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 .
Information Disclosure Statement
The information disclosure statements (IDS) submitted on 05/16/2025 are in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Claim Objections
Claim 61 is objected to because of the following informalities:
Claim 61 recites “regions ;” in line 12 of the claim when it should most likely recite “regions.
Appropriate correction is required.
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 61-80 are 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. Claims 61-74 are drawn to a method, claims 75-79 are drawn to a system, and claim 80 is drawn to a media, each of which is within the four statutory categories. Claims 61-80 are further directed to an abstract idea on the grounds set out in detail below. As discussed below, the claims do not include additional elements that are sufficient to amount to significantly more than the abstract idea because the additional computer elements, which are recited at a high level of generality, provide conventional computer functions that do not add meaningful limits to practicing the abstract idea (Step 1: YES).
Step 2A:
Prong One:
Claim 61 recites a method comprising:
1) obtaining, by a) a computing system having one or more hardware processors and memory, testing data from an individual, the testing data including individual sequence representations having a threshold amount of methylated cytosines included in one or more portions of the individual sequence representations having at least threshold cytosine-guanine content;
2) analyzing, by the computing system, the testing data to determine first counts of a first number of the individual sequence representations that correspond to individual classification regions of a plurality of classification regions that have the threshold amount of methylated cytosines and that have at least the threshold cytosine-guanine content;
3) analyzing, by the computing system, the testing data to determine second counts of a second number of the individual sequence representations that correspond to individual control regions of a plurality of control regions;
4) determining, by the computing system, a metric for the individual classification regions based on a ratio of (i) the first counts for the individual classification regions and (ii) the second counts for the individual control regions;
5) generating, by the computing system, an input vector that includes the metrics for the individual classification regions; and
6) determining, by the computing system, an indication of a biological condition being present in the individual by providing the input vector to a model that implements b) one or more machine learning techniques, the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another.
Claim 61 recites, in part, performing the steps of 2) analyzing the testing data to determine first counts of a first number of the individual sequence representations that correspond to individual classification regions of a plurality of classification regions that have the threshold amount of methylated cytosines and that have at least the threshold cytosine-guanine content, 3) analyzing the testing data to determine second counts of a second number of the individual sequence representations that correspond to individual control regions of a plurality of control regions, 4) determining a metric for the individual classification regions based on a ratio of (i) the first counts for the individual classification regions and (ii) the second counts for the individual control regions, 5) generating an input vector that includes the metrics for the individual classification regions, and 6) determining an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more techniques, the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another. These steps correspond to Mathematical Concepts. Independent claims 75 and 80 recite similar limitations and are also directed to an abstract idea under the same analysis.
Depending claims 62-74 and 76-79 include all of the limitations of claims 61 and 75, and therefore likewise incorporate the above described abstract idea. Depending claim 62 adds the additional steps of “obtaining, by the computing system having one or more hardware processors and memory, training data including additional sequence representations having a threshold amount of methylated cytosines included in one or more portions of individual additional sequence representations having at least a threshold cytosine-guanine content”, “analyzing, by the computing system, the training data to determine additional first counts of a first number of the additional sequence representations that corresponds to individual classification regions of the plurality of classification regions”, “analyzing, by the computing system, the training data to determine an additional second counts of a second number of the additional sequence representations that correspond to individual control regions of a plurality of control regions”, “determining, by the computing system, an additional metric for the individual classification regions based on an additional ratio of (i) the additional first counts for the individual classification regions and (ii) the additional second counts for the individual control regions”, “generating, by the computing system, additional training data that includes the additional metric for the individual classification regions”, and “implementing, by the computing system and using the additional training data, the one or more machine learning techniques to generate the model to determine indications of the biological condition being present in individuals”; claim 67 adds the additional step of “performing, by the computing system, a training process using the training data and the additional training data to generate the model, wherein the training process includes: determining, by the computing system, one or more additional weights of individual samples used to produce the training data based on the indication of the biological