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 .
DETAILED ACTION
Claims 1-13 were pending. On 6/4/26, applicant filed an amendment, claims 1-5,12-13 are amended and claims 14-16 are new. Thus claims 1-16 are pending. After careful consideration of applicant arguments and amendments, the examiner finds them to be moot and/or non-persuasive. This action is a Final Rejection.
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 1-16 are rejected under 35 U.S.C. 101 because they are directed to an abstract idea without more.
Claim 1-11, and 14-16 are method claims, claim 12 is a system and claim 13 is a non-transitory computer readable medium. Thus the claims are directed to statutory classes of invention. Step 1 Yes
Independent claim 1 is being analyzed as representative. The limitations under their broadest reasonable interpretation cover “mental processes” that can be conducted in the human mind.
The abstract limitations
obtaining a non-linear classifier configured to estimate a risk class for values of a plurality of clinical data; - obtaining, for a patient (P),respective the values of a of the plurality of clinical data (p); - generating a local linear classifier model based on the non-linear classifier, the local linear classifier model to locally approximate the non-linear classifier in a neighborhood of the values of said plurality of clinical data of said patient, wherein the training comprises: a)- generating a training dataset via the steps of:-i)_estimating by means of a by using the non-linear classifier a respective risk class (v) for the values of said plurality of clinical data (p) of said patient (P);-ii)adding the values of said plurality of clinical data (p) of said patient (P) and the respective estimated risk class (v) to said training dataset;- iii) generating a plurality of modified datasets by modifying the values of said plurality of clinical data (p) of said patient (P);- iv)_estimating, by means using of said non-linear classifiers a respective risk class (v) for the values of each modified dataset;-v)adding the values of each modified dataset and the respective estimated risk class (v) to said training dataset; b) - training the local linear classifier model by using said training dataset; - determining a risk class (v) among said risk classes (v) estimated for the plurality of modified datasets said modified datasets (304) indicating a lower risk than said risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P), - training a linear classifier configured to estimate said risk class (v) as a function of the values of said plurality of clinical data (p) by using said training dataset; - determining a separation plane of said local linear classifier model linear classifier separating the risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) from said risk class (v) indicating a lower risk;- using said separation plane of said local linear classifier model to calculate the minimum change (MP) required to the values of said plurality of clinical data (p) of said patient (P) to change the risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) in said risk class (v) indicating a lower risk by calculating the minimum distance between the values of said plurality of clinical data of said patient and said separation plane; and- providing a support for clinical decisions by showing said change (MP) to the values of said plurality of clinical data (p) of said patient (P) on a display.
Here the claims are directed to a mental process. Aside from the initial rendition of computer and “display” at the end of claim 1, there are no tangible elements and the process does not seem to improve the state of AI or training data.
In fact the claims (1) are directed to “providing support for clinical decisions by showing said change to the values of said plurality of clinical data of said patient on a display”
Ie it’s more of data intended for support.
The recitation of generic computing elements does not preclude a claim from reciting an abstract idea. Step 2A prong 1 yes, the claims recite an abstract idea)
The judicial exception is not integrated into a practical application. As disclosed a computer and a display are not indicative of more than a generic computer, thus to amounting to instructions to apply the generic computer component. Claims 12 and 13 are similar. Thus the claims 1-13 are directed to an abstract idea without a practical application.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because when considered separately and as an ordered combination they do not add significantly more known as inventive concept to the exception. As discussed above with resect to integration to the abstract idea into a practical application the additional element of a computer and display amounts to no more than mere instructions to apply a generic computer. The specification does not provide any specific technical improvement that could be applied on first review. However, applicant might be able to show an improvement in the field of machine learning.
Step 2B No, the claims do not provided significantly more. The dependent claims 2-11, 14-16 do not improve the analysis for claim 1.
Here, in regards to the claims, the applicant might be able to argue that the mental process cannot be conducted in the mind and accompanying amendments re: computer and/or machine learning.
Claim Objections
Claims 1, 3, 12 and 13 are objected to because of the following informalities: see below. Appropriate correction is required.
1, 3, 12, 13 contain
“A Lower risk”? First, “lower” is a relative term so relative is in the eyes of the beholder. Further more, “a lower risk is followed by a lower risk and then a lower risk. If these are the same risks then a lower, then “the lower would be correct” antecedent basis.
