DETAILED ACTION
It is noted that the examiner and art unit of record have changed since the previous Office 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 .
Election/Restrictions
Applicant’s election without traverse of Group II, claims 29-30 and 35-39, in the reply filed on 10 November 2025 is acknowledged. However, it is noted that after further consideration that there is not a search and/or burden between the different groups of inventions. Therefore, the restriction requirement mailed 11 June 2025 is hereby withdrawn. In view of the withdrawal of the restriction requirement as to the rejoined inventions, applicant(s) are advised that if any claim presented in a divisional application is anticipated by, or includes all the limitations of, a claim that is allowable in the present application, such claim may be subject to provisional statutory and/or non-statutory double patenting rejections over the claims of the instant application.
Once the restriction requirement is withdrawn, the provisions of 35 U.S.C. 121 are no longer applicable. See In re Ziegler, 443 F.2d 1211, 1215, 170 USPQ 129, 131-32 (CCPA 1971). See also MPEP § 804.01.
Claim Status
Claims 14, 28, and 31-34 are cancelled.
Claims 1-13, 29-30, and 35-39 are currently pending and examined on the merits.
Claims 1-13, 29-30, and 35-39 are rejected.
Priority
The instant application claims priority to U.S. Provisional Application 63/171,355 filed on 6 April 2021. At this point in examination, the effective filing date of claims 1-13, 29-30, and 35-39 is 6 April 2021.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 7 June 2023 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements have been considered by the examiner.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because of the following:
Reference characters "110" and "172" have both been used to designate "Receive a first plurality of cancer signals of a first sample".
Reference characters “120” and “174” have both been used to designate “Determine a first cancer signal having a greatest probability among the first plurality of cancer signals”.
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Figures 9 and 16 are submitted in color. Color photographs and color drawings are not accepted in utility applications unless a petition filed under 37 CFR 1.84(a)(2) is granted. Any such petition must be accompanied by the appropriate fee set forth in 37 CFR 1.17(h), one set of color drawings or color photographs, as appropriate, if submitted via the USPTO patent electronic filing system or three sets of color drawings or color photographs, as appropriate, if not submitted via the via USPTO patent electronic filing system, and, unless already present, an amendment to include the following language as the first paragraph of the brief description of the drawings section of the specification:
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.
Color photographs will be accepted if the conditions for accepting color drawings and black and white photographs have been satisfied. See 37 CFR 1.84(b)(2).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-13, 29-30, and 35-39 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims recite: (a) mathematical concepts, (e.g., mathematical relationships, formulas or equations, mathematical calculations); (b) mental processes, i.e., concepts performed in the human mind, (e.g., observation, evaluation, judgement, opinion); and (c) natural phenomena/laws of nature.
Subject matter eligibility evaluation in accordance with MPEP 2106:
Eligibility Step 1: Claims 1-13 are directed to a method (process) for cancer diagnosis. Claim 29 is directed to a system (machine). Claims 30 and 35-39 are directed to a non-transitory computer readable medium (machine). Therefore, these claims are encompassed by the categories of statutory subject matter, and thus satisfy the subject matter eligibility requirements under Step 1.
[Step 1: YES]
Eligibility Step 2A: First, it is determined in Prong One whether a claim recites a judicial exception, and if so, then it is determined in Prong Two whether the recited judicial exception is integrated into a practical application of that exception.
Eligibility Step 2A, Prong One: In determining whether a claim is directed to a judicial exception, examination is performed that analyzes whether the claim recites a judicial exception, i.e., whether a law of nature, natural phenomenon, or abstract idea is set forth described in the claim.
Claims 1-10, 29-30, and 35-38 recite the following steps which fall within the mental processes and/or mathematical concepts groups of abstract ideas, and/or laws of nature/natural phenomena, as noted below.
Independent claims 1 and 29-30 further recite:
determining a first cancer signal having a greatest probability among the first plurality of cancer signals (i.e., mental processes);
responsive to determining that the first cancer signal satisfies a criterion (i.e., mental processes);
associating the first sample with a disease state corresponding to the first cancer signal (i.e., mental processes, laws of nature);
determining a second cancer signal having a greatest probability among the second plurality of cancer signals (i.e., mental processes);
responsive to determining that the second cancer signal does not satisfy the criterion (i.e., mental processes);
associating the second sample with a subset of the plurality of disease states corresponding to a subset of the second plurality of cancer signals including at least the second cancer signal (i.e., mental processes, laws of nature);
Dependent claims 2 and 35 further recite:
determining a third cancer signal having a second greatest probability among the second plurality of cancer signals, wherein the subset of the second plurality of cancer signals further includes the third cancer signal (i.e., mental processes).
