the argument is moot and persuasive. Further amended limitations have not been examined and thus the argument regarding the claims is moot and not persuasive.
Claim Rejections - 35 USC § 112 (a)
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claim 1-14 rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Regarding claim 1 and analogous claim 8, the amended limitation found in the limitations “when the validating fails, transforming the input data by: determining if a global range transformation, or a global distribution transformation would cause successful validation and applying at least one of the global range transformation, and the global distribution transformation; and applying a machine learning (ML) transformation; , wherein the ML transformation selects from a plurality of ML models based upon the domain;” the applicant did not underline or identified the added limitations to the section. However to show highlight the amended sections and explain the lack of written description the underlines are provided. The applicant amend the sections above however based on the specification it is unclear what is meant by “a global range transformation” or “a global distribution transformation”. The specification in para. [00145]-[00150] in Fig. 26 And Fig. 27 provide the steps of ingesting the data and validating and when validation fails transforming the data. However the section of the application noted above do not provide any support for “a global range transformation” or “a global distribution transformation” thus the limitation noted above lack written description to define what the limitations are based on based on the specification. The limitation “wherein the ML transformation selects from a plurality of ML models based upon the domain” fails to have written description as the specification in para. 00142 “A ML transformer 2550 may then apply the transform identified by the ML algorithm. To achieve this, a machine learning algorithm would be trained on large sets of healthcare or other domain-specific data that have been transformed with known transformations” and para. 00150 “A machine learning algorithm then consumes the input data (at 2735). Different ML algorithms are utilized, each algorithm trained upon data within the specific domain contemplated. The ML model identifies if a transform exists (at 2745) which would convert the input data into a format/set of values that will pass validation.” does not specify that any model is selected as claimed. All dependent claims inherit the issue.
Claim Rejections - 35 USC § 112 (b)
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 1-14 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.
Claim 1 and analogous claims 8 have the limitation “the results”. There is insufficient antecedent basis for this limitation in the claim. Further it is unclear if the previously mentioned results in line 4 is the same results as the one stated in line 26. Thus claim 1 and 8 are indefinite as to what results it refers to or if they are the same results. All dependent claims inherit the issue.
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 1-14 rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more. The subject matter eligibility test for products and process is describe below for claim 1 in view of dependent claims.
Regarding claim 1:
Step 1: Is the claim to a process machine manufacture or composition of matter?
Yes – Claim 1 recites a method, which is a process that falls under the statutory categories.
Step 2A Prong 1: Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes – The claim recites the following:
“validating the set of input data by: comparing numerical range values of the input data against range values allowed by the domain, and failing the validation if a range threshold of the input data values is outside the range values allowed by the domain, and wherein the range threshold is configurable up to 10%;”- The limitations recites a mental process of validating the set of inputs by comparing ranges of values (see MPEP 2106.04(a)(2)III).
“comparing and distribution of the input data against a distribution curve allowed by responsive to the domain;” – The limitation recites a mathematical process of comparing a distribution of input data against the allowed domain (See MPEP 2106.04(a)(2)I).
“comparing distribution of the input data against a distribution curve allowed by the domain by at least one of least mean square, Procrustes distance or Frechet distance methodologies, and failing the validation if the distribution distance is above a distribution threshold;” The limitation recites a mathematical process of calculating one of least mean square, Procrustes distance or Frechet distance methodologies (See MPEP 2106.04(a)(2)I) and See MPEP 2106.04(a)(2)I). The limitation also recites a mental process of comparing a distribution of the input data based on one of the mathematical methodologies (see MPEP 2106.04(a)(2)III).
“when the validating fails, transforming the input data by at least one of a range transformation, a distribution transformation and a machine learning (ML) transformation;” – The limitation recites a mathematical process of transforming data by distribution transformation (See MPEP 2106.04(a)(2)I).
Step 2 Prong 2: Does the claim recite additional elements that integrate the judicial exception into a particular application? No –
The claim includes the additional element(s):
“A computerized method of processing input data comprising: providing a set of input data into a sequestered computing node”
The additional elements fall under Insignificant Extra-Solution Activity as mere data gathering. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
“wherein the sequestered computing node prevents access by any party to computing environment of the sequestered computing node, and generates an encrypted artifact containing results of all processing within the sequestered computing node;”
The additional elements fall under “apply it” as using a generic computer to implement a sequestered computing environment and generate an encrypted artifact. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
“identifying a domain for the set of input data by comparing headers and metadata to the kind of data in the set of input data ;
The additional elements fall under “apply it” as using a generic computer to implement identifying input based on hears and metadata. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
“iteratively validating and transforming the input data until validation passes; and processing the validated data using at least one algorithm to generate the results.”
