DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. Acknowledgement is made of applicant’s claim for foreign priority to 20 April 2023 under 35 U.S.C. 119(a)-(d).
Response to Preliminary Amendment
Claims 1-22 were previously pending in this application. The preliminary amendment filed 20 October 2025 has been entered and the following has occurred: no Claims have been amended. Claims 1-22 have been cancelled. Claims 23-42 have been added.
Claims 23-42 remain pending in the application
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 20 October 2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner in this Office Action.
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.
Claims 26, 33, & 40 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, regards as the invention.
Claims 26, 33, & 40, recite the limitation “… said obtaining the standard deviation and/or variance of the cleaned testing data comprises…” in the claims. There is insufficient antecedent basis for this limitation in the claim, because these claims or the claims from which claims 26, 33, & 40 are dependent (claims 23, 29, 37) do not previously establish a step of “obtaining the standard deviation and/or variance…”. Therefore, claims 26, 33, & 40 will be interpreted to recite “…obtaining the standard deviation and/or variance of the cleaned testing data comprises…” for examination purposes.
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 23-42 rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
The claims recite subject matter within a statutory category as a process (claims 23-28), machine (claims 37-42), and manufacture (claims 29-36) (Subject Matter Eligibility (SME) Test Step 1: Yes) which recite steps of:
obtaining testing data of a patient's sample;
cleaning the testing data by removing data items unrelated to the health analysis;
obtaining statistics of the cleaned testing data; and
obtaining analysis results of one or more genes and/or species of the patient's sample using one or more artificial intelligence (AI) methods based on the obtained statistics of the cleaned testing data and statistics of healthy data.
These steps of obtaining testing data, cleaning the testing data by removing data items, obtaining statistics of the cleaned testing data, and obtaining analysis results, as drafted, under the broadest reasonable interpretation, includes performance of the limitation in the mind but for recitation of generic computer components. That is, other than reciting steps as performed by the generic computer components, nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the obtaining testing data language, obtaining test data in the context of this claim encompasses a mental process of a person gathering data manually, such as parsing one or more records, resources, etc. or through use of a tool for collecting data. Similarly, the limitation of cleaning the testing data by removing data items, as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, such as removing outliers or data that is unrelated to healthcare. For example, but for the obtaining statistics of the cleaned testing data language, obtaining statistics in the context of this claim encompasses a mental process of the user either performing math or other mental processes for determining statistics of the manually filtered or cleaned dataset. Similarly, the limitation of obtaining analysis results drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, such as obtaining the results of the person analyzing the manually filtered or cleaned dataset. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
These steps of cleaning testing data, obtaining statistics of the cleaned testing data, and obtaining analysis results based on obtained statistics of the cleaned testing data and statistics of healthy data, as drafted, under the broadest reasonable interpretation, includes mathematical concepts. While no explicit formulae are recited, these steps recite aspects of merely receiving data, performing various mathematical steps, relationships, or concepts of filtering claim sets, performing statistical analysis on the cleaned testing data, and/or receiving results of the statistical analysis relates substantially to mathematical concepts. Clearly these steps, as currently drafted, are designed the monopolize the abstract idea of obtaining data, cleaning said data, obtaining statistics of said cleaned data, and obtaining analysis results. Accordingly, the claim recites an abstract idea.
Dependent claims recite additional subject matter which further narrows or defines the abstract idea embodied in the claims (such as claim 24-42, reciting particular aspects of how obtaining data, c leaning testing data, obtaining statistics, and/or obtaining analysis results may be performed in the mind but for recitation of generic computer components, such as by applying generic learning models and/or algorithms) (SME Test Step 2A, Prong 1: Yes).