Dependent claims 3 and 36 further recite:
wherein the criterion is a probability threshold (i.e., mental processes);
wherein determining that the first cancer signal satisfies the criterion comprises: determining that the greatest probability of the first cancer signal is greater than the probability threshold (i.e., mental processes).
Dependent claim 4 further recites:
wherein the probability threshold is at least 90% (i.e., mental processes).
Dependent claim 5 further recites:
determining the criterion based on accuracy of cancer signal probabilities and false positives (i.e., mental processes).
Dependent claim 6 further recites:
determining the criterion based on residual risk of current cancer being associated with a sample (i.e., mental processes).
Dependent claims 7 and 37 further recite:
determining a subset of n cancer signals of the first plurality of cancer signals having the n greatest probabilities among the first plurality of cancer signals (i.e., mental processes);
responsive to determining that at least a threshold number of the subset of the first plurality of cancer signals is associated with a category of disease states, associating the first sample with each disease state of the category of disease states (i.e., mental processes, laws of nature).
Dependent claim 8 further recites:
wherein the category of disease states is human papillomavirus (HPV) cancer (i.e., mental processes, laws of nature).
Dependent claim 9 further recites:
wherein the category of disease states includes stomach cancer and intestinal cancer (i.e., mental processes, laws of nature).
Dependent claims 10 and 38 further recite:
wherein the plurality of disease states includes a non-cancer state (i.e., mental processes, laws of nature).
The abstract ideas recited in the claims are evaluated under the broadest reasonable interpretation (BRI) of the claim limitations when read in light of and consistent with the specification. As noted in the foregoing section, the claims are determined to contain limitations that can practically be performed in the human mind with the aid of a pencil and paper, and therefore recite judicial exceptions from the mental process grouping of abstract ideas. Additionally, the recited limitations that are identified as judicial exceptions from the mathematical concepts grouping of abstract ideas are abstract ideas irrespective of whether or not the limitations are practical to perform in the human mind. Claims are also determined to contain limitations that include naturally occurring principles/relations or nature-based products that are naturally occurring, and therefore recite judicial exceptions from the laws of nature/natural phenomena grouping.
Therefore, claims 1-10, 29-30, and 35-38 recite an abstract idea.
[Step 2A, Prong One: YES]
Eligibility Step 2A, Prong Two: In determining whether a claim is directed to a judicial exception, further examination is performed that analyzes if the claim recites additional elements that, when examined as a whole, integrates the judicial exception(s) into a practical application (MPEP 2106.04(d)). A claim that integrates a judicial exception into a practical application will apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception. The claimed additional elements are analyzed to determine if the abstract idea is integrated into a practical application (MPEP 2106.04(d)(I); MPEP 2106.05(a-h)). If the claim contains no additional elements beyond the abstract idea, the claim fails to integrate the abstract idea into a practical application (MPEP 2106.04(d)(III)).
Claims 1, 11-13, 29-30, and 39 recite the additional non-abstract elements of data gathering:
receiving a first plurality of cancer signals of a first sample of a first individual, wherein each one of the first plurality of cancer signals indicates a probability that the first sample is associated with a different disease state of a plurality of disease states (claims 1 and 29-30);
providing, for presentation on a client device to determine a first diagnosis of the first individual, the disease state corresponding to the first cancer signal associated with the first sample (claims 1 and 29-30);
receiving a second plurality of cancer signals of a second sample of a second individual, wherein each one of the second plurality of cancer signals indicates a probability that the second sample is associated with a different disease state of the plurality of disease states (claims 1 and 29-30);
providing, for presentation on the client device to determine a second diagnosis of the second individual, the subset of the plurality of disease states corresponding to the subset of the second plurality of cancer signals associated with the second sample (claims 1 and 29-30);
wherein the plurality of disease states includes one or more types of cancer selected from the group including anus cancer, breast cancer, uterine cancer, cervical cancer, ovarian cancer, bladder cancer, urothelial cancer of renal pelvis and ureter, renal cancer other than urothelial, prostate cancer, anorectal cancer, colorectal cancer, squamous cell cancer of esophagus, esophageal cancer other than squamous, gastric cancer, hepatobiliary cancer arising from hepatocytes, hepatobiliary cancer arising from cells other than hepatocytes, pancreatic cancer, human-papillomavirus-associated head and neck cancer, head and neck cancer not associated with human papillomavirus, lung adenocarcinoma, small cell lung cancer, squamous cell lung cancer and lung cancer other than adenocarcinoma or small cell lung cancer, neuroendocrine cancer, melanoma, thyroid cancer, sarcoma, multiple myeloma, lymphoma, leukemia, kidney cancer, liver cancer, bile duct cancer, plasma cell neoplasm cancer, upper gastrointestinal tract cancer, vulvar cancer, and lung neuroendocrine tumors and other high-grade neuroendocrine tumors (claim 11);
providing, for presentation on the client device, a graphical comparison of each disease state corresponding to the subset of the plurality of disease states associated with the second sample (claims 12 and 39);
wherein the graphical comparison is a bar plot based on the probabilities of the second plurality of cancer signals (claim 13).
which are each a data gathering step, or a description of the data gathered.