The additional elements fall under “apply it” as using a generic computer to iteratively validate and process the data using one algorithm to generate the results. See Mere Instructions to Apply an Exemption (see MPEP 2106.05(f)).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
No - The claim does not include additional elements that are sufficient to amount to a significantly more than the judicial exemption. As an order whole, the claim is directed processing data and performing validations and transformations to validate. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements of processing iteratively validating and transforming fall under using generic computer to apply an exemption. The method does not improve on the function of a computer, transforms an article into another article, nor is it applied by a particular machine, making the claim not patent eligible.
Regarding claim 2:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein the domain is at least one of pathology dependent and financial use case dependent.”
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 3:
Step 2A Prong 2, Step 2B: The additional element(s):
“The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application”
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 4:
Step 2A Prong 2, Step 2B: The additional element(s):
“The method of claim 1, wherein the ML transform is trained on domain specific datasets.
The additional elements fall under Insignificant Extra-Solution Activity. See MPEP 2106.5(g). The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 5:
Step 2A Prong 1:
“The method of claim 1, wherein the validation includes comparing the input data to an expected range and distribution curve for the domain.” – The limitation recites the mental process of validating input data to an expected range and distribution curve of the domain (see MPEP 2106.04(a)(2)III.
Step 2A Prong 2, Step 2B: The additional element(s):
No additional elements. The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 6:
Step 2A Prong 1:
“The method of claim 5, wherein the validation of expected distribution is a curve which fits within two standard deviations of the expected distribution.” - The limitation recites a mental process by validating the expected distribution by a curve within two standard deviations (see MPEP 2106.04(a)(2)III.
Step 2A Prong 2, Step 2B: The additional element(s):
No additional elements. The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Regarding claim 7:
Step 2A Prong 1:
“The method of claim 5, wherein the validation of expected distribution is a curve which fits within a configurable threshold of standard deviations of the expected distribution.” - The limitation recites a mental process by validating the expected distribution by a curve which fits within a configurable threshold of standard deviations of the expected distribution (see MPEP 2106.04(a)(2)III.
Step 2A Prong 2, Step 2B: The additional element(s):
No additional elements. The judicial exemptions do not integrate into a practical application nor provide an improvement. The process does not provide an inventive concept nor provides a practical application.
Claims 8-14 recite a system and are analogous to the method of claims 1-7. Therefore, the rejections of claim 1-7 above applies to claims 8-14.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1, 2, 3, 8, 9, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Kunkel, Roland, et al. "Tensorscone: A secure tensorflow framework using intel sgx." arXiv preprint arXiv:1902.04413 (2019) (“Kunkel”) in view of Callcut et al. (US20200311300A1) (“Callcut”) and further in view of 9. Shi, X., Prins, C., Van Pottelbergh, G. et al. An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge. BMC Med Inform Decis Mak 21, 267 (2021). (“Shi”) and M. S. Munia, M. Nourani and S. Houari, "Biosignal Oversampling Using Wasserstein Generative Adversarial Network," 2020 IEEE International Conference on Healthcare Informatics (ICHI), Oldenburg, Germany, 2020, pp. 1-7, (“Munia”).
Regarding claim 1 and analogous claims 8, Kunkel A computerized method of processing input data comprising: providing a set of input data into a sequestered computing node, wherein the sequestered computing node prevents access by any party to computing environment of the sequestered computing node, and generates an encrypted artifact containing results of all processing within the sequestered computing node (Kunkel page 2,
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2 BACKGROUND AND THREAT MODEL
2.1 Intel SGX and Shielded Execution
Intel Software Guard Extension (SGX) is a set of x86 ISA extensions for Trusted Execution Environment (TEE) [22]. SGX provides an abstraction of secure enclave—a hardware-protected memory region for which the CPU guarantees the confidentiality and integrity of the data and code residing in the enclave memory. The enclave memory is located in the Enclave Page Cache (EPC)—a dedicated memory region protected by an on-chip Memory Encryption Engine (MEE). The MEE encrypts and decrypts cache lines with writes and reads in the EPC, respectively. Intel SGX supports a call-gate mechanism to control entry and exit into the TEE.
Shielded execution based on Intel SGX aims to provide strong confidentiality and integrity guarantees for applications deployed on an untrusted computing infrastructure [15, 17, 39, 52, 60]. Our work builds on the SCONE [15] shielded execution framework. In the SCONE framework, the applications are statically compiled and linked against a modified standard C library (SCONE libc). In this model, application’s address space is confined to the enclave memory, and interaction with the untrusted memory is performed via the system call interface. In particular, SCONE runtime provides an asynchronous system call mechanism [55] in which threads outside the enclave asynchronously execute the system calls. Furthermore, it ensures memory safety [37] for the applications running inside the SGX enclaves [34]. Lastly, SCONE provides an integration to Docker for seamlessly deploying container images [A computerized method of processing input data comprising: providing a set of input data into a sequestered computing node, wherein the sequestered computing node prevents access by any party to computing environment of the sequestered computing node,].