This judicial exception is not integrated into a practical application. In particular, the additional elements do not integrate the abstract idea into a practical application, other than the abstract idea per se, because the additional elements amount to no more than limitations which:
amount to mere instructions to apply an exception (such as recitation of one or more artificial intelligence methods amounts to invoking computers as a tool to perform the abstract idea, see Applicant’s Specification p. 11, ll. 1-4 for one or more AI methods, see MPEP 2106.05(f));
add insignificant extra-solution activity to the abstract idea (such as recitation of obtaining testing data of a patient’s sample, obtaining statistics of the cleaned testing data, obtaining analysis results, amounts to mere data gathering, recitation of cleaning the testing data by removing data items unrelated to the health analysis amounts to selecting a particular data source or type of data to be manipulated, recitation of cleaning the testing data by removing data items unrelated to the health analysis, applying an AI model to obtain analysis results or perform statistics amounts to insignificant application, see MPEP 2106.05(g));
generally link the abstract idea to a particular technological environment or field of use (such as recitation of a health-analysis method and/or one or more genes/patient’s samples, in particular, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 24-42, which recite limitations relating to a machine learning model (e.g. a random forest model, a regression model, a clustering model, a maximum relevance minimum redundancy model, a deep learning model, a convolutional neural network, a reinforcement learning model, a genetic model, etc.), one or more AI models, one or more non-transitory computer-readable storage devices, a processing structure/one or more processors, additional limitations which amount to invoking computers as a tool to perform the abstract idea and/or merely reciting “using” said algorithms, i.e. instances of “apply it”, see Applicant’s Specification p. 11, ll. 1-11 for a machine learning model, see Spec p. 11, ll. 1-4 for an AI model; see Spec p. 5, ll. 32, p. 6, ll. 7 for a non-transitory computer-readable storage device; see Spec p. 5, ll. 11-28 for a processing structure/one or more processors see MPEP 2106.05(f); claims 26-27, 29, 32-33, 35, 37, 40, which recite limitations relating to obtaining one or more data, evaluations, results, etc., additional limitations which add insignificant extra-solution activity to the abstract idea which amounts to mere data gathering; claims 25, 28, 31, 34, 39, & 42, which recite limitations relating to cleaning testing data using one or more received gene panels, using the obtained analysis results for determining one or more diseases, additional limitations which add insignificant extra-solution activity to the abstract idea by selecting a particular data source or type of data to be manipulated; claims 25-26, 31-32, & 39-40, which recite limitations relating to cleaning testing data, obtaining standard deviations and/or variance of various data, additional limitations which amount to insignificant application; claims 24-42, additional limitations which generally link the abstract idea to a particular technological environment or field of use, such as relating to medical/patient data, disease identification, etc.). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation and do not impose a meaningful limit to integrate the abstract idea into a practical application (SME Test Step 2A, Prong 2: No).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to discussion of integration of the abstract idea into a practical application, the additional elements amount to no more than mere instructions to apply an exception, add insignificant extra-solution activity to the abstract idea, and generally link the abstract idea to a particular technological environment or field of use. Additionally, each of the additional limitations, other than the abstract idea per se, amount to no more than limitations which:
amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields (such as obtaining testing data of a patient’s sample, obtaining statistics of the cleaned testing data, obtaining analysis results, e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); performing cleaning of testing data, performing analysis of said statistics, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); obtaining testing data of patient’s samples which could include accessing a patient EHR, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); storing obtained testing data, obtained statistics of the cleaned testing data, and/or obtained analysis results, storing one or more AI Models, storing computerized instructions for performance of the methods recited, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); obtaining testing data of a patient’s sample which could include extraction or accessing a patient health record or EHR, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)).
Dependent claims recite additional subject matter which, as discussed above with respect to integration of the abstract idea into a practical application, amount to invoking computers as a tool to perform the abstract idea. Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims (such as claims 24-42, additional limitations which amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, claims 26-27, 29, 32-33, 35, 37, 40, which recite limitations relating to obtaining one or more data, evaluations, results, etc., e.g., receiving or transmitting data over a network, Symantec, MPEP 2106.05(d)(II)(i); claims 25-26, 31-32, & 39-40, which recite limitations relating to cleaning testing data, obtaining standard deviations and/or variance of various data, e.g., performing repetitive calculations, Flook, MPEP 2106.05(d)(II)(ii); claims 24-42, maintaining parameters of one or more machine learning and/or artificial intelligence models, maintaining storage and upkeep of one or more received evaluations, results, etc. esp. in a record, e.g., electronic recordkeeping, Alice Corp., MPEP 2106.05(d)(II)(iii); claims 24-42, which recite limitations relating to storing obtained data, analysis, results, etc., storing one or more learning models and parameters thereof, storing computerized instructions in a computer memory for performing the steps recited, e.g., storing and retrieving information in memory, Versata Dev. Group, MPEP 2106.05(d)(II)(iv); claims 26-27, 29, 32-33, 35, 37, 40, which recite limitations relating to obtaining one or more data, evaluations, results, etc., which could include extraction or accessing a patient health record or EHR for parsing said data to obtain, e.g., electronic scanning or extracting data from a physical document, Content Extraction, MPEP 2106.05(d)(II)(v)). Looking at the limitations as an ordered combination adds nothing that is not already present when looking at the elements taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide conventional computer implementation (SME Test Step 2B: No).