condition for the individual samples being within a threshold confidence level”; claim 69 adds the additional steps of “performing, by the computing system and using the one or more machine learning techniques, one or more first iterations of the training process for the model using a portion of the training data” and “generating, by the computing system, first output data for the model based on the one or more first iterations of the training process, the first output data corresponding to one or more first additional indications of the biological condition being present in first individuals, wherein first samples obtained from the first individuals are used to produce the portion of the training data”; claim 70 adds the additional steps of “combining, by the computing system, the first output data and the training data to produce further training data”, “performing, by the computing system, one or more second iterations of the training process for the model using a portion of the further training data”, and “generating, by the computing system, second output data for the model based on the one or more second iterations of the training process, the second output data indicating one or more second additional indications of the biological condition being present in second individuals, wherein second samples obtained from the second individuals are used to produce the portion of the additional training data; wherein the weights for the individual classification regions of the plurality of classification regions are determined based on the first output data and the second output data”; claim 72 adds the additional steps of “determining, by the computing system, that a number of indications of the biological condition being present that were determined during one or more iterations of the training process are at least a threshold value” and “determining, by the computing system, that modifications to one or more weights of the model are not modified or are modified by a minimal amount”; claim 73 adds the additional steps of “determining, by the computing system, that an additional number of indications of the biological condition being present that were determined during the one or more iterations of the training process are less than the threshold value” and “determining, by the computing system, that modifications to one or more additional weights of the model are modified by more than the minimal amount”; claim 76 adds the additional steps of “determining, using the testing data, a distribution of the individual sequence representations for a differentially methylated region”, “determining that at least a threshold amount of the individual sequence representations included in the distribution overlap with a subregion of the differentially methylated region”, and “determining that the subregion of the differentially methylated region is a classification region of the plurality of classification regions”; claim 78 adds the additional steps of “determining an order of values of the metrics” and “determining a subset of the individual classification regions from among the plurality of classification regions based on the order; and a portion of the metrics that correspond to the subset of the individual classification regions is used to determine the indication of the biological condition being present in the individual”; and claim 79 adds the additional step of “applying a scaling factor to the initial indication of the biological condition being present in the individual to determine a modified indication of the biological condition being present in the individual”. Additionally, the limitations of depending claims 63-66, 71, 74, and 77 further specify elements from the claims from which they depend on without adding any additional steps. These additional limitations only further serve to limit the abstract idea. Thus, depending claims 62-74 are nonetheless directed towards fundamentally the same abstract idea as independent claims 61 and 75 (Step 2A (Prong One): YES).
Prong Two:
This judicial exception is not integrated into a practical application. In particular, the claims recite the additional elements of – using a) a computing system having one or more hardware processors and memory, b) one or more machine learning techniques, and c) one or more non-transitory computer-readable storage media including computer-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations (from claims 75 and 80) to perform the claimed steps.
The claims also include the additional element step of 1) “obtaining testing data from an individual, the testing data including individual sequence representations having a threshold amount of methylated cytosines included in one or more portions of the individual sequence representations having at least threshold cytosine-guanine content.”
The a) a computing system having one or more hardware processors and memory and c) one or more non-transitory computer-readable storage media including computer-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations in these steps are recited at a high-level of generality (i.e., as generic components performing generic computer functions) such that they amount to no more than mere instructions to apply the exception using generic computer components (see: Applicant’s specification, paragraph [0499] where there are generic components for these claim elements, see MPEP 2106.05(f)).
The additional element step of 1) “obtaining testing data from an individual, the testing data including individual sequence representations having a threshold amount of methylated cytosines included in one or more portions of the individual sequence representations having at least threshold cytosine-guanine content” adds insignificant extra-solution activity to the abstract idea which amounts to mere data gathering, see MPEP 2106.05(g).
The b) one or more machine learning techniques in these steps generally links the abstract idea to a particular technological environment or field of use (such as machine learning, see MPEP 2106.05(h)).