Page 4 3.3 TensorSCONE Controller para 2 and 3,
File system shield. The file system shield protects confidentiality and integrity of data files. Whenever the application would write a file, the shield either encrypts and authenticates, simply authenticates or passes the file as is. The choice depends on user-defined path prefixes, which are part of the configuration of an enclave. The shield splits files into chunks that are then handled separately. Metadata for these chunks is kept inside the enclave, meaning it is protected from manipulation. The secrets used for these operations are different from the secrets used by the SGX implementation. They are instead configuration parameters at the startup time of the enclave.
Network shield. TensorFlow applications do not inherently include end-to-end encryption for network traffic. Users who want to add security must apply other means to secure the traffic, such as a proxy for the Transport Layer Security (TLS) protocol. According to the threat model however, data may not leave the enclave unprotected, because the system software is not trusted. Network communication must therefore always be end-to-end protected. Our network shield wraps sockets, and all data passed to a socket will be processed by the network shield instead of the system software. The shield then transparently wraps the communication channel in a TLS connection on behalf of the user application. The keys for TLS are saved in files and protected by the file system shield [, and generates an encrypted artifact containing results of all processing within the sequestered computing node;].));
Kunkel does not explicitly teach identifying a domain for the set of input data by comparing headers and metadata to the kind of data in the set of input data
validating the set of input data by:
comparing numerical range values of the input data against range values allowed by the domain, and failing the validation if a range threshold of the input data values is outside the range values allowed by the domain, and wherein the range threshold is configurable up to 10%;
comparing distribution of the input data against a distribution curve allowed by the domain by at least one of least mean square, Procrustes distance or Frechet distance methodologies, and failing the validation if the distribution distance is above a distribution threshold; and
determining if a global range transformation, or a global distribution transformation would cause successful validation and applying at least one of the global range transformation, and the global distribution transformation; and applying a machine learning (ML) transformation, wherein the ML transformation selects from a plurality of ML models based upon the domain;
iteratively validating and transforming the input data until validation passes; and processing the validated data using at least one algorithm to generate the results.
However Callcut teaches identifying a domain for the set of input data by comparing headers and metadata to the kind of data in the set of input data (Callcut para 0039, At block 430 , the data assets may be indexed . Data indexing allows queries to efficiently retrieve data from a database . The indexes may be related to specific tables and may be comprised of one or more keys or values to be looked up in the index ( e.g. , the keys may be based on a data table’s columns or rows )[identifying a domain for the set of input data by comparing headers]. By comparing query terms to the keys within the index it is possible to find one or more database records with the same value in an efficient manner. In some instances , basic information such as metadata and statistical attributes of data fields are computed as one or more keys and stored in the index . The details of what basic information is collected and how it is exposed for searching within the platform depends on the data type and anticipated use cases . In general , this basic information is intended to aid in queries by identifying what data might be available on the platform for projects and the attributes of the data . The basic information can also be used to inform the platform or end user of data transformation and harmonization methods that may be needed to work with the data assets . Further , the basic information can be used for anomaly detection and general data quality assurance purposes [and metadata to the kind of data in the set of input data]);
when the validating fails, transforming the input data by:
determining if a global range transformation, or a global distribution transformation would cause successful validation and applying at least one of the global range transformation, and the global distribution transformation;
and applying a machine learning (ML) transformation, wherein the ML transformation selects from a plurality of ML models based upon the domain (Callcut Para 0063, At block 305 , a third - party algorithm developer ( a first entity ) provides one or more algorithms or models to be optimized and / or validated in a new project . The one or more algorithms or models may be developed by the algorithm developer using their own development environment , tools , and seed data sets ( e.g. , training / testing data sets ) . In some instances , the models include one or more prediction models . The prediction models can comprise any algorithm, for example , a ML model including but not limited to a convolutional neural network ( “ CNN ” ) , e.g. an inception neural network , a residual neural network ( “ Resnet ” ) , or a recurrent neural network , e.g. , long short - term memory ( “ LSTM ” ) models or gated recurrent units ( “ GRUs ” ) models . A pre diction model can also be any other suitable ML model trained to predict something that cannot be directly measured or which will occur in the future , or to make an inference from data ( a conclusion ( e.g. , a prediction of a model or a result of an algorithm) [wherein the ML transformation selects from a plurality of ML models based upon the domain;].
(Examiner Note: The model changes depending on the requirements of the developer.)
para 0071 line 1-14, In some embodiments , the validation constraints include but are not limited to one or more of following : validation data selection criteria , validation termination criteria , and a validation report requirements . The validation data selection criteria may include selection criteria for the validation data set that can include any factors required to select an appropriate subset of the data for the application being developed . For example , in healthcare applications , cohort selection includes , but is not limited to clinical cohort criteria , demographic criteria , and data set class balance . In healthcare algorithm development , cohort studies are a type, of medical research used to investigate the causes of disease and to establish links between risk factors and health outcomes in groups of people , known as a cohort .