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 23-25, 27-31, 34-39, 41-42 are rejected under 35 U.S.C. 102(a)(2) as being anticipated over Odiz et al. (U.S. Patent Publication No. 2020/0357526), hereinafter “Odiz”.
Claim 23 –
Regarding Claim 23, Odiz discloses a health-analysis method comprising:
obtaining testing data of a patient's sample (See Odiz Par [0139] which discloses the user being able to filter the list for specific testing data, such as those relating to “Thoracic Aortic Aneurysm and Dissection”, and thereby receiving clinical guidance and testing results for patients that received this genetic testing);
cleaning the testing data by removing data items unrelated to the health analysis (See Odiz Par [0068] which discloses the graphical genome system may analyze acquired information from a subject and a clinical indication determined by a physician of the subject to generate an output of genomic information of the subject having the clinical indication, such that the system may apply a classification algorithm to the acquired information from the subject and the clinical indication (e.g., a clinical guidance) determined by the physician of the subject to generate the output of genomic information (e.g., a list of genes) of the subject having the clinical indication; See Odiz Par [0088]-[0089] which discloses filtering a gene panel based no parameters, including gene coverage, cost of test, quality of the data, turnaround time, insurance, deletion/duplication, variation coverage, etc.; See Odiz Par [0139] which discloses the user being able to filter the list for specific testing data, such as those relating to “Thoracic Aortic Aneurysm and Dissection”, and thereby receiving clinical guidance and testing results for patients that received this genetic testing, and subsequently, the filter can be removed to return all clinical guidances to view);
obtaining statistics of the cleaned testing data (See Odiz Par [0068] which discloses the machine learning based classifier being configured to process the acquired information from the subject and the clinical indication determined by the physician of the subject to generate the output of genomic information of the subject having the clinical indication; See Odiz Par [0137] which discloses the analytics section providing statistical information); and
obtaining analysis results of one or more genes and/or species of the patient's sample using one or more artificial intelligence (AI) methods based on the obtained statistics of the cleaned testing data and statistics of healthy data (See Odiz Par [0045] which discloses the system being able to apply artificial intelligence-based algorithms to the patient's data to compare the data with its knowledge base to identify disease categories and inheritance patterns).
Claim 24 –
Regarding Claim 24, Odiz discloses the health-analysis method of claim 23 in its entirety. Odiz further discloses a method, wherein:
said obtaining the analysis results of the one or more genes and/or species of the patient's sample comprises:
using a machine learning method for identifying characteristics of the one or more genes, and/or for analyzing genetic data of the testing data and identifying patterns or relationships between different genetic markers (See Odiz Par [0036] & [0045] & [0068]-[0069] which discloses a machine learning model being trained using datasets from one or more sets of subjects with a given clinical indication as inputs and known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome) of the subjects as outputs to the machine learning classifier);
using a random forest method for building a plurality of decision trees and taking their majority vote for classification (See Odiz Par [0068]-[0069] which discloses a random forest and/or decision trees for classification purposes);
using a clustering method for grouping individuals based on their genetic similarity (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a regression method for predicting expression of the one or more genes (See Odiz Par [0068]-[0070] which discloses the use of a regression method for predicting gene expression and predicted outcomes);
using a maximum relevance minimum redundancy (MRMR) method for selecting a set of features of minimum redundancy and maximum relevance with respect to a target variable (See Odiz Par [0045] which discloses the system can match the patient data with patterns in its knowledge base, the more effective the set of identified genetic tests is expected to be, e.g. smaller gene panel with higher accuracy, i.e. a small amount of redundancy and a maximum amount of relevance);
using a deep learning method for analyzing datasets of the testing data and identifying features and/or patterns (See Odiz Par [0068]-[0070] which discloses a deep learning model being applied for analyzing datasets);
using a convolutional neural network (CNN) for recognizing patterns in gene expression data of the testing data for identifying one of the one or more genes that is associated with an environmental condition (See Odiz Par [0068]-[0070] which discloses the use of a neural network for analyzing datasets);
using a reinforcement learning method for optimizing genetic datasets by identifying informative genetic features (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a reinforcement learning method for selecting informative genetic markers for a population while minimizing a number of genetic markers required for predictions (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a genetic method for optimizing genetic datasets by iteratively testing and refining different combinations of genetic markers (See Odiz Par [0068]-[0070] & [0072] which discloses the machine learning classifier being trained until certain predetermined conditions for accuracy or performance is satisfied, the accuracy measure may correspond to classification of known genomic information of a clinical indication in the subject);
using a genetic method for identifying an optimal set of genetic markers for predicting a risk of a particular disease or condition; or a combination thereof (See Odiz Par [0068]-[0070] & [0072] which discloses the machine learning classifier being trained until certain predetermined conditions for accuracy or performance is satisfied, the accuracy measure may correspond to classification of known genomic information of a clinical indication in the subject).