Claim 1 provides support for clinical decisions…. “is support more of an intended use? Claims 12, and 13 are similar to claim 1.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-16 are directed under 35 USC 112 (b) because they lack proper antecedent basis
Claims 1, 12 and 13, contain A risk class, then “ a respective risk class” then “the estimated risk class”, referring to “estimate a risk class”, However, a respective risk class and “the risk class are utilized multiple times without correct antecedent basis.
The minimum change MP and then said Change MP …. No antecedent basis.
“by showing said change MP….. (there is no antecedent basis”)
Here
For the purpose of examination, the examiner will presume that risk classes are all referring to the same risk class.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-16 is/are rejected under 35 U.S.C. 103 as being unpatentable over
US Patent Publication 2022/0208305 to Bontrager in view of Finkle 20210398617
As per claim 1, Bontrager discloses;
obtaining a non-linear classifier configured to estimate a risk class for values of a plurality of clinical data;
Bontrager(0100, neural network may be a non-linear classification 0084)
- obtaining, for a patient (P), the values of the plurality of clinical data
Bontrager(0084)
a)- generating a training dataset via the steps of:-i)_estimating by using a by using the non-linear classifier a respective risk class (v) for the values of said plurality of clinical data (p) of said patient (P);-
Bontrager(0105, classifiers of different types)
ii)adding the values of said plurality of clinical data (p) of said patient (P) and the respective estimated risk class (v) to said training dataset;-
Bontrager(0108, 0140)
iii) generating a plurality of modified datasets by modifying the values of said plurality of clinical data (p) of said patient (P);- iv)_estimating, by using of said non-linear classifiers a respective risk class (v) for the values of each modified dataset;
Bontrager(0156, classify as reportable or non reportable or conflicting data)
iv)adding the values of each modified dataset and the respective estimated risk class (v) to said training dataset; b) - training the local linear classifier model by using said training dataset; - determining a risk
class (v) among said risk classes (v) estimated for the plurality of modified datasets said modified datasets (304) indicating a lower risk than said risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P),
Bontrager(0108)
training a linear classifier configured to estimate said risk class (v) as a function of the values of said plurality of clinical data (p) by using said training dataset;
Bontrager(0105, linear classification…. )
determining a … of said local linear classifier model linear classifier separating the risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) from said risk class
Bontrager(separation, graph based approaches)
(v) indicating a lower risk;- using said … of said local linear classifier model to calculate the minimum change (MP)
required to the values of said plurality of clinical data (p) of said patient (P) to change the risk class (v)
Bontrager(0105)
estimated for the values of said plurality of clinical data (p) of said patient (P) in said risk class (v) indicating a lower risk
Bontrager(0213)
by calculating the minimum distance between the values of said plurality of
Bontrager(0122, minimum text distance…. For example)
clinical data of said patient and said …;
Bontrager(0105, similar to previous)
and- providing a support for clinical decisions by showing said change (MP) to the values of said plurality of clinical data (p) of said patient (P) on a display. Bontrager(0149,… clinical decision support)
Bontrager does not explicitly disclose what Finkle teaches;
“separation plane” which by an internet search appears to be known but may also be called a decision boundary or hyperplane (0235 of Finkle)
(p); - generating a local linear classifier model based on the non-linear classifier, the local linear classifier model to locally approximate the non-linear classifier in a neighborhood of the values of said plurality of clinical data of said patient, wherein the training comprises:
Finkle( Local algorithm 0320)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the AI driven therapy analysis of Bontrager with molecular analysis of Finkle for the motivation of “detection and cancer treatment” (0002)
Claims 12, 13 are similar to claim 1.
As per claim 2, Bontrager discloses;
The method according to Claim 1, comprising the following steps during a training phase: - obtaining an additional training dataset comprising for each of a plurality of reference patients (PR) respective values for said plurality of clinical data (p) and a respective value identifying a risk class (v); - training said non-linear classifier using said additional training dataset, wherein said non-linear classifier is configured to estimate said risk class as a function of the values of said plurality of clinical data (p).