Para 0071 line 31-46, The data set class balance defines if and how the data is to be presented for the study . For example, in many instances a data set is imbalanced ( e.g. , many more patients with a negative analytical test result as compared to patients with a positive analytical test result ) A simple way to fix imbalance dataset balance them , either by oversampling instances of the minority class or under sampling instances of the majority class. Thus, constraints for data set class balance may define ( i ) whether the data set should be balanced at all, ( ii ) how balanced should the data set be, e.g. , is 40:60 acceptable compared to 80:20 or does it have to be 50:50 , and ( iii ) how to perform the balance , e.g. , oversample the minority class [and applying at least one of the global range transformation, and the global distribution transformation;].
Para 0077, At block 340 , a determination is made as to whether the annotated data assets are harmonized in accordance with an algorithm protocol ( e.g. , the training and / or validation constraints defined in block 310 ) . Data harmonization is the process of bringing together data sets of varying file formats , naming conventions , and columns , and trans forming it into one cohesive data set . In some instances , the determination may be made by comparing the training and/or validation constraints to the harmonization of the annotated data assets . When the annotated data assets are harmonized in accordance with an algorithm protocol , then further harmonization is not required and the process continues at block 360. When the annotated data assets are not harmonized in accordance with an algorithm protocol , then harmonization is required and the process continues at block 345. At block 345 , the annotated data assets are harmonization as described in detail with respect to FIG . 8. In some instances , the algorithm protocol requires the data assets to be transformed into a specific format or modified in a specific manner for computation . Harmonization of the data may be performed to transform the data assets to the specific format or modified in a specific manner.
Para 0081 line 20-28, The validation may include running one or more instances of validation with the data assets in an attempt to validate the models based on gold standard labels as described in detail with respect to FIG . 10. At block 365 , or more reports are generated and delivered to the algorithm developer based on the results of block 360. In some instances , the reports are generated in accordance with the training / testing report requirements and / or validation report requirements defined in block 310 [determining if a global range transformation, or a global distribution transformation would cause successful validation]);
iteratively validating and transforming the input data until validation passes; and processing the validated data using at least one algorithm to generate the results (Callcut
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[iteratively],
Para 0077, At block 340 , a determination is made as to whether the annotated data assets are harmonized in accordance with an algorithm protocol ( e.g. , the training and / or validation constraints defined in block 310) [validating]. Data harmonization is the process of bringing together data sets of varying file formats , naming conventions , and columns , and transforming it into one cohesive data set . In some instances , the determination may be made by comparing the training and / or validation constraints to the harmonization of the annotated data assets . When the annotated data assets are harmonized in accordance with an algorithm protocol , then further harmonization is not required and the process continues at block 360. When the annotated data assets are not harmonized in accordance with an algorithm protocol , then harmonization is required and the process continues at block 345. At block 345 , the annotated data assets are harmonization as described in detail with respect to FIG . 8. In some instances , the algorithm protocol requires the data assets to be transformed into a specific format or modified in a specific manner for computation. Harmonization of the data may be performed to transform the data assets [and transforming the input data until validation passes])
Para 0081, At block 360, the optimization and/or validation of the models is performed using the data assets . In some instances, the optimization comprises initializing the algorithms with predefined values or random values for weights and biases and attempting to predict an output with those values . In certain instances, the models are pre-trained by the algorithm developer, and thus the algorithms may already be initialized with weights and biases. In other instances , predefined values or random values for weights and biases may be defined by the algorithm developer in block 310 and populated into the algorithms at block 360. Thereafter , data assets and hyperparameters may be input into the algorithms , inferences or predictions may be computed , and testing or comparisons may be made to determine how accurately the trained models predicted the output [processing the validated data]. The optimization may include running one or more instances of training and/or testing with the data assets in an attempt to optimize performance of the models ( e.g. , optimize the weights and biases ) as described in detail with respect to FIG . 9 [using at least one algorithm to generate the results.]).
Kunkel and Callcut are considered to be analogous to the claim invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kunkel to incorporate the teachings of Callcut to validate and transform until validation passes. Doing so to validate the machine learning model is able to achieved sufficient validation (Callcut para 0111, At block 1015 , performance or accuracy of the model is computed based on gold standard labels ( i.e. , ground truths ) and determination is made as to whether the model has been validated . For example , an algorithm designed to detect breast cancer lesions in mammograms can be validated on a set of mammograms that have been labeled by medical experts as either containing or not containing a lesion . The performance of the algorithm relative to this expertly labeled set of mammograms is the validation report . In some instances , feature selection , classification , and parameterization of the model are visualized ( e.g. , using area under curve analysis ) and ranked , according to validation criteria defined ( i.e. , criteria upon which validation of the model is determined ) by algorithm developer in block 310 of FIG . 3. In some instances , determining whether the model has been validated includes determining whether the model has satisfied validation termination criteria ( i.e. , criteria that defines whether a model has achieved sufficient validation).