Data gathering steps are not an abstract idea, they are extra-solution activity, as they collect the data needed to carry out the JE. The data gathering does not impose any meaningful limitation on the JE, or how the JE is performed. The additional limitation (data gathering) must have more than a nominal or insignificant relationship to the identified judicial exception. (MPEP 2106.04/.05, citing Intellectual Ventures LLC v. Symantee Corp, McRO, TLI communications, OIP Techs. Inc. v. Amason.com Inc., Electric Power Group LLC v. Alstrom S.A.).
Claims 1, 12, 29-30, and 39 recite the additional non-abstract element (EIA) of a general-purpose computer system or parts thereof:
a client device (claims 1, 12, 29-30, and 39);
a system comprising a computer processor and a memory (claim 29);
a non-transitory computer-readable medium comprising instructions (claim 30).
The EIA do not provide any details of how specific structures of the computer elements are used to implement the JE. The claims require nothing more than a general-purpose computer to perform the functions that constitute the judicial exceptions. The computer elements of the claims do not provide improvements to the functioning of the computer itself (as in DDR Holdings, LLC v. Hotels.com LP); they do not provide improvements to any other technology or technical field (as in Diamond v. Diehr); nor do they utilize a particular machine (as in Eibel Process Co. v. Minn. & Ont. Paper Co.). Hence, these are mere instructions to apply the JE using a computer, and therefore the claim does not recite integrate that JE into a practical application.
Thus, the additionally recited elements merely invoke a computer as a tool, and/or amount to insignificant extra-solution data gathering activity, and as such, when all limitations in claims 1-13, 29-30, and 35-39 have been considered as a whole, the claims are deemed to not recite any additional elements that would integrate a judicial exception into a practical application. Claims 1, 11-13, 29-30, and 39 contain additional elements that would not integrate a judicial exception into a practical application and are further probed for inventive concept in Step 2B.
[Step 2A, Prong Two: NO]
Eligibility Step 2B: Because the claims recite an abstract idea, and do not integrate that abstract idea into a practical application, the claims are probed for a specific inventive concept. The judicial exception alone cannot provide that inventive concept or practical application (MPEP 2106.05). Identifying whether the additional elements beyond the abstract idea amount to such an inventive concept requires considering the additional elements individually and in combination to determine if they amount to significantly more than the judicial exception (MPEP 2106.05A i-vi).
The claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception(s) because of the reasons noted below.
With respect to claims 1, 11-13, 29-30, and 39: The limitations identified above as non-abstract elements (EIA) related to data gathering do not rise to the level of significantly more than the judicial exception. Activities such as data gathering do not improve the functioning of a computer, or comprise an improvement to any other technical field. The limitations do not require or set forth a particular machine, they do not affect a transformation of matter, nor do they provide an unconventional step (citing McRO and Trading Technologies Int’l v. IBG). Data gathering steps constitute a general link to a technological environment. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp.,).
With respect to claims 1, 12, 29-30, and 39: The limitations identified above as non-abstract elements (EIA) related to general-purpose computer systems do not rise to the level of significantly more than the judicial exception. These elements do not improve the functioning of the computer itself, or comprise an improvement to any other technical field (Trading Technologies Int’l v. IBG, TLI Communications). They do not require or set forth a particular machine (Ultramercial v. Hulu, LLC., Alice Corp. Pty. Ltd v. CLS Bank Int’l), they do not affect a transformation of matter, nor do they provide an unconventional step. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception are insufficient to provide significantly more (as discussed in Alice Corp., CyberSource v. Retail Decisions, Parker v. Flook, Versata Development Group v. SAP America).
[Step 2B: NO]
Therefore, claims 1-13, 29-30, and 35-39 are patent ineligible under 35 U.S.C. § 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
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.
Claims 1-4, 7-13, 29-30, and 35-39 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Annals of Oncology, 2020, 31(6), 1-30), referred to as Liu [A], in view of Liu et al. (Journal of Clinical Oncology, ASCO Annual Meeting I, 2019, 37(15), 3049), referred to as Liu [B].