Dependent claims recite additional subject matter which amount to limitations consistent with the additional elements in the independent claims. 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.
Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea (Step 2A (Prong Two): NO).
Step 2B:
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 integration of the abstract idea into a practical application, the additional elements of using a) a computing system having one or more hardware processors and memory, b) one or more machine learning techniques, and c) one or more non-transitory computer-readable storage media including computer-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations to perform the claimed steps and using the additional element step of 1) “obtaining testing data from an individual, the testing data including individual sequence representations having a threshold amount of methylated cytosines included in one or more portions of the individual sequence representations having at least threshold cytosine-guanine content” amounts to no more than insignificant extra-solution activity in the form of WURC activity (well-understood, routine, and conventional activity), a general linking to a particular technological field, and mere instructions to apply the exception using generic computer components that do not offer “significantly more” than the abstract idea itself because the claims do not recite an improvement to another technology or technical field, an improvement to the functioning of any computer itself, or provide meaningful limitations beyond generally linking an abstract idea to a particular technological environment. It should be noted that the claims do not include additional elements that amount to significantly more than the judicial exception because the Specification recites mere generic computer components, as discussed above that are being used to apply certain method steps of organizing human activity. Specifically, MPEP 2106.05(d), MPEP 2106.05(f), and MPEP 2106.05(h) recite that the following limitations are not significantly more:
Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 573 U.S. at 225, 110 USPQ2d at 1984 (see MPEP § 2106.05(d));
Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); and
Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook, 437 U.S. 584, 588-90, 198 USPQ 193, 197-98 (1978) (MPEP § 2106.05(h)).
The current invention determines a condition utilizing a) a computing system having one or more hardware processors and memory and c) one or more non-transitory computer-readable storage media including computer-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations, thus these computing components are adding the words “apply it” with mere instructions to implement the abstract idea on a computer.
Additionally, the b) one or more machine learning techniques generally links the abstract idea to a particular technological environment or field of use. The following represent an example that courts have identified as generally linking the abstract idea to a particular technological environment (e.g. see MPEP 2106.05(h)): Limiting the abstract idea data to using machine learning techniques, because limiting application of the abstract idea to machine learning is simply an attempt to limit the use of the abstract idea to a particular technological environment, e.g. see Electric Power Group, LLC v. Alstom S.A.
Lastly, the additional element step of 1) “obtaining testing data from an individual, the testing data including individual sequence representations having a threshold amount of methylated cytosines included in one or more portions of the individual sequence representations having at least threshold cytosine-guanine content” in these steps add insignificant extra-solution activity/pre-solution activity in the form of WURC activity to the abstract idea. The following is an example of a court decision demonstrating computer functions as well-understood, routine and conventional activities, e.g. see MPEP 2106.05(d)(II): Receiving or transmitting data over a network, e.g. see Intellectual Ventures v. Symantec – similarly, the current invention receives testing data, and transmits the data to system over a network, for example the Internet.
Mere instructions to apply an exception using generic computer components, a general linking to a particular technological field, or insignificant extra-solution activity in the form of WURC activity cannot provide an inventive concept. The claims are not patent eligible (Step 2B: NO).
Claims 61-80 are therefore rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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.
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(s) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 61-65, 75-77, and 79-80 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0407623 to Westesson et al. in view of U.S. 10,861,590 to White et al.
As per claim 61, Westesson et al. teaches method comprising:
--obtaining, by a computing system having one or more hardware processors and memory, testing data from an individual, the testing data including individual sequence representations having a threshold amount of methylated cytosines included in one or more portions of the individual sequence representations having at least threshold cytosine-guanine content; (see: claim 1 where there is such an obtaining step)
--analyzing, by the computing system, the testing data to determine first counts of a first number of the individual sequence representations that correspond to individual classification regions of a plurality of classification regions that have the threshold amount of methylated cytosines and that have at least the threshold cytosine-guanine content; (see: claim 1 where there is such an analyzing step)
--analyzing, by the computing system, the testing data to determine second counts of a second number of the individual sequence representations that correspond to individual control regions of a plurality of control regions; (see: claim 1 where there is such an analyzing step) and
--determining, by the computing system, a metric for the individual classification regions based on a ratio of (i) the first counts for the individual classification regions and (ii) the second counts for the individual control regions (see: claim 1 where there is such a determining step).