Shi teaches comparing numerical range values of the input data against range values allowed by the domain, and failing the validation if a range threshold of the input data values is outside the range values allowed by the domain, and wherein the range threshold is configurable up to 10% (Shi
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page 5 Outlier detection para 2-3, Van den Broeck et al. [19] suggested an outlier detection method based on variable-specific information. They specified the true normal, true extreme, erroneous and idiopathic values for each variable and discussed the outliers case by case. Their method could not easily be applied in a big data and automated framework, thus, it was simplified into an outlier detection method based on the normal range and extreme range of variables.
If the values were out of the normal range, a transformation in the order of magnitude was first conducted, trying to make the values as close to the normal range as possible, e.g. 9 for blood pressure could be converted into 90 (Type 1 error in Section Data Descriptions and Issues). If the values were still out of the extreme range after the transformation, they were regarded as outliers and replaced with NA (Type 2 error in Section Data Descriptions and Issues), e.g. 3000 for blood pressure was replaced with NA because it is an unreasonable value even when it was converted as 30 or 300 [and failing the validation if a range threshold of the input data values is outside the range values allowed by the domain]. Figure 2 shows an example of the outlier detection by using the data range of clinical variables [comparing numerical range values of the input data against range values allowed by the domain]
(Examiner Note: The outliers are configure to be detected based the normal values as explained in Figure 2. One in the art could adjust the range as required to determine an outliers));
Kunkel and Shi are considered to be analogous to the claim invention because they are in the same field of processing data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kunkel in view of Callcut to incorporate the teachings of Callcut to validate data against a range. Doing so to make sure all values are within the normal range after cleaning and automate cleaning data (Shi page 1 Abstract, Results: All variables had more than 50% values within the normal range after cleaning, of which 43 variables had a percentage higher than 70%.
Conclusions: We propose a general method for clinical variables, which achieves high automation and is capable to deal with large-scale data. This method largely improved the efficiency to clean the data and removed the technical barriers for non-technical people.)
Munia teaches comparing distribution of the input data against a distribution curve allowed by the domain by at least one of least mean square, Procrustes distance or Frechet distance methodologies, and failing the validation if the distribution distance is above a distribution threshold (
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page 3 3) Synthetic Image Generation line 13-20, the FID score of the real and synthetic images is calculated. We have empirically chosen an FID threshold, which we use as an accept/reject criterion []. Comparing the calculated FID score of the real and synthetic images with the FID threshold, we decide if the synthetic images should be accepted or discarded. If they are accepted, the remaining synthetic images are augmented to the training dataset for later application. We continue this experimentation until no more improvement is seen in FID.
Page 4, B. Evaluations Metrics
1) 1) Frechet Inception Distance (FID): The FID score evaluates the quality of the synthetic images generated by GANs [23]. It examines the similarity between the synthetic images and the real images by comparing statistics of a group of synthetic images with the statistics of a group of real images. The FID score uses the Inception network [24] to calculate these statistics for each collection of images. An intermediate layer of the inception network is used to capture features of an input image (real or synthetic). The mean and covariance of the features of the real and synthetic images are then determined and the features are portrayed as two multivariate Gaussian distributions (one for real and one for synthetic images). Afterwards, the distance between these two distributions is measured using the Wasserstein-2 distance, which is also known as Frechet distance. The smaller the distance, the more similar the two distributions, and as a result, the better the quality of the synthetic images [comparing distribution of the input data against a distribution curve allowed by the domain by at least one of].
Page 5 IV. Results,
The FID score between the real and synthetic signal images for the WGAN models are reported in Table III. We have calculated the FID score at 100, 300, 600 and 1000 epochs for the WGAN model. From the FID values, we can say that the distance between the original and synthetic data distribution is still large, but as the training progresses, the FID score reduces and the real data distribution and synthetic data distribution get closer [Frechet distance methodologies, and failing the validation if the distribution distance is above a distribution threshold].);
Kunkel and Munia are considered to be analogous to the claim invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kunkel to incorporate the teachings of Munia to validate distribution using Frechet distance methodologies. Doing so to evaluate the efficiency of the of generating synthetic data by determine image quality using Frechet distance (Munia Abstract We first trained the WGAN with fixed-dimensional images of the signal and generated synthetic data with similar characteristics. Two evaluation methods were then used for evaluating the efficiency of the proposed technique in generating synthetic ECG data. We used Frechet Inception Distance score for measuring synthetic image quality. We then performed a binary classification of normal and abnormal (Anterior Myocardial Infarction) ECG using Support Vector Machine to verify the performance of the proposed method as an oversampling technique.)
Regarding claim 2 and analogous claim 9, Kunkel in view of Callcut, Shi and Munia teach the method of claim 1 and analogous 8.
Kunkel, Callcut, Shi and Munia are combined with the same rationale as set forth above with respect to claim 1 and analogous 8.