With respect to claims 1 and 29-30:
With respect to the recited receiving a first plurality of cancer signals of a first sample of a first individual, wherein each one of the first plurality of cancer signals indicates a probability that the first sample is associated with a different disease state of a plurality of disease states, Liu [A] discloses “Fragmentation methylation patterns for a given region were evaluated using multiple probability models evaluating the relative likelihood of fragments originating from one of several source populations (ie, individuals with cancer originating in a particular tissue) (“local source models”).” (pg. 16, para. 1, lines 4-7). Also, further discloses “In each classification region, a set of probabilistic models were trained, one for each training label (ie, one for each cancer type and one for non-cancer).” (pg. 17, para. 3, lines 1-2). This suggests that fragmentation methylation patterns for a given region are the cancer signals of a sample of an individual that are evaluated using probability models to indicate a likelihood that the region is associated with a cancer type.
With respect to the recited determining a first cancer signal having a greatest probability among the first plurality of cancer signals, Liu [A] discloses “Fragment methylation patterns for a given region were evaluated using multiple probability models evaluating the relative likelihood of fragments originating from one of several source populations (ie, individuals with cancer originating in a particular tissue) (“local source models”). The output of these models was thresholded to a single binary value per model per region: a sufficiently unusual fragment was detected from a particular tissue model, or no such fragment existed within a region (“extracting features”). The binary vector consisting of the output of these models for all regions of the targeted methylation panel was fed into two ensembles of logistic regressions: one binary logistic regression ensemble specialized in establishing the cancer/no-cancer-signal distinction, and one multinomial logistic regression ensemble specialized in predicting the source tissue for the cancer (“training the ensemble logistic regression”). The continuous scores from each model were thresholded to obtain a discrete cancer/no-cancer-signal call, as well as a discrete source tissue assignment or an indeterminate tissue assignment if the threshold for resolving a tissue was not passed (“assigning thresholds for decision calls”).” (pg. 16, para. 1-2, lines 4-17). Also, further discloses “A second threshold was used to distinguish confident and indeterminate TOO calls. The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference) assigned by the second-stage classifier.” (pg. 20, para. 2, lines 6-8). This suggests that cancer signal samples were determined to have a highest probability score among a plurality of cancer signals when calculating for a top-two TOO-score differential.
With respect to the recited responsive to determining that the first cancer signal satisfies a criterion, associating the first sample with a disease state corresponding to the first cancer signal, Liu [A] discloses “The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference assigned by the second-stage classifier. The cross-validated training set scores were used to identify the lowest threshold value such that TOO accuracy was at least 90% among samples whose top-two TOO-score differential exceeded the threshold. At prediction time, samples receiving a score from the first-stage classifier below the predefined specificity threshold were assigned a “non-cancer” label. For the remaining samples, those whose top-two TOO-score differential from the second-stage classifier was below the second predefined threshold were assigned the “indeterminate cancer” label. The remaining samples were assigned the cancer label to which the TOO classifier assigned the highest score.” (pg. 20-21, para. 2, lines 7-15). This suggests that the cancer signal samples whose top-two TOO-score differential was above the second predefined threshold were assigned a cancer label corresponding to the cancer signal.
With respect to the recited receiving a second plurality of cancer signals of a second sample of a second individual, wherein each one of the second plurality of cancer signals indicates a probability that the second sample is associated with a different disease state of the plurality of disease states, Liu [A] discloses “Fragmentation methylation patterns for a given region were evaluated using multiple probability models evaluating the relative likelihood of fragments originating from one of several source populations (ie, individuals with cancer originating in a particular tissue) (“local source models”).” (pg. 16, para. 1, lines 4-7). Also, further discloses “In each classification region, a set of probabilistic models were trained, one for each training label (ie, one for each cancer type and one for non-cancer).” (pg. 17, para. 3, lines 1-2). This suggests that fragmentation methylation patterns for a given region are the cancer signals of a sample of an individual that are evaluated using probability models to indicate a likelihood that the region is associated with a cancer type.