Westesson et al. may not further, specifically teach:
1) --generating, by the computing system, an input vector that includes the metrics for the individual classification regions; and
2) --determining, by the computing system, an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another.
White et al. teaches:
1) --generating, by the computing system, an input vector that includes the metrics for the individual classification regions; (see: column 21, lines 39-58 and claim 1 where there is generation of a patient vector) and
2) --determining, by the computing system, an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, (see: claim 1 where there is a determination of an indication of a biological condition using this vector information and a machine learning model) the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another (see: claim 1 where there is a determination of an indication of a biological condition using this vector information. Column 27, lines 46-47 where there is a prediction of a future medical state and column 28, lines 1-30 where there is prediction of a future set of body coordinates and condition. The model includes weights which define what the condition is and where its location is).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) generate, by the computing system, an input vector that includes the metrics for the individual classification regions and 2) determine, by the computing system, an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another as taught by White et al. in the method as taught by Westesson et al. with the motivation(s) of assessing a patient for the afflictions (see: column 1, lines 22-39 of White et al.).
As per claim 62, Westesson et al. and White et al. in combination teaches the method of claim 61, see discussion of claim 61. Westesson et al. further teaches:
--obtaining, by the computing system having one or more hardware processors and memory, training data including additional sequence representations having a threshold amount of methylated cytosines included in one or more portions of individual additional sequence representations having at least a threshold cytosine-guanine content; (see: Table 2 and claim 1 where there is the obtaining step and this would apply to both the training data and the testing data)
--analyzing, by the computing system, the training data to determine additional first counts of a first number of the additional sequence representations that corresponds to individual classification regions of the plurality of classification regions; (see: Table 2 and claim 1 where there is the analyzing step and this would apply to both the training data and the testing data)
--analyzing, by the computing system, the training data to determine an additional second counts of a second number of the additional sequence representations that correspond to individual control regions of a plurality of control regions; (see: Table 2 and claim 1 where there is the analyzing step and this would apply to both the training data and the testing data) and
--determining, by the computing system, an additional metric for the individual classification regions based on an additional ratio of (i) the additional first counts for the individual classification regions and (ii) the additional second counts for the individual control regions (see: Table 2 and claim 1 where there is the determining step and this would apply to both the training data and the testing data).
White et al. further teaches:
--generating, by the computing system, additional training data that includes the additional metric for the individual classification regions; (see: column 21 line 39-58 where there is generation of patient vector data (additional training data)) and
--implementing, by the computing system and using the additional training data, the one or more machine learning techniques to generate the model to determine indications of the biological condition being present in individuals (see: column 27 line 63- column 28, line 30 where there is generation of a model using the generated patient data (additional training data) using machine learning techniques (LTSM) to determine the indications of a biological condition being present).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 61, and incorporated herein.
As per claim 63, Westesson et al. and White et al. in combination teaches the method of claim 61, see discussion of claim 61. Westesson et al. further teaches wherein:
--the one or more machine learning techniques include one or more classification algorithms; (see: paragraph [0129] where there is classification occurring, thus there is a classification algorithm present) and
--the indication of the biological condition corresponds to a first numerical indicator of the biological condition being present in the individual (see: paragraph [0150] where there is a model generating likelihoods. This is the first numerical indicator of a condition being present in the individual).
As per claim 64, Westesson et al. and White et al. in combination teaches the method of claim 61, see discussion of claim 61. Westesson et al. further teaches wherein the one or more machine learning techniques include one or more regression algorithms; (see: paragraph [0118] where there is linear regression used) and
--the indication of the biological condition corresponds to a second numerical indicator of the biological condition being present in the individual (see: paragraphs [0118] – [0119] where once the MBD binding calibration data is determined at step 520, the method 500 may proceed to determine a tumor fraction at step 530).