Bontrager(0104, 0107)
Bontrager does not explicitly disclose what Finkle teaches;
using said separation plane to calculate the minimum change (MP) required to the values of said plurality of clinical data (p) of said patient (P) to change
“separation plane” which by an internet search appears to be known but may also be called a decision boundary or hyperplane (0235 of Finkle)
It would therefore have been obvious to one of ordinary skill in the art before the effective filing date of the invention to combine the AI driven therapy analysis of Bontrager with molecular analysis of Finkle for the motivation of “detection and cancer treatment” (0002)
As per claim 3, Bontrager discloses; The method according to Claim 1 or Claim 2, wherein said risk class indicating a lower risk than said risk class (v) estimated for the values of said plurality of clinical data (p) of said patient (P) corresponds to the risk class (v) among said risk classes (v) estimated for said modified datasets indicating a minimum risk.
Bontrager(0213)
As per claim 4 Bontrager discloses; The method according to Claim 3, wherein said local linear classifier model includes a multi-class classifier, and said separation plane of said local linear classifier model separates said risk class indicating a minimum risk from all other risk classes estimated for said modified datasets.
Bontrager(0105)
As per claim 5, Bontrager discloses;
The method according to Claim 3, comprising: - replacing the risk class estimated for the values of said plurality of clinical data (p) of said patient (P) with a first value;
- associating the risk class indicating a minimum risk with a second value; - replacing each risk class estimated for a respective modified dataset of the plurality of modified datasets with said first value if the respective risk class indicates a risk being greater than said risk class indicating a minimum risk and said second value if the respective risk class corresponds to said risk class indicating a minimum risk, - training said local linear classifier, whereby said local linear classifier model includes a binary classifier configured to classify the values of said plurality of clinical data (p) either with said first value or said second value, wherein said separation plane of said local linear classifier model separates said risk class indicating a minimum risk from all other risk classes estimated for said the plurality of modified datasets .Bontrager (0105)
As per claim 6, Bontrager discloses;
The method according to any of the previous claimsClaim1, wherein said using said separation plane to calculate the minimum modification (MP) required comprises: - determining the clinical data (p) to be modified by calculating the normal of said separation plane; and - determining the extent of the modification to said clinical data (p) to be modified by calculating the minimum distance between the values of said plurality of clinical data (p) of said patient (P) and said separation plane.
Bontrager (0109)
As per claim 7 Bontrager discloses; The method according Claim 1, comprising receiving one or more constraints (CS) for modifications to the values of said plurality of clinical data (p) of said patient (P), and at least one of: - generating said plurality of modified datasets by modifying the values of said plurality of clinical data (p) of said patient (P) as a function of said one or more constraints (CS); and/or - verifying whether the minimum modification (MP) required to the values of said plurality of clinical data (p) of said patient (P) satisfies said one or more constraints (CS).
Bontrager (0085 constraint in the feature set)
As per claim 8, Bontrager discloses;
The method according to Claim 7, wherein said one or more constraints (CS) comprise at least one of: - a blacklist containing clinical data which cannot be modified; and/or - a list containing the definitions of the range of acceptable values for one or more of said clinical data (p); and/or - data specifying one or more correlation matrices, wherein each correlation matrix specifies the correlation between a plurality of clinical data (p).
Bontrager(0209, blacklist)
As per claim 9 Bontrager discloses; The method according to Claim 7, wherein said one or more constraints (CS) comprise a list of actions,
such as taking a given drug and/or a given medical intervention and/or a given physical activity,
wherein each action specifies a change to one or more of said clinical data (p), and wherein said method comprises: - determining whether one of said actions is compatible with said minimum modification (MP) required by verifying whether the direction of the changes to said one or more clinical data (p) corresponds substantially to the direction of said minimum modification (MP) required; and - in case an action is compatible with said minimum modification (MP) required, showing said action on said display.
Bontrager(0107, such as is a generic list…. More of a suggestion, in 0109 various suggestions for intervention are discussed)
As per claim 10, Bontrager discloses;
The method according to Claim 2, wherein said value identifying a risk class corresponds to: - information on whether the patient will develop a given disease; - data that indicate the seriousness of the disease that the patient has developed; - data that identify a disease-free survival time; - data that identify whether a stay in hospital envisaged; or - data that identify a risk of death, onset of complications or the likelihood of adverse events in the case of surgical operation.