With respect to the recited determining a second cancer signal having a greatest probability among the second plurality of cancer signals, Liu [A] discloses “Fragment methylation patterns for a given region were evaluated using multiple probability models evaluating the relative likelihood of fragments originating from one of several source populations (ie, individuals with cancer originating in a particular tissue) (“local source models”). The output of these models was thresholded to a single binary value per model per region: a sufficiently unusual fragment was detected from a particular tissue model, or no such fragment existed within a region (“extracting features”). The binary vector consisting of the output of these models for all regions of the targeted methylation panel was fed into two ensembles of logistic regressions: one binary logistic regression ensemble specialized in establishing the cancer/no-cancer-signal distinction, and one multinomial logistic regression ensemble specialized in predicting the source tissue for the cancer (“training the ensemble logistic regression”). The continuous scores from each model were thresholded to obtain a discrete cancer/no-cancer-signal call, as well as a discrete source tissue assignment or an indeterminate tissue assignment if the threshold for resolving a tissue was not passed (“assigning thresholds for decision calls”).” (pg. 16, para. 1-2, lines 4-17). Also, further discloses “A second threshold was used to distinguish confident and indeterminate TOO calls. The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference) assigned by the second-stage classifier.” (pg. 20, para. 2, lines 6-8). This suggests that cancer signal samples were determined to have a highest probability score among a plurality of cancer signals when calculating for a top-two TOO-score differential.
With respect to the recited responsive to determining that the second cancer signal does not satisfy the criterion, associating the second sample with a subset of the plurality of disease states corresponding to a subset of the second plurality of cancer signals including at least the second cancer signal, Liu [A] discloses “The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference assigned by the second-stage classifier. The cross-validated training set scores were used to identify the lowest threshold value such that TOO accuracy was at least 90% among samples whose top-two TOO-score differential exceeded the threshold. At prediction time, samples receiving a score from the first-stage classifier below the predefined specificity threshold were assigned a “non-cancer” label. For the remaining samples, those whose top-two TOO-score differential from the second-stage classifier was below the second predefined threshold were assigned the “indeterminate cancer” label. The remaining samples were assigned the cancer label to which the TOO classifier assigned the highest score.” (pg. 20-21, para. 2, lines 7-15). This suggests that the cancer signal samples whose top-two TOO-score differential was below the second predefined threshold were assigned the indeterminate cancer label corresponding to the cancer signals.
Liu [A] does not disclose providing, for presentation on a client device to determine a first diagnosis of the first individual, the disease state corresponding to the first cancer signal associated with the first sample.
However, Liu [B] discloses “Agreement between the true (x-axis) and predicted (y-axis) TOO per sample using the TOO classifier with the methylation database in stage I-IV samples. Color corresponds to the proportion of predicted TOO (y-axis) which were correct (x-axis), as indicated to the right of the plot.” (pg. 3049, Figure 4. Tissue of Origin Performance). This suggests a plot depicting diagnoses of individuals, where the disease states correspond to tissue of origin representing cancer signals associated with the sample of an individual.
Liu [A] does not disclose providing, for presentation on the client device to determine a second diagnosis of the second individual, the subset of the plurality of disease states corresponding to the subset of the second plurality of cancer signals associated with the second sample.
However, Liu [B] discloses “Agreement between the true (x-axis) and predicted (y-axis) TOO per sample using the TOO classifier with the methylation database in stage I-IV samples. Color corresponds to the proportion of predicted TOO (y-axis) which were correct (x-axis), as indicated to the right of the plot.” (pg. 3049, Figure 4. Tissue of Origin Performance). This suggests a plot depicting diagnoses of individuals, where the disease states correspond to tissue of origin representing cancer signals associated with the sample of an individual.
Claim 29 recites a system comprising a computer processor and a memory, the memory storing computer program instructions. Claim 30 recites a non-transitory computer-readable medium comprising instructions.
Broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F .3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F. 2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
With respect to claims 2 and 35:
With respect to the recited determining a third cancer signal having a second greatest probability among the second plurality of cancer signals, wherein the subset of the second plurality of cancer signals further includes the third cancer signal, Liu [A] discloses “Fragment methylation patterns for a given region were evaluated using multiple probability models evaluating the relative likelihood of fragments originating from one of several source populations (ie, individuals with cancer originating in a particular tissue) (“local source models”). The output of these models was thresholded to a single binary value per model per region: a sufficiently unusual fragment was detected from a particular tissue model, or no such fragment existed within a region (“extracting features”). The binary vector consisting of the output of these models for all regions of the targeted methylation panel was fed into two ensembles of logistic regressions: one binary logistic regression ensemble specialized in establishing the cancer/no-cancer-signal distinction, and one multinomial logistic regression ensemble specialized in predicting the source tissue for the cancer (“training the ensemble logistic regression”). The continuous scores from each model were thresholded to obtain a discrete cancer/no-cancer-signal call, as well as a discrete source tissue assignment or an indeterminate tissue assignment if the threshold for resolving a tissue was not passed (“assigning thresholds for decision calls”).” (pg. 16, para. 1-2, lines 4-17). Also, further discloses “A second threshold was used to distinguish confident and indeterminate TOO calls. The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference) assigned by the second-stage classifier.” (pg. 20, para. 2, lines 6-8). This suggests that cancer signal samples were determined to have a second highest probability score among a plurality of cancer signals when calculating for a top-two TOO-score differential.