As per claim 65, Westesson et al. and White et al. in combination teaches the method of claim 61, see discussion of claim 61. Westesson et al. further teaches herein the one or more machine learning techniques include:
--a classification algorithm that determines a first numerical indication of the biological condition being present in the individual; (see: paragraph [0129] where there is classification occurring, thus there is a classification algorithm present. Also see: paragraph [0150] where there is a model generating likelihoods. This is the first numerical indicator of a condition being present in the individual) and
--a regression algorithm that determines a second numerical indication of the biological condition being present in the individual; (paragraphs [0118] – [0119] where there is linear regression used. Also, once the MBD binding calibration data is determined at step 520, the method 500 may proceed to determine a tumor fraction at step 530)
--wherein an integration system combines the first numerical indication and the second numerical indication to determine an aggregated numerical indication of the biological condition being present in the individual (see: paragraph [0224] where there is aggregation of the site count data).
As per claim 75, Westesson et al. teaches a computing system includes:
--one or more hardware processors; (see: paragraph [0009] where there are processors) and
--one or more non-transitory computer-readable storage media including computer-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform operations (see: paragraph [0043] where there is a storage media) comprising:
--obtaining testing data from an individual, the testing data including individual sequence representations having a threshold amount of methylated cytosines included in one or more portions of the individual sequence representations having at least threshold cytosine-guanine content; (see: claim 1 where there is such an obtaining step)
--analyzing the testing data to determine first counts of a first number of the individual sequence representations that correspond to individual classification regions of a plurality of classification regions that have the threshold amount of methylated cytosines and that have at least the threshold cytosine-guanine content; (see: claim 1 where there is such an analyzing step)
--analyzing the testing data to determine second counts of a second number of the individual sequence representations that correspond to individual control regions of a plurality of control regions; (see: claim 1 where there is such an analyzing step) and
--determining a metric for the individual classification regions based on a ratio of (i) the first counts of the individual classification regions and the second counts of the individual control regions (see: claim 1 where there is such a determining step).
Westesson et al. may not further, specifically teach:
1) --generating an input vector that includes the metrics for the individual classification regions; and
2) --determining an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another.
White et al. teaches:
1) --generating an input vector that includes the metrics for the individual classification regions; (see: column 21, lines 39-58 and claim 1 where there is generation of a patient vector) and
2) --determining an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, (see: claim 1 where there is a determination of an indication of a biological condition using this vector information and a machine learning model) the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another (see: claim 1 where there is a determination of an indication of a biological condition using this vector information. Column 27, lines 46-47 where there is a prediction of a future medical state and column 28, lines 1-30 where there is prediction of a future set of body coordinates and condition. The model includes weights which define what the condition is and where its location is).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) generate an input vector that includes the metrics for the individual classification regions and 2) determine an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another as taught by White et al. in the system as taught by Westesson et al. with the motivation(s) of assessing a patient for the afflictions (see: column 1, lines 22-39 of White et al.).
As per claim 76, Westesson et al. and White et al. in combination teaches the system of claim 75, see discussion of claim 75. Westesson et al. further teaches wherein the one or more non-transitory computer-readable storage media include additional computer-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform additional operations (see: paragraph [0179] where there are computing instructions which perform operations) comprising:
--determining, using the testing data, a distribution of the individual sequence representations for a differentially methylated region; (see: paragraph [0146] where there is such a determination)
--determining that at least a threshold amount of the individual sequence representations included in the distribution overlap with a subregion of the differentially methylated region; (see: paragraph [0292] where there is such a determination) and
--determining that the subregion of the differentially methylated region is a classification region of the plurality of classification regions (see: paragraph [0292] where there is such a determination).