Claim 25 –
Regarding Claim 25, Odiz discloses the health-analysis method of claim 23 in its entirety. Odiz further discloses a method, wherein:
said cleaning the testing data comprises:
cleaning the testing data using one or more gene panels related to the health analysis (See Odiz Par [0045] which discloses the navigator being configured to, based on the identified disease categories and inheritance patterns, propose one or more gene panels as necessary genetic tests; See Odiz Par [0068] which discloses the graphical genome system may analyze acquired information from a subject and a clinical indication determined by a physician of the subject to generate an output of genomic information of the subject having the clinical indication, such that the system may apply a classification algorithm to the acquired information from the subject and the clinical indication (e.g., a clinical guidance) determined by the physician of the subject to generate the output of genomic information (e.g., a list of genes) of the subject having the clinical indication; See Odiz Par [0088]-[0089] which discloses filtering a gene panel based no parameters, including gene coverage, cost of test, quality of the data, turnaround time, insurance, deletion/duplication, variation coverage, etc.; See Odiz Par [0139] which discloses the user being able to filter the list for specific testing data, such as those relating to “Thoracic Aortic Aneurysm and Dissection”, and thereby receiving clinical guidance and testing results for patients that received this genetic testing, and subsequently, the filter can be removed to return all clinical guidances to view).
Claim 27 –
Regarding Claim 27, Odiz discloses the health-analysis method of claim 23 in its entirety. Odiz further discloses a method, wherein:
obtaining the one or more genes and/or species analysis results comprises:
using the one or more Al models to obtain analysis results regarding similarity between the cleaned testing data and the healthy data and/or ranking of the one or more genes or bacteria based on the obtained statistics (See Odiz Par [0069] which discloses the use of an AI model to obtain various outcomes (e.g., labels such as known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome, i.e. cleaned testing data compared to healthy data); See Odiz Par [0070] which discloses training datasets may be generated from one or more sets of subjects having common characteristics (e.g., features such as clinical indications) and outcomes (e.g., labels such as known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome) of the subjects, such that it is understood that the genes that experience a significantly higher or significantly lower mutation rate as compared to a reference genome are thereby ranked according to said mutation rates)).
Claim 28 –
Regarding Claim 28, Odiz discloses the health-analysis method of claim 23 in its entirety. Odiz further discloses a method, further comprising:
using the obtained analysis results of the one or more genes and/or species of the patient's sample for determining one or more diseases for the patient (See Odiz Par [0078] which discloses hospitals being able to track and analyze data corelating genetic variants with various diseases, e.g., correlating patient's diseases or disorders with their genomic health data and other health information ), and/or for analyzing and/or monitoring the patient's response to drugs (See Odiz Par [0115]-[0117] which discloses generating and obtaining a patient’s response to a medication, such that a doctor can check the navigator for a more suitable medication and change medications prior to the prescription being filled at the pharmacy).
Claim 29 –
Regarding Claim 29, Odiz discloses one or more non-transitory computer-readable storage devices comprising computer-executable instructions, wherein the instructions, when executed, cause a processing structure to perform the method of claim 23 (See Odiz Par [0100]).