With respect to claims 3 and 36:
With respect to the recited wherein the criterion is a probability threshold, and wherein determining that the first cancer signal satisfies the criterion comprises: determining that the greatest probability of the first cancer signal is greater than the probability threshold, Liu [A] discloses “The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference assigned by the second-stage classifier. The cross-validated training set scores were used to identify the lowest threshold value such that TOO accuracy was at least 90% among samples whose top-two TOO-score differential exceeded the threshold. At prediction time, samples receiving a score from the first-stage classifier below the predefined specificity threshold were assigned a “non-cancer” label. For the remaining samples, those whose top-two TOO-score differential from the second-stage classifier was below the second predefined threshold were assigned the “indeterminate cancer” label. The remaining samples were assigned the cancer label to which the TOO classifier assigned the highest score.” (pg. 20-21, para. 2, lines 7-15). This suggests that a cancer signal satisfies the criterion threshold if its top-two TOO-score differential was a highest probability score above the probability threshold.
With respect to claim 4:
With respect to the recited wherein the probability threshold is at least 90%, Liu [A] discloses “The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference assigned by the second-stage classifier. The cross-validated training set scores were used to identify the lowest threshold value such that TOO accuracy was at least 90% among samples whose top-two TOO-score differential exceeded the threshold.” (pg. 20, para. 2, lines 7-11). This suggests that the probability threshold value is identified based on the tissue of origin accuracy being at least 90% for samples with differentials that exceed the threshold.
With respect to claims 7 and 37:
With respect to the recited determining a subset of n cancer signals of the first plurality of cancer signals having the n greatest probabilities among the first plurality of cancer signals, Liu [A] discloses “The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference assigned by the second-stage classifier. The cross-validated training set scores were used to identify the lowest threshold value such that TOO accuracy was at least 90% among samples whose top-two TOO-score differential exceeded the threshold. At prediction time, samples receiving a score from the first-stage classifier below the predefined specificity threshold were assigned a “non-cancer” label. For the remaining samples, those whose top-two TOO-score differential from the second-stage classifier was below the second predefined threshold were assigned the “indeterminate cancer” label. The remaining samples were assigned the cancer label to which the TOO classifier assigned the highest score.” (pg. 20-21, para. 2, lines 7-15). This suggests that cancer signals were assigned highest probability scores by the TOO classifier among a plurality of cancer signals.
With respect to the recited responsive to determining that at least a threshold number of the subset of the first plurality of cancer signals is associated with a category of disease states, associating the first sample with each disease state of the category of disease states, Liu [A] discloses “The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference assigned by the second-stage classifier. The cross-validated training set scores were used to identify the lowest threshold value such that TOO accuracy was at least 90% among samples whose top-two TOO-score differential exceeded the threshold. At prediction time, samples receiving a score from the first-stage classifier below the predefined specificity threshold were assigned a “non-cancer” label. For the remaining samples, those whose top-two TOO-score differential from the second-stage classifier was below the second predefined threshold were assigned the “indeterminate cancer” label. The remaining samples were assigned the cancer label to which the TOO classifier assigned the highest score.” (pg. 20-21, para. 2, lines 7-15). This suggests that a threshold number of cancer signal samples are associated with cancer labels or disease states because the samples were assigned a highest probability score above the second predefined threshold.
With respect to claim 8:
With respect to the recited wherein the category of disease states is human papillomavirus (HPV) cancer, Liu [A] discloses “More than >50 cancer types, as defined using the American Joint Committee on Cancer 8th edition [1], were represented in the CCGA study as defined at enrollment and supported by pathology reports; these included … HPV-mediated (p16+) oropharyngeal cancer” (pg. 4-5, para. 3, lines 1-8). This describes categories of disease states including HPV-mediated (p16+) oropharyngeal cancer, which is a cancer of the throat caused by the human papillomavirus type 16 virus.
With respect to claim 9:
With respect to the recited wherein the category of disease states includes stomach cancer and intestinal cancer, Liu [A] discloses “Cancers were grouped for reporting purposes, per the statistical analysis plan, as follows: anus, bladder, brain, breast, cervix, colon/rectum, esophagus, gallbladder, head and neck, kidney, liver/bile duct, lung, lymphoid leukemia, lymphoma, melanoma, myeloid neoplasm, orbit, ovary, pancreas, penis, plasma cell neoplasm, prostate, sarcoma, skin cancer (not squamous cell carcinoma, basal cell carcinoma, or melanoma), small intestine, stomach, testis seminoma, thymus, thyroid, uterus, vagina, and vulva.” (pg. 5, para. 2, lines 1-6). This describes categories of disease states including stomach cancer and small intestine cancer, which is a type of intestinal cancer.