As per claim 77, Westesson et al. and White et al. in combination teaches the system of claim 76, see discussion of claim 76. Westesson et al. further teaches wherein the threshold amount of sequence representations is at least about 70% of the sequence representations included in the distribution (see: paragraph [0072] where there is such a threshold percentage amount).
As per claim 79, Westesson et al. and White et al. in combination teaches the system of claim 75, see discussion of claim 75. Westesson et al. further teaches wherein:
--the indication of the biological condition being present in the individual is an initial indication of the biological condition being present in the individual; (see: paragraph [0047] where there is an initial indication of a biological condition of the patient having a tumor) and
--the one or more non-transitory computer-readable storage media include additional computer-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform additional operations (see: paragraph [0179] where there are computing instructions which perform operations) comprising:
--applying a scaling factor to the initial indication of the biological condition being present in the individual to determine a modified indication of the biological condition being present in the individual (see: paragraph [0108] where there is an application of a scaling factor to the initial indication).
As per claim 80, Westesson et al. teaches one or more non-transitory computer-readable storage media including computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform operations (see: paragraph [0043] where there is a storage media) comprising:
--obtaining testing data from an individual, the testing data including individual sequence representations having a threshold amount of methylated cytosines included in one or more portions of the individual sequence representations having at least threshold cytosine-guanine content; (see: claim 1 where there is such an obtaining step)
--analyzing the testing data to determine first counts of a first number of the individual sequence representations that correspond to individual classification regions of a plurality of classification regions that have the threshold amount of methylated cytosines and that have at least the threshold cytosine-guanine content; (see: claim 1 where there is such an analyzing step)
--analyzing the testing data to determine second counts of a second number of the individual sequence representations that correspond to individual control regions of a plurality of control regions; (see: claim 1 where there is such an analyzing step) and
--determining a metric for the individual classification regions based on a ratio of (i) the first counts for the individual classification regions and (ii) the second counts for the individual control regions (see: claim 1 where there is such a determining step).
Westesson et al. may not further, specifically teach:
1) --generating an input vector that includes the metrics for the individual classification regions; and
2) --determining an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another.
White et al. teaches:
1) --generating an input vector that includes the metrics for the individual classification regions; (see: column 21, lines 39-58 and claim 1 where there is generation of a patient vector) and
2) --determining an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, (see: claim 1 where there is a determination of an indication of a biological condition using this vector information and a machine learning model) the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another (see: claim 1 where there is a determination of an indication of a biological condition using this vector information. Column 27, lines 46-47 where there is a prediction of a future medical state and column 28, lines 1-30 where there is prediction of a future set of body coordinates and condition. The model includes weights which define what the condition is and where its location is).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to 1) generate an input vector that includes the metrics for the individual classification regions and 2) determine an indication of a biological condition being present in the individual by providing the input vector to a model that implements one or more machine learning techniques, the model including weights for individual classification regions of the plurality of classification regions and at least a portion of the weights of the individual classification regions being different from one another as taught by White et al. in the media as taught by Westesson et al. with the motivation(s) of assessing a patient for the afflictions (see: column 1, lines 22-39 of White et al.).
Claims 67-68 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0407623 to Westesson et al. in view of U.S. 10,861,590 to White et al. as applied to claim 76, and further in view of U.S. 2022/0031955 to Haun et al.
As per claim 67, Westesson et al. and White et al. in combination teaches the method of claim 62, see discussion of claim 62. The combination may not further, specifically teach:
--performing, by the computing system, a training process using the training data and the additional training data to generate the model, wherein the training process includes:
--determining, by the computing system, one or more additional weights of individual samples used to produce the training data based on the indication of the biological condition for the individual samples being within a threshold confidence level.