Claim 30 –
Regarding Claim 30, Odiz discloses the one or more non-transitory computer-readable storage devices of claim 29 in its entirety. Odiz further discloses a device, wherein:
said obtaining the analysis results of the one or more genes and/or species of the patient's sample comprises:
using a machine learning method for identifying characteristics of the one or more genes, and/or for analyzing genetic data of the testing data and identifying patterns or relationships between different genetic markers (See Odiz Par [0036] & [0045] & [0068]-[0069] which discloses a machine learning model being trained using datasets from one or more sets of subjects with a given clinical indication as inputs and known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome) of the subjects as outputs to the machine learning classifier);
using a random forest method for building a plurality of decision trees and taking their majority vote for classification (See Odiz Par [0068]-[0069] which discloses a random forest and/or decision trees for classification purposes);
using a clustering method for grouping individuals based on their genetic similarity (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a regression method for predicting expression of the one or more genes (See Odiz Par [0068]-[0070] which discloses the use of a regression method for predicting gene expression and predicted outcomes);
using a maximum relevance minimum redundancy (MRMR) method for selecting a set of features of minimum redundancy and maximum relevance with respect to a target variable (See Odiz Par [0045] which discloses the system can match the patient data with patterns in its knowledge base, the more effective the set of identified genetic tests is expected to be, e.g. smaller gene panel with higher accuracy, i.e. a small amount of redundancy and a maximum amount of relevance);
using a deep learning method for analyzing datasets of the testing data and identifying features and/or patterns (See Odiz Par [0068]-[0070] which discloses a deep learning model being applied for analyzing datasets);
using a convolutional neural network (CNN) for recognizing patterns in gene expression data of the testing data for identifying one of the one or more genes that is associated with an environmental condition (See Odiz Par [0068]-[0070] which discloses the use of a neural network for analyzing datasets);
using a reinforcement learning method for optimizing genetic datasets by identifying informative genetic features (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a reinforcement learning method for selecting informative genetic markers for a population while minimizing a number of genetic markers required for predictions (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a genetic method for optimizing genetic datasets by iteratively testing and refining different combinations of genetic markers (See Odiz Par [0068]-[0070] & [0072] which discloses the machine learning classifier being trained until certain predetermined conditions for accuracy or performance is satisfied, the accuracy measure may correspond to classification of known genomic information of a clinical indication in the subject);
using a genetic method for identifying an optimal set of genetic markers for predicting a risk of a particular disease or condition; or a combination thereof (See Odiz Par [0068]-[0070] & [0072] which discloses the machine learning classifier being trained until certain predetermined conditions for accuracy or performance is satisfied, the accuracy measure may correspond to classification of known genomic information of a clinical indication in the subject).
Claim 31 –
Regarding Claim 31, Odiz discloses the one or more non-transitory computer-readable storage devices of claim 29 in its entirety. Odiz further discloses a device, wherein:
said obtaining the statistics of the cleaned testing data comprises:
cleaning the testing data using one or more gene panels related to the health analysis (See Odiz Par [0068] which discloses the graphical genome system may analyze acquired information from a subject and a clinical indication determined by a physician of the subject to generate an output of genomic information of the subject having the clinical indication, such that the system may apply a classification algorithm to the acquired information from the subject and the clinical indication (e.g., a clinical guidance) determined by the physician of the subject to generate the output of genomic information (e.g., a list of genes) of the subject having the clinical indication; See Odiz Par [0088]-[0089] which discloses filtering a gene panel based no parameters, including gene coverage, cost of test, quality of the data, turnaround time, insurance, deletion/duplication, variation coverage, etc.; See Odiz Par [0139] which discloses the user being able to filter the list for specific testing data, such as those relating to “Thoracic Aortic Aneurysm and Dissection”, and thereby receiving clinical guidance and testing results for patients that received this genetic testing, and subsequently, the filter can be removed to return all clinical guidances to view).
Claim 34 –
Regarding Claim 34, Odiz discloses the one or more non-transitory computer-readable storage devices of claim 29 in its entirety. Odiz further discloses a device, wherein:
said obtaining the one or more genes and/or species analysis results comprises:
using the one or more Al models to obtain analysis results regarding similarity between the cleaned testing data and the healthy data and/or ranking of the one or more genes or bacteria based on the obtained statistics (See Odiz Par [0069] which discloses the use of an AI model to obtain various outcomes (e.g., labels such as known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome, i.e. cleaned testing data compared to healthy data); See Odiz Par [0070] which discloses training datasets may be generated from one or more sets of subjects having common characteristics (e.g., features such as clinical indications) and outcomes (e.g., labels such as known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome) of the subjects, such that it is understood that the genes that experience a significantly higher or significantly lower mutation rate as compared to a reference genome are thereby ranked according to said mutation rates)).