Bontrager(0067)
As per claim 11 Bontrager discloses; The method according to any of the previous 1 wherein said clinical data (p) comprise one or more of: omics data, such as transcriptomics, genomics, epigenomics, metabolomics, methylomics, and/or proteomics data, data obtained via laboratory analyses, such as blood tests, radiological data, and general clinical data, such as age, sex, history of smoking.
Bontrager(fig. 1 Omics)
Claims 12 and 13 are similar to claim 1.
As per claim 14, Bontrager does not explicitly disclose what Finkle teaches;
The method of claim 1, wherein said generating a plurality of modified datasets by modifying the values of said plurality of clinical data of said patient comprises: determining for each of said plurality of clinical data a respective probability distribution; and generating a modified dataset by extracting randomly for each of said plurality of clinical data a respective value according to the respective probability distribution. Finkle(distribution of values, 0094)
The motivation would be similar to that provided for claim 1.
As per claim 15, Bontrager does not explicitly disclose what Finkle teaches;
The method of claim 14, wherein the probability distribution of each of said plurality of clinical data corresponds to a respective normal distribution with a respective standard deviation centered on the respective value of the respective clinical data for said patient.
Finkle(0345, standard deviation)
The motivation would be similar to that provided for claim 1.
As per claim 16, Bontrager does not explicitly disclose what Finkle teaches;
The method of claim 15, wherein the standard deviation of a clinical data corresponds to the standard deviation of the values of said additional training dataset for the respective clinical data.
Finkle(0345, standard deviation)
The motivation would be similar to that provided for claim 1.
Response to Arguments
Claims 1-13 were pending. On 6/4/26, applicant filed an amendment, claims 1-5,12-13 are amended and claims 14-16 are new. Thus claims 1-16 are pending. After careful consideration of applicant arguments and amendments, the examiner finds them to be moot and/or non-persuasive. This action is a Final Rejection.
Claim Rejections - 35 U.S.C. 112- withdrawn
Claim Rejections - 35 U.S.C. 101
The § 101 rejection is respectfully traversed for at least the following reasons.
The now familiar structure of the Alice two-part test requires, at Step One, determining "whether the claims are directed to patent ineligible subject matter." Contour IP Holding LLC v. GoPro, Inc., 113 F.4th 1373, 1379 (Fed. Cir. 2024) (emphasis added). If the claims are determined to be directed to such ineligible subject matter, then, at Step Two, the claims are assessed to determine if they "recite something significantly more than an abstract idea itself."
The USPTO splits the analysis of Alice Step One into two separate "prongs." Under Prong One, the claims are analyzed for whether a claim recites judicial exceptions, including certain groupings of abstract ideas (i.e., mathematical concepts, certain methods of organizing human activity such as a fundamental economic practice, or mental processes).
Under the Prong Two, the "additional elements" in the claims are analyzed to determine if they integrate the judicial exception (Prong One) into a practical application. If a claim does not recite a judicial exception or it integrates any recited judicial exception into a practical application, then it is not "directed to" patent ineligible subject matter under Alice Step One.
The independent claims are directed to techniques for generating (training) a local linear classifier model. This subject matter does not "recite" any judicially excepted subject matter under the USPTO's Prong One Analysis.
The examiner interpreted the claims to be a mental process. While applicant’s use of “training” implies machine learning, and may be directed to machine learning, the actual machine learning is not currently claimed.
MPEP 2106.04(a)(1) is instructive for how the USPTO's Prong One analysis is relevant to a claim that is directed to training in the context of machine learning because it provides a specific example of a claim that does "not recite (set forth or describe) an abstract idea" under Prong One. The claim 1
a method of training a neural network for facial detection comprising:
collecting a set of digital facial images, applying one or more transformations to the digital images,
creating a first training set including the modified set of digital facial images;
training the neural network in a first stage using the first training set,
creating a second training set including digital non-facial images that are incorrectly detected as facial images in the first stage of training; and
training the neural network in a second stage using the second training set.
MPEP 2106.04(a)(1) (emphasis added).
This hypothetical claim from the MPEP is similar to the claims at issue for purposes of subject matter eligibility in that they both relate to machine learning and training models.
The hypothetical claim from the MPEP was not "directed to" judicially excepted subject matter under Step One of Alice. So too are the claims in this case not "directed to" patent ineligible subject matter. Withdrawal is respectfully requested.