Callcut further teaches wherein the domain is at least one of pathology dependent and financial use case dependent (Callcut Para. 0071, In some embodiments , the validation constraints include but are not limited to one or more of following : validation data selection criteria , validation termination criteria , and a validation report requirements . The validation data selection criteria may include selection criteria for the validation data set that can include any factors required to select an appropriate subset of the data for the application being developed . For example , in healthcare applications , cohort selection includes , but is not limited to clinical cohort criteria , demographic criteria , and data set class balance . In healthcare algorithm development , cohort studies are a type of medical research used to investigate the causes of disease and to establish links between risk factors and health outcomes in groups of people , known as a cohort . Retrospective cohort studies look at data that already exists and try to identify risk factors for particular conditions . In a prospective cohort study , researchers raise a question and form a hypothesis about what might cause a disease . Then the researchers observe a cohort over a period of time , to prove or disprove the hypothesis . Thus , the clinical cohort criteria may define a group of people that the data is to be obtained from for the study , the type of study ( e.g. , retrospective or prospective ) , risk factors that the group may have exposure to over a period of time , question / hypothesis to be solved and associated disease or condition , and / or other parameters that define criteria for the cohort study [wherein the domain is at least one of pathology dependent]).
Regarding claim 3 and analogous claim 10, Kunkel in view of Callcut, Shi and Munia teach the method of claim 1 and analogous 8.
Kunkel, Callcut, Shi and Munia are combined with the same rationale as set forth above with respect to claim 1 and analogous 8.
Callcut further teaches further comprising cleaning the input data (Callcut Para 0076, At block 335 , once the data assets are prepare for annotation , the data assets are annotated as described in detail with respect to FIG . 7. Each algorithm of the models may require data to be labeled in a specific way . For example , a breast cancer detection / screening system may require specific lesions to be sized and identified . Another example would be gastro - intestinal cancer digital pathology , in which each image may need to be segmented and labeled by the type of tissue present (normal ,necrotic ,malignant, etc. ) . In some instances involving text or clinical data , annotation may include applying a labeling ontology to selected subsets of text and structured data . The annotation is performed ly to the data host in the secure capsule computing service . A key principle to the transformation and annotation processes is that the platform facilitates a variety of processes to apply and refine data cleaning and transformation algorithms [further comprising cleaning the input data], while preserving the privacy of the data assets, all without requiring data to be moved outside of then technical purview of the data host).
Claim(s) 4 and 11 are rejected under 35 U.S.C. 103 as being unpatentable over Kunkel in view of Callcut and Shi and Munia and further in view of K. Stacke, G. Eilertsen, J. Unger and C. Lundström, "Measuring Domain Shift for Deep Learning in Histopathology," in IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 2, pp. 325-336, Feb. 2021.
Regarding claim 4 and analogous claim 11, Kunkel in view of Callcut, Shi and Munia teach the method of claim 1 and analogous 8.
Kunkel, Callcut, Shi and Munia are combined with the same rationale as set forth above with respect to claim 1 and analogous 8.
Stacke further teaches wherein the ML transform is trained on domain specific datasets (Stacke page 330, Fig. 6
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Page 331 C. Data Transformations 1) Color and Intensity, line 13-18, Tumor classification on H&E stained images are less dependent on color information and more on pattern, making it possible to heavily augment the colors, while retaining high model accuracy. The augmentations were done online during training [wherein the ML transform is trained]).
Kunkel and Callcut and Stacke are considered to be analogous to the claim invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kunkel in view of Callcut to incorporate the teachings of Stacke to use a machine learning algorithm to transform data. Doing so to validate the machine learning model based on the data used during training to affirm the model performance(Stacke page 334 VII. Discussion Para 4, Before a CNN can be used in a clinical setting, it needs to be properly validated. This validation should include evaluation on data with more variance than included in the training set, to affirm model performance. However, it is not possible to cover all data variations that may occur. Using the representation shift it is possible to do both an initial control on a large amount of unlabelled data, and continuous monitoring of a deployed model. By monitoring a model's data representation, differences in data statistics and model performance degradation can be detected.)
Claim(s) 5 and 12 are rejected under 35 U.S.C. 103 as being unpatentable over Kunkel in view of Callcut and Shi and Munia and further in view of of Li (US20190236114A1) (“Li”).
Regarding claim 5 and analogous claim 12, Kunkel in view of Callcut, Shi, and Munia teach the method of claim 1 and analogous 8.
Kunkel, Callcut, Shi and Munia are combined with the same rationale as set forth above with respect to claim 1 and analogous 8.
Kunkel does not explicitly teach wherein the validation includes comparing the input data to an expected range and distribution curve for the domain.