With respect to claims 10 and 38:
With respect to the recited wherein the plurality of disease states includes a non-cancer state, Liu [A] discloses “Through ongoing review of the enrollment metrics in the CCGA study, the sponsor ensured that the intended population stratification for cancer and non-cancer participants within each center was met.” (pg. 3, para. 2, lines 1-3). This suggests that the disease states in the study includes a non-cancer state.
With respect to claim 11:
With respect to the recited wherein the plurality of disease states includes one or more types of cancer selected from the group including anus cancer, breast cancer, uterine cancer, cervical cancer, ovarian cancer, bladder cancer, urothelial cancer of renal pelvis and ureter, renal cancer other than urothelial, prostate cancer, anorectal cancer, colorectal cancer, squamous cell cancer of esophagus, esophageal cancer other than squamous, gastric cancer, hepatobiliary cancer arising from hepatocytes, hepatobiliary cancer arising from cells other than hepatocytes, pancreatic cancer, human-papillomavirus-associated head and neck cancer, head and neck cancer not associated with human papillomavirus, lung adenocarcinoma, small cell lung cancer, squamous cell lung cancer and lung cancer other than adenocarcinoma or small cell lung cancer, neuroendocrine cancer, melanoma, thyroid cancer, sarcoma, multiple myeloma, lymphoma, leukemia, kidney cancer, liver cancer, bile duct cancer, plasma cell neoplasm cancer, upper gastrointestinal tract cancer, vulvar cancer, and lung neuroendocrine tumors and other high-grade neuroendocrine tumors, Liu [A] discloses “Cancers were grouped for reporting purposes, per the statistical analysis plan, as follows: anus, bladder, brain, breast, cervix, colon/rectum, esophagus, gallbladder, head and neck, kidney, liver/bile duct, lung, lymphoid leukemia, lymphoma, melanoma, myeloid neoplasm, orbit, ovary, pancreas, penis, plasma cell neoplasm, prostate, sarcoma, skin cancer (not squamous cell carcinoma, basal cell carcinoma, or melanoma), small intestine, stomach, testis seminoma, thymus, thyroid, uterus, vagina, and vulva.” (pg. 5, para. 2, lines 1-6). This describes a plurality of disease states that includes one or more types of cancer as listed above.
With respect to claims 12 and 39:
Liu [A] does not disclose providing, for presentation on the client device, a graphical comparison of each disease state corresponding to the subset of the plurality of disease states associated with the second sample.
However, Liu [B] discloses “Here, we report multi-cancer detection and TOO determination from initial analyses of an optimized targeted methylation assay in 2,301 participants from the second CCGA substudy.” (pg. 3049, Section “Background”, fourth bullet). Also, further discloses “Initial results from the ongoing second sub-study of CCGA showed targeted methylation simultaneously detected multiple cancer types, at early stages, at a specificity (99%) appropriate for population screening.” (pg. 3049, Section “Conclusions”, first bullet). This describes Figure 3, which depicts a comparison of multiple cancer types corresponding to cancer types associated with the second sub-study.
With respect to claim 13:
Liu [A] does not disclose wherein the graphical comparison is a bar plot based on the probabilities of the second plurality of cancer signals.
However, Liu [B] discloses “Observed fragments were assigned a relative probability of originating from cancer. Similarly, for TOO, observed fragments were assigned a relative probability of originating from a particular tissue. Fragments characteristic of cancer and TOO were combined across targeted regions to classify cancer versus non-cancer and identify TOO. For binary cancer classification, clinical sensitivity was estimated at 99% specificity.” (pg. 3049, Section “Classification”, bullets 2-5). This describes Figure 3, which depicts a bar plot based on the probabilities of fragments representing cancer signals.
It would have been prima facie obvious to one of ordinary skill in the art to modify the teachings disclosed by Liu [A] to incorporate a bar plot disclosed by Liu [B]. One would be motivated to make this modification because the multi-cancer detection approach disclosed by Liu [B] accurately localizes the tissue of origin (TOO), which could streamline subsequent diagnostic work-up (pg. 3049, Section “Conclusions”, col. 1, third bullet). There is a likelihood of success, since both teachings are of methods for cancer diagnosis and are well known in the field of oncology.
Claim 5 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Annals of Oncology, 2020, 31(6), 1-30), referred to as Liu [A], and Liu et al. (Journal of Clinical Oncology, ASCO Annual Meeting I, 2019, 37(15), 3049), referred to as Liu [B], as applied to claims 1-4, 7-13, 29-30, and 35-39 above, in view of Trevethan (Frontiers in Public Health, 2017, 5(307), 1-7).