Claim 35 –
Regarding Claim 35, Odiz discloses the one or more non-transitory computer-readable storage devices of claim 29 in its entirety. Odiz further discloses a device, wherein the method further comprises:
automatically monitoring development in one or more research areas (See Odiz Par [0115] & [0119] which discloses creating a new clinical guidance and patient data needing to be updated regularly or in real time with the integrated EMR system, such that the navigator updating all patient data, assesses the new data (e.g., a new medication or condition) to determine if there are issues or concerns as it relates to PGx, and generates a color-coded notification to the provider indicating the issue, such that updated data can be maintained); and
automatically notifying patients and/or healthcare workers regarding changes of functions of the one or more genes and/or changes of ranking of the one or more genes (See Odiz Par [0115] & [0119] which discloses assessing new data and/or clinical guidance to determine if there are issues or concerns and generating a color-coded notification to the provider and ranking said one or more therapies, genes, medications, etc., according to color-coded indicators as green, yellow, or red).
Claim 36 –
Regarding Claim 36, Odiz discloses the one or more non-transitory computer-readable storage devices of claim 29 in its entirety. Odiz further discloses a device, wherein the method further comprises:
using the obtained analysis results of the one or more genes and/or species of the patient's sample for determining one or more diseases for the patient (See Odiz Par [0078] which discloses hospitals being able to track and analyze data corelating genetic variants with various diseases, e.g., correlating patient's diseases or disorders with their genomic health data and other health information ), and/or for analyzing and/or monitoring the patient's response to drugs (See Odiz Par [0115]-[0117] which discloses generating and obtaining a patient’s response to a medication, such that a doctor can check the navigator for a more suitable medication and change medications prior to the prescription being filled at the pharmacy).
Claim 37 –
Regarding Claim 37, Odiz discloses one or more processors functionally coupled to one or more non-transitory computer-readable storage devices, the one or more non-transitory computer-readable storage devices comprising computer-executable instructions, wherein the instructions, when executed, cause the one or more processors to perform the above-described method for performing the method of any one of claim 23 (See Odiz Par [0100]).
Claim 38 –
Regarding Claim 38, Odiz discloses the one or more processors of claim 37, wherein said obtaining the analysis results of the one or more genes and/or species of the patient's sample comprises:
using a machine learning method for identifying characteristics of the one or more genes, and/or for analyzing genetic data of the testing data and identifying patterns or relationships between different genetic markers (See Odiz Par [0036] & [0045] & [0068]-[0069] which discloses a machine learning model being trained using datasets from one or more sets of subjects with a given clinical indication as inputs and known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome) of the subjects as outputs to the machine learning classifier);
using a random forest method for building a plurality of decision trees and taking their majority vote for classification (See Odiz Par [0068]-[0069] which discloses a random forest and/or decision trees for classification purposes);
using a clustering method for grouping individuals based on their genetic similarity (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a regression method for predicting expression of the one or more genes (See Odiz Par [0068]-[0070] which discloses the use of a regression method for predicting gene expression and predicted outcomes);
using a maximum relevance minimum redundancy (MRMR) method for selecting a set of features of minimum redundancy and maximum relevance with respect to a target variable (See Odiz Par [0045] which discloses the system can match the patient data with patterns in its knowledge base, the more effective the set of identified genetic tests is expected to be, e.g. smaller gene panel with higher accuracy, i.e. a small amount of redundancy and a maximum amount of relevance);
using a deep learning method for analyzing datasets of the testing data and identifying features and/or patterns (See Odiz Par [0068]-[0070] which discloses a deep learning model being applied for analyzing datasets);
using a convolutional neural network (CNN) for recognizing patterns in gene expression data of the testing data for identifying one of the one or more genes that is associated with an environmental condition (See Odiz Par [0068]-[0070] which discloses the use of a neural network for analyzing datasets);
using a reinforcement learning method for optimizing genetic datasets by identifying informative genetic features (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a reinforcement learning method for selecting informative genetic markers for a population while minimizing a number of genetic markers required for predictions (Because this claim limitations is written the alternative “or” this limitation does not necessarily have to be met by Odiz to be anticipated by Odiz under BRI);
using a genetic method for optimizing genetic datasets by iteratively testing and refining different combinations of genetic markers (See Odiz Par [0068]-[0070] & [0072] which discloses the machine learning classifier being trained until certain predetermined conditions for accuracy or performance is satisfied, the accuracy measure may correspond to classification of known genomic information of a clinical indication in the subject);
using a genetic method for identifying an optimal set of genetic markers for predicting a risk of a particular disease or condition; or a combination thereof (See Odiz Par [0068]-[0070] & [0072] which discloses the machine learning classifier being trained until certain predetermined conditions for accuracy or performance is satisfied, the accuracy measure may correspond to classification of known genomic information of a clinical indication in the subject).