However Li teaches wherein the validation includes comparing the input data to an expected range and distribution curve for the domain (Li Para. 45, FIG. 5 is a schematic diagram of a curve corresponding to t distribution shown in FIG. 4. A shape of the t distribution curve is related to a value of the degree of freedom v. If the value of the degree of freedom v is smaller, the t distribution curve is flatter, a middle part of the curve is lower, and the two tails of the curve are higher. If the value of the degree of freedom v is larger, the t distribution curve is closer to a normal distribution curve. When the value of the degree of freedom v is infinite, the t distribution curve is a standard normal distribution curve. FIG. 5 is a schematic diagram of a curve corresponding to t distribution when a degree of freedom v is equal to 34 in FIG. 4. When t=2.032, as shown in FIG. 5, the corresponding P=0.05 is a sum of shadow areas on two sides of the t distribution curve. To be specific, a total area under the t distribution curve is 1, and a total area of the shadow areas is 0.05. When t>2.032, the corresponding shadow areas on two sides of the t distribution curve is smaller, in other words, the probability P is smaller.
Para 0060, In an implementation, the data in the data group that is to be validated and the data in the comparison data group are severely positively skewed. In this case, a reciprocal transformation can be performed on the data in the data group that is to be validated and the data in the comparison data group. In another implementation, population distribution of the data in the data group that is to be validated and population distribution of the data in the comparison data group are binomial distribution whose population rate is relatively small or whose population rate is relatively large [distribution curve for the ]. In this case, an arcsine square root transformation can be performed on the data in the data group that is to be validated and the data in the comparison data group [the validation includes comparing the input data].
Para 0077, At 902, by a data processing platform, a to-be-validated data group including to-be-validated data corresponding to a predetermined feature is obtained In some implementations, the predetermined feature defines a type of numerical values within a predetermined range. From 902, method 900 proceeds to 904 [expected range]).
Kunkel and Li are considered to be analogous to the claim invention because they are in the same field of processing data. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kunkel to incorporate the teachings of Li to validate data based on a distribution and range. Doing so to detect abnormal data and continue to use the data (Li Para 0084, Implementation of the present application provide methods and apparatuses for improving abnormal data detection in data processing. In some implementations, a processing platform (e.g., an payment processing server) obtains data that is to be validated and that corresponds to a predetermined feature from a data providing platform as a data group that is to be validated ( e.g., a data group that corresponds to user transaction amounts). In addition, the processing platform can further obtains historical data of the data that is to be validated as a comparison data group. The historical data may also corresponds to the same predetermined feature, and the comparison data group can be provided by the data providing platform in advance. Then, the processing platform performs a two-group significance test on the data group that is to be validated and the comparison data group, and determines whether there is abnormal data based on a test result. If there is no abnormal data, the processing platform can continue to process the data or send the data to a next service step. If the processing platform determines that there is abnormal data, the processing platform can start alerting, instruct related persons to analyze the cause of the data exception, and trigger related solutions.).
Claim(s) 6 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over unpatentable over Kunkel in view of Callcut and Shi and Munia and further in view of Li and T. -C. Hsu and C. Lin, "Generative Adversarial Networks for Robust Breast Cancer Prognosis Prediction with Limited Data Size," 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Montreal, QC, Canada, 2020, pp. 5669-5672, (“Hsu”).
Regarding claim 6 and analogous claim 13, Kunkel in view of Callcut, Shi, Munia and Li teach the method of claim 5 and analogous 12.
Kunkel, Callcut, Shi and Munia are combined with the same rationale as set forth above with respect to claim 1 and analogous 8.
Kunkel and Li are combined with the same rationale as set forth above with respect to claim 1 and analogous 8.
Kunkel and Li are combined with the same rationale as set forth above with respect to claim 5 and analogous 12.
Kunkel does not explicitly teach wherein the validation of expected distribution is a curve which fits within two standard deviations of the expected distribution.
However Hsu teaches wherein the validation of expected distribution is a curve which fits within two standard deviations of the expected distribution (Hsu Page 5672,
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[a curve which fits within two standard deviations of the expected distribution.]
Page 5672 C. Results Para 3, To further analyze how well each model performed in terms of patient stratification, we also plotted KM-plots, as shown in Fig. 2. The curves shown on the figure were the mean of the curves of 30 realizations, and the light/dark shades were the curves one/two standard deviations away from the mean curve, respectively. It showed that both DADA and LR provided insignificant stratification since the two curves had a large potential of overlapping (Fig. 2a and Fig. 2b). On the other hand, wDADA and Bimodal both achieved remarkable stratification (Fig. 2c and Fig. 2d). To quantify the separation between the curves, we also conducted the log-rank test. We counted the number of realizations with p-values greater than 0.005, which represents insignificant stratification (the bad in subfigure captions). We observed that both DADA and LR had approximately 50% of the chance of producing poor stratification. At the same time, wDADA and Bimodal achieved a much lower probability as well as having smaller standard deviations, which indicates the robustness of both models [wherein the validation of expected distribution]).