Haun et al. teaches:
--performing, by the computing system, a training process using the training data and the additional training data to generate the model, wherein the training process includes:
--determining, by the computing system, one or more additional weights of individual samples used to produce the training data based on the indication of the biological condition for the individual samples being within a threshold confidence level (see: claim 5 where there is such a determination of such weights).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to perform, by the computing system, a training process using the training data and the additional training data to generate the model, wherein the training process includes: determine, by the computing system, one or more additional weights of individual samples used to produce the training data based on the indication of the biological condition for the individual samples being within a threshold confidence level as taught by Haun et al. in the method as taught by Westesson et al. and White et al. in combination with the motivation(s) of improving data quality (see: paragraph [0017] of Haun et al.).
As per claim 68, Westesson et al., White et al., and Haun et al. in combination teaches the method of claim 67, see discussion of claim 67. Haun et al. further teaches wherein the indication of the biological condition for an individual sample is outside of the threshold confidence level and the method comprises:
--applying, by the computing system, a penalty to a weight of the individual sample during the training process (see: paragraph [0152] where there is such a penalty to a weight during training).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 67, and incorporated herein.
Claims 69-70 and 72 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0407623 to Westesson et al. in view of U.S. 10,861,590 to White et al. further in view of U.S. 2022/0031955 to Haun et al. as applied to claim 67, and further in view of U.S. 2022/0150275 to McNee et al.
As per claim 69, Westesson et al., White et al., and Haun et al. in combination teaches the method of claim 67, see discussion of claim 67. The combination may not further, specifically teach:
--performing, by the computing system and using the one or more machine learning techniques, one or more first iterations of the training process for the model using a portion of the training data; and
--generating, by the computing system, first output data for the model based on the one or more first iterations of the training process, the first output data corresponding to one or more first additional indications of the biological condition being present in first individuals, wherein first samples obtained from the first individuals are used to produce the portion of the training data.
McNee et al. teaches:
--performing, by the computing system and using the one or more machine learning techniques, one or more first iterations of the training process for the model using a portion of the training data; (see: paragraph [0058] where there are iterations used to improve the model) and
--generating, by the computing system, first output data for the model based on the one or more first iterations of the training process, the first output data corresponding to one or more first additional indications of the biological condition being present in first individuals, wherein first samples obtained from the first individuals are used to produce the portion of the training data (see: paragraph [0058] where there is generation of feedback data which is used to improve the model. The data being related to samples was taught in the independent claim).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to perform, by the computing system and using the one or more machine learning techniques, one or more first iterations of the training process for the model using a portion of the training data and generate, by the computing system, first output data for the model based on the one or more first iterations of the training process, the first output data corresponding to one or more first additional indications of the biological condition being present in first individuals, wherein first samples obtained from the first individuals are used to produce the portion of the training data as taught by McNee et al. in the method as taught by Westesson et al., White et al., and Haun et al. in combination with the motivation(s) of improving the accuracy and reliability of the predictions (see: paragraph [0022] of McNee et al.).
As per claim 70, Westesson et al., White et al., Haun et al., and McNee et al. in combination teaches the method of claim 69, see discussion of claim 69. McNee et al. further teaches:
--combining, by the computing system, the first output data and the training data to produce further training data; (see: paragraph [0058] where there is combining of previous data and the feedback in order to learn and improve the model)
--performing, by the computing system, one or more second iterations of the training process for the model using a portion of the further training data; (see: paragraph [0058] where there iteration of the model using the feedback data) and
--generating, by the computing system, second output data for the model based on the one or more second iterations of the training process, the second output data indicating one or more second additional indications of the biological condition being present in second individuals, wherein second samples obtained from the second individuals are used to produce the portion of the additional training data; (see: paragraph [0058] where there is output data of feedback data which is used to constantly improved)
--wherein the weights for the individual classification regions of the plurality of classification regions are determined based on the first output data and the second output data (see: paragraph [0058] where the weights are improved for the model).
The motivations to combine the above-mentioned references are discussed in the rejection of claim 70, and incorporated herein.
As per claim 72, Westesson et al., White et al., and Haun et al. in combination teaches the method of claim 67, see discussion of claim 67. The combination may not further, specifically teach:
--determining, by the computing system, that a number of indications of the biological condition being present that were determined during one or more iterations of the training process are at least a threshold value; and
--determining, by the computing system, that modifications to one or more weights of the model are not modified or are modified by a minimal amount.