Liu [A] and Liu [B] are applied to claims 1-4, 7-13, 29-30, and 35-39 above.
With respect to claim 5:
Liu [B] does not disclose determining the criterion based on accuracy of cancer signal probabilities and false positives.
However, Liu [A] discloses “During classifier training, scores assigned to the validation folds within the training set were retained for use in assigning cutoff values to target certain performance metrics. In particular, the scores assigned to the training set non-cancer samples were used to define thresholds corresponding to particular specificity levels. For example, for a specificity target of 99.4%, the threshold was set at the 99.4th percentile of the training set non-cancer score distribution. A second threshold was used to distinguish confident and indeterminate TOO calls. The metric used to assess the confidence of the TOO call was the difference between the highest and second-highest score (top-two TOO score difference) assigned by the second-stage classifier. The cross-validated training set scores were used to identify the lowest threshold value such that TOO accuracy was at least 90% among samples whose top-two TOO-score differential exceeded the threshold.” (pg. 20, para. 2, lines 1-11). This suggests that a criterion was determined based on accuracy of TOO cancer signal probabilities. A threshold criterion was also determined based on particular specificity levels.
Trevethan defines specificity as “a screening test’s probability of correctly identifying, solely from among people who are known not to have a condition, all those who do indeed not have that condition (i.e., identifying true negatives), and, at the same time, not categorizing some people as having the condition when in fact they do not have it (i.e., avoiding false positives).” (pg. 4, col. 1, para. 2, lines 2-7). Therefore, the criterion determined based on specificity is also in turn determined based on avoiding false positives.
It would have been prima facie obvious to one of ordinary skill in the art to modify the teachings disclosed by Liu [A] and Liu [B] to incorporate the definition of specificity disclosed by Trevethan. One would be motivated to make this modification because high specificity permits people to be confidently regarded as having a condition if their diagnostic test yields a positive result (pg. 5, col. 1, para. 2, lines 4-6). There is a likelihood of success, since the multi-cancer detection methods of Liu [A] and Liu [B] and clinical screening test metrics from Trevethan are relevant and well known in the field of oncology.
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Liu et al. (Annals of Oncology, 2020, 31(6), 1-30), referred to as Liu [A], and Liu et al. (Journal of Clinical Oncology, ASCO Annual Meeting I, 2019, 37(15), 3049), referred to as Liu [B], as applied to claims 1-4, 7-13, 29-30, and 35-39 above, in view of Wei et al. (JAMA Oncology, 2018, 4(4), 1-6).
Liu [A] and Liu [B] are applied to claims 1-4, 7-13, 29-30, and 35-39 above.
With respect to claim 6:
Liu [A] and Liu [B] do not disclose determining the criterion based on residual risk of current cancer being associated with a sample.
However, Wei et al. discloses “There are several validated multivariable residual risk predictors such as Adjuvant! Online or PREDICT that consider both the baseline anatomical risk factors and the expected benefit from adjuvant therapy to estimate the risk of recurrence, or death, that remains after completing all therapy. We call this residual risk because it is the risk of recurrence that remains despite receiving standard-of-care therapy.” (pg. 2, col. 1, para. 2, lines 1-7). Also, further discloses “using a minimal residual risk threshold as the eligibility criterion, calculated by a validated multivariate model, could lead to more predictable event rates and therefore more reliable power estimates in adjuvant trials. This would also enable clinical trialists to more predictably identify high-risk patients who are not likely to be cured by current therapies and therefore require further improvements in therapy.” (pg. 3, col. 1, para. 2, lines 1-7). This suggests that a criterion was determined based on residual risk, or potential recurrence, of current cancer associated with a patient.
It would have been prima facie obvious to one of ordinary skill in the art to modify the teachings disclosed by Liu [A] and Liu [B] to incorporate the criterion based on residual risk of current cancer disclosed by Wei et al. One would be motivated to make this modification because using a minimal residual risk threshold as the eligibility criterion, calculated by a validated multivariate model, could lead to more predictable event rates and therefore more reliable power estimates in adjuvant trials (pg. 3, col. 1, para. 2, lines 1-4). There is a likelihood of success, since the multi-cancer detection methods of Liu [A] and Liu [B] and the clinical trial design for breast cancer treatment from Wei et al. are well known in the field of oncology.
Conclusion
No claims are allowed.
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/J.N.L./Examiner, Art Unit 1686
/LARRY D RIGGS II/Supervisory Patent Examiner, Art Unit 1686