Kunkel and HSU are considered to be analogous to the claim invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kunkel to incorporate the teachings of Hsu to training a model that produces data within 2 standard deviations of the expected data. Doing so to generate accurate results and have a flexible model that can be incorporated into ensemble learning and simi-supervised learning (Hsu Page 5669 Abstract line 11-23, We found that wDADA achieved 0.6726_0.0278, 0.7538_0.0328, and 0.6507_0.0248 in terms of accuracy, AUC, and concordance index in predicting 5-year DSS, respectively, which is comparable to our previously proposed Bimodal model (accuracy: 0.6889_0.0159; AUC: 0.7546_0.0183; concordance index: 0.6542_0.0120), which needs careful calibration and extensive search on pre-trained network architectures. The flexibility of the proposed wDADA allows us to incorporate it with ensemble learning and semi-supervised learning to further improve performance. Our results indicate that it is possible to utilize generative adversarial networks to train deep models in medical applications, wherein only limited data are available.).
Claim(s) 7 and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Kunkel in view of Callcut and Shi and Munia and further in view of Li and Gurumurthy, Swaminathan, Ravi Kiran Sarvadevabhatla, and R. Venkatesh Babu. "Deligan: Generative adversarial networks for diverse and limited data." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017 (“Swaminathan”).
Regarding claim 7 and analogous claim 14, Kunkel in view of Callcut, Shi, Munia and Li teach the method of claim 5 and analogous 12.
Kunkel, Callcut, Shi and Munia are combined with the same rationale as set forth above with respect to claim 1 and analogous 8.
Kunkel and Li are combined with the same rationale as set forth above with respect to claim 1 and analogous 8.
Kunkel does not explicitly teach wherein the validation of expected distribution is a curve which fits within a configurable threshold of standard deviations of the expected distribution.
However Swaminathan teaches wherein the validation of expected distribution is a curve which fits within a configurable threshold of standard deviations of the expected distribution (Gurumurthy Page 167 3. Generative Adversarial Networks (GANs) Para 2-3, The generator G is modelled so that it transforms a random vector z into an image
x
G
, i.e.
x
G
= G(z). z typically arises from an easy-to-sample distribution, for e.g. z
~
U(−1, 1) where U denotes a uniform distribution. G is trained to generate images which are indistinguishable from a sampling of the true distribution. In other words, while training G, we try to maximise
p
d
a
t
a
(
x
G
), the probability that the generated samples belong to the data distribution.
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The above equations make explicit the fact that GANs assume a fixed, easy to sample, prior distribution pz(z) and then maximize pdata(xG|z) by training the generator network to produce samples from the data distribution. [wherein the validation of expected distribution is a curve].
Page 169 4.1. Learning μ and σ para 1-2,
For each Gaussian component, we first need to initialize its parameters. For
μ
i
,
≤
i
≤
N
, we sample from a simple prior – in our case, a uniform distribution U(−1, 1). For σi, we assign a small, fixed non-zero initial value (0.2 in our case). Normally, the number of samples we generate from each Gaussian relative to the other Gaussians during training gives us a measure of the ‘weight’ _ for that component. However, _ is not a trainable parameter in our model since we cannot obtain gradients for πis. Therefore, as mentioned before, we consider all components to be equally important.
To generate data, we randomly choose one of the N Gaussian components and sample a latent vector z from the chosen Gaussian (Equation 8). z is passed to G to obtain the output data (image). The generated sample z can now be used to train parameters of D or G using the standard GAN training procedure (Equation 5). In addition, μ and σ are also trained simultaneously along with G’s parameters, using gradients arising from G’s loss function
Page 169 5.1. Modified Inception Score Passing a generated image x = G(z) through a trained classifier with an “inception” architecture [22] results in a conditional label distribution p(y|x). If x is realistic enough, it should result in a “peaky” label distribution i.e. p(y|x) should have low entropy. [is a curve which fits within a configurable threshold of standard deviations of the expected distribution]).
Kunkel and Gurumurthy are considered to be analogous to the claim invention because they are in the same field of machine learning. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filling date of the claimed invention to have modified Kunkel to incorporate the teachings of Gurumurthy to training a model based on the sample distribution by decreasing the sample distribution. Doing so to stabilize the model and produce diverse sample even in low data scenarios (Gurumurthy Page 173 7. Conclusions and Future Work line 1-7, In this work, we have shown that reparameterizing the latent space in GANs as a mixture model can lead to a powerful generative model. Via experiments across a diverse set of modalities (digits, hand-drawn object sketches and color photos of objects), we have observed that this seemingly simple modification helps stabilize the model and produce diverse samples even in low data scenarios.).
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Arvamudan et al. (US11545242B2) – teaches a method for sequestering computing resources into an enclave that prevent external access.
Conclusion
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/ALFREDO CAMPOS/Examiner, Art Unit 2129
/IMAD KASSIM/Primary Examiner, Art Unit 2129
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