Claim 39 –
Regarding Claim 39, Odiz discloses the one or more processors of claim 37 in its entirety. Odiz further discloses one or more processors, wherein:
said cleaning the testing data comprises:
cleaning the testing data using one or more gene panels related to the health analysis (See Odiz Par [0069] which discloses the use of an AI model to obtain various outcomes (e.g., labels such as known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome, i.e. cleaned testing data compared to healthy data); See Odiz Par [0070] which discloses training datasets may be generated from one or more sets of subjects having common characteristics (e.g., features such as clinical indications) and outcomes (e.g., labels such as known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome) of the subjects, such that it is understood that the genes that experience a significantly higher or significantly lower mutation rate as compared to a reference genome are thereby ranked according to said mutation rates)).
Claim 41 –
Regarding Claim 41, Odiz discloses the one or more processors of claim 37 in its entirety. Odiz further discloses one or more processors, wherein:
said obtaining the one or more genes and/or species analysis results comprises:
using the one or more AI models to obtain analysis results regarding similarity between the cleaned testing data and the healthy data and/or ranking of the one or more genes or bacteria based on the obtained statistics (See Odiz Par [0069] which discloses the use of an AI model to obtain various outcomes (e.g., labels such as known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome, i.e. cleaned testing data compared to healthy data); See Odiz Par [0070] which discloses training datasets may be generated from one or more sets of subjects having common characteristics (e.g., features such as clinical indications) and outcomes (e.g., labels such as known genomic information (e.g., genes which experience a significantly higher or significantly lower mutation rate as compared to a reference genome) of the subjects, such that it is understood that the genes that experience a significantly higher or significantly lower mutation rate as compared to a reference genome are thereby ranked according to said mutation rates)).
Claim 42 –
Regarding Claim 42, Odiz discloses the one or more processors of claim 37 in its entirety. Odiz further discloses one or more processors, wherein:
using the obtained analysis results of the one or more genes and/or species of the patient's sample for determining one or more diseases for the patient (See Odiz Par [0078] which discloses hospitals being able to track and analyze data corelating genetic variants with various diseases, e.g., correlating patient's diseases or disorders with their genomic health data and other health information), and/or for analyzing and/or monitoring the patient's response to drugs (See Odiz Par [0115]-[0117] which discloses generating and obtaining a patient’s response to a medication, such that a doctor can check the navigator for a more suitable medication and change medications prior to the prescription being filled at the pharmacy).
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or
nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 26, 32-33, & 40 are rejected under 35 U.S.C. 103 as being unpatentable over Odiz in view of Brandsma et al. (U.S. Patent Publication No. 2023/0018537), hereinafter “Brandsma”.
Claim 26 –
Regarding Claim 26, Odiz discloses the health-analysis method of claim 23 in its entirety. Odiz does not further disclose a method, wherein:
obtaining the standard deviations and/or variance of the cleaned testing data comprises:
obtaining standard deviations and/or variance of real read, estimated read, and abundance of the one or more genes from the cleaned testing data.
However, Brandsma discloses obtaining standard deviations and/or variance of real read, estimated read, and abundance of the one or more genes from the cleaned testing data (See Brandsma Par [0067]-[0068] empirical bayes method algorithm utilizing estimated distributions and are used to approximate values in a dataset and subset data based on the parameters of the estimated distribution, such that feature selection can include various statistics, including t-statistics, i.e. standard deviation/variance analysis of said empirical data). The disclosure of Brandsma is directly applicable to the disclosure of Odiz because the disclosures share limitations and capabilities, such as being directed towards predicting disease development and/or risk based on specific biomolecular/genetic signatures.
It would have been obvious to one of ordinary skill in the effective filing date of the claimed invention to modify the disclosure of Odiz, which already discloses performing statistical analysis on the received/cleaned testing data, to further include obtaining standard deviations and/or variance of real read, estimated read, and abundance of the one or more genes from the cleaned testing data, as disclosed by Brandsma, because this allows for development of an empirical bayes method algorithm utilizing estimated distributions and various selected features including standard deviation/variance analysis of said empirical data (See Brandsma Par [0067]-[0068]).