McNee et al. teaches:
--determining, by the computing system, that a number of indications of the biological condition being present that were determined during one or more iterations of the training process are at least a threshold value; (see: paragraphs [0020] and [0058] where there is a determination that a number of indications has met a threshold value of an optimization value. The indications being for a biological condition was already taught in the previous claims) and
--determining, by the computing system, that modifications to one or more weights of the model are not modified or are modified by a minimal amount (see: paragraph [0020] where there is a determination that modifications of weights are modified by a minimal amount (minimum for an optimization value)).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to determine, by the computing system, that a number of indications of the biological condition being present that were determined during one or more iterations of the training process are at least a threshold value and determine, by the computing system, that modifications to one or more weights of the model are not modified or are modified by a minimal amount as taught by McNee et al. in the method as taught by Westesson et al., White et al., and Haun et al. in combination with the motivation(s) of improving the accuracy and reliability of the predictions (see: paragraph [0022] of McNee et al.).
Claim 74 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0407623 to Westesson et al. in view of U.S. 10,861,590 to White et al. as applied to claim 61, and further in view of U.S. 2020/0157636 to Velculescu et al.
As per claim 74, Westesson et al. and White et al. in combination teaches the method of claim 61, see discussion of claim 61. The combination may not further, specifically teach wherein a limit of detection for the model to determine the indication of the biological condition is no greater than 0.05%.
Velculescu et al. teaches:
--wherein a limit of detection for the model to determine the indication of the biological condition is no greater than 0.05% (see: paragraph [0269] where there is such a limit).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to have wherein a limit of detection for the model to determine the indication of the biological condition is no greater than 0.05% as taught by Velculescu et al. in the method as taught by Westesson et al. and White et al. in combination with the motivation(s) of improving the detection for cancer (see: paragraph [0038] of Velculescu et al.).
Claim 78 is rejected under 35 U.S.C. 103 as being unpatentable over U.S. 2021/0407623 to Westesson et al. in view of U.S. 10,861,590 to White et al. as applied to claim 75, and further in view of U.S. 2011/0028333 to Christensen et al.
As per claim 78, Westesson et al. and White et al. in combination teaches the system of claim 75, see discussion of claim 75. Westesson et al. further teaches wherein:
--the one or more non-transitory computer-readable storage media include additional computer-readable instructions that, when executed by the one or more hardware processors, cause the one or more hardware processors to perform additional operations (see: paragraph [0179] where there are computing instructions which perform operations) comprising:
--determining a subset of the individual classification regions from among the plurality of classification regions based on the order; (see: paragraph [0206] where there is a determination of a subset) and
--a portion of the metrics that correspond to the subset of the individual classification regions is used to determine the indication of the biological condition being present in the individual (see: paragraph [0208] where there is a determination of amounts of homology between at least a portion of the sequencing reads included in the sequencing data).
Westesson et al. and White et al. in combination may not further, specifically teach:
--determining an order of values of the metrics.
Christensen et al. teaches:
--determining an order of values of the metrics (see: paragraphs [0004] and [0010] where the classification regions which are used to indicate cancer in a subject are selected based on the amount of differential methylation within the region. The regions with the highest differential methylation are used as a subset. The highest differential methylation is equivalent to hyper- and hypomethylation. Hypermethylated regions are known as contributors to carcinogenesis).
One of ordinary skill before the effective filing date of the claimed invention would have found it obvious to determining an order of values of the metrics as taught by Christensen et al. in the media as taught by Westesson et al. and White et al. in combination with the motivation(s) of improving sample classification (see: paragraph [0089] of Christensen et al.).
No Prior Art Rejections
Claim 66, 71, and 73 have not been given prior art rejections.
Conclusion
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/STEVEN G.S. SANGHERA/Primary Examiner, Art Unit 3684
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