Turning to the Prong Two Analysis, the "additional elements" in the claims are analyzed to determine if they integrate the judicial exception (Prong One) into a practical application. If a claim integrates any recited judicial exception into a practical application, then the claim is not "directed to" patent ineligible subject matter under Alice Step One. Here, even assuming that the claims do recite some judicially excepted subject matter, they still integrate that subject matter into a practical application at least because of the generating and training elements.
In this case, the independent claims set forth generating a "local linear classifier model" from the non-linear classifier. These are not a mental processes or other such judicially excepted subject matter. Specifically, both the generating and the training elements in claim 1 do not "recite" judicially excepted subject matter for reasons similar with respect the training element set forth in the above MPEP example. Accordingly, at least these elements integrate any otherwise judicially excepted subject matter into a practical application and accordingly the claims are not "directed to" patent eligible subject matter under step one of the Alice test.
Withdrawal is respectfully requested.
Here applicant arguers the claims are directed to machine learning but the machine learning is not claimed. However a claim that is directed to an improvement in machine learning would be acceptable.
Claim Rejections - 35 U.S.C. 103
The art-based rejections are respectfully traversed for at least the following reasons.
Independent claim 1 recites, in combination,
generating a local linear classifier model based on the non-linear classifier,
the local linear classifier model to locally approximate the non-linear classifier in a neighborhood of the values of said plurality of clinical data of said patient, wherein
the training comprises...
b) training the local linear classifier model by using said training dataset...
determining a separation plane of said local linear classifier model
separating the risk class estimated for the values of said plurality of clinical data (p)
of said patient (P) from said risk class indicating a lower risk;
Generally similar features are set forth in the other independent claims, and these features are not taught or suggested by the relied upon references, or their alleged combination.
In particular, the relied upon art, or the alleged combination thereof fails to teach or
suggest the generation of a construction of a local linear classifier model based around data of a the single patient, obtained from a non-linear classifier, in order to derive modifications of the patient's clinical data.
The Office Action cites to Bontrager and Tell. However, neither of these references teach of suggest there features.
Bontrager appears to be concerned with a system for therapy curation and prioritization. For example, it collects clinical and molecular patient data, extracts features, compares them with therapeutic evidence, publications and clinical trials, and selects or ranks the therapies considered most relevant for that patient. Of the paragraphs cited by the Office Action, paragraph 74 mentions classifiers and machine-learning techniques generally. Paragraph 84 concerns the breadth of possible omics and other data types. Paragraph 105 provides a laundry list of possible machine-learning algorithms such as linear regression, logistic regression, decision trees, Naive Bayes, clustering, PCA, random forest and adaptive boosting. Paragraph 108 concerns source article inclusion and exclusion, i.e. management of therapeutic evidence. Paragraph 213 discusses identifying and organizing therapeutic recommendations or clinically relevant information for the patient.
None of these cited paragraphs (or Bontrager more generally) teaches of suggests a
procedure in which one starts from a non-linear risk classifier, and then "generates a local linear
classifier model" based on that non-linear classifier in the manner specifically called for in claim
1.
The Office Action's reliance on Tell does not cure this deficiency. Tell discusses
dynamic variant thresholding in a liquid biopsy assay including validating or rejecting somatic variants based on dynamic thresholds, variant count filters, coverage filters, allele fraction filters, etc. Tell seems to mention classification modules. However, there is no teaching or suggestion of generating a local linear classifier model" based on that non-linear classifier in the manner specifically called for in claim 1. For example, while Tell appears to address threshold-based validation, there is no teaching or suggestion of deriving (for example) a minimum patient modification from a generated local linear classifier model.
Here, the examiner finds applicants arguments to generally be moot in view of new grounds of rejection in order to address a substantial amendment to the claims.
This action is a final rejection.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
A Multi-Learning Training Approach for Distinguishing Low and High Risk Cancer Patients- IEEE 2021
Machine Learning Model for Breast Cancer Prediction, IEEE, 2020
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BRUCE I EBERSMAN whose telephone number is (571)270-3442. The examiner can normally be reached 8:00 am - 5:00 pm Monday-Friday.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael W Anderson can be reached at 571-270-0508. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/BRUCE I EBERSMAN/Primary Examiner, Art Unit 3693
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