Claim 32 –
Regarding Claim 32, Odiz discloses the one or more non-transitory computer-readable storage devices of claim 29 in its entirety. Odiz does not, but Brandsma further discloses a device, wherein:
said obtaining the statistics of the cleaned testing data comprises:
obtaining standard deviations and/or variance of the cleaned testing data (See Brandsma Par [0067]-[0068] empirical bayes method algorithm utilizing estimated distributions and are used to approximate values in a dataset and subset data based on the parameters of the estimated distribution, such that feature selection can include various statistics, including t-statistics, i.e. standard deviation/variance analysis of said empirical data).
It would have been obvious to one of ordinary skill in the effective filing date of the claimed invention to modify the disclosure of Odiz, which already discloses performing statistical analysis on the received/cleaned testing data, to further include obtaining standard deviations and/or variance of real read, estimated read, and abundance of the one or more genes from the cleaned testing data, as disclosed by Brandsma, because this allows for development of an empirical bayes method algorithm utilizing estimated distributions and various selected features including standard deviation/variance analysis of said empirical data (See Brandsma Par [0067]-[0068]).
Claim 33 –
Regarding Claim 33, Odiz discloses the one or more non-transitory computer-readable storage devices of claim 29 in its entirety. Odiz does not, but Brandsma further discloses a device, wherein:
said obtaining the standard deviations and/or variance of the cleaned testing data comprises:
obtaining standard deviations and/or variance of real read, estimated read, and abundance of the one or more genes from the cleaned testing data (See Brandsma Par [0067]-[0068] empirical bayes method algorithm utilizing estimated distributions and are used to approximate values in a dataset and subset data based on the parameters of the estimated distribution, such that feature selection can include various statistics, including t-statistics, i.e. standard deviation/variance analysis of said empirical data).
It would have been obvious to one of ordinary skill in the effective filing date of the claimed invention to modify the disclosure of Odiz, which already discloses performing statistical analysis on the received/cleaned testing data, to further include obtaining standard deviations and/or variance of real read, estimated read, and abundance of the one or more genes from the cleaned testing data, as disclosed by Brandsma, because this allows for development of an empirical bayes method algorithm utilizing estimated distributions and various selected features including standard deviation/variance analysis of said empirical data (See Brandsma Par [0067]-[0068]).
Claim 40 –
Regarding Claim 40, Odiz discloses the one or more processors of claim 37 in its entirety. Odiz does not, Brandsma further discloses one or more processors, wherein:
said obtaining the standard deviations and/or variance of the cleaned testing data comprises:
obtaining standard deviations and/or variance of real read, estimated read, and abundance of the one or more genes from the cleaned testing data See Brandsma Par [0067]-[0068] empirical bayes method algorithm utilizing estimated distributions and are used to approximate values in a dataset and subset data based on the parameters of the estimated distribution, such that feature selection can include various statistics, including t-statistics, i.e. standard deviation/variance analysis of said empirical data).
It would have been obvious to one of ordinary skill in the effective filing date of the claimed invention to modify the disclosure of Odiz, which already discloses performing statistical analysis on the received/cleaned testing data, to further include obtaining standard deviations and/or variance of real read, estimated read, and abundance of the one or more genes from the cleaned testing data, as disclosed by Brandsma, because this allows for development of an empirical bayes method algorithm utilizing estimated distributions and various selected features including standard deviation/variance analysis of said empirical data (See Brandsma Par [0067]-[0068]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to Applicant's disclosure:
McCarson et al. (U.S. Patent Publication No. 2025/0200108) discloses an artificial intelligence system and method for analyzing genetic information to create individualized or population-based analysis, diagnostics, or treatment plans;
Jessen et al. (U.S. Patent No. 12,626,189) discloses a system obtaining datasets including features and/or historical laboratory test results for subjects, filtering the datasets based on a denoise-balance scheme to obtain filtered datasets, training a machine learning model using the filtered datasets to obtain a trained machine learning model, and predicting results for clinical diagnostic tests;
Regev et al. (U.S. Patent Publication No. 2022/0180975) discloses train machine learning methods with intrinsic and extrinsic features of a cell and/or tissue to define transcriptomic profiles of the cell and/or tissue.
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/H.R./Examiner, Art Unit 3684
/Shahid Merchant/Supervisory Patent Examiner, Art Unit 